Using graphs to express and automatically - Events

Using graphs to express and automatically - Events
PGA: Using Graphs to Express and Automatically
Reconcile Network Policies
Chaithan Prakash∧ ∗ Jeongkeun Lee† Yoshio Turner◦ ∗ Joon-Myung Kang† Aditya Akella∧
Sujata Banerjee† Charles Clark‡ Yadi Ma† Puneet Sharma† Ying Zhang†
∧
University of Wisconsin-Madison, † HP Labs, ◦ Banyan, ‡ HP Networking
ABSTRACT
1.
Software Defined Networking (SDN) and cloud automation
enable a large number of diverse parties (network operators,
application admins, tenants/end-users) and control programs
(SDN Apps, network services) to generate network policies
independently and dynamically. Yet existing policy abstractions and frameworks do not support natural expression and
automatic composition of high-level policies from diverse
sources. We tackle the open problem of automatic, correct and fast composition of multiple independently specified network policies. We first develop a high-level Policy Graph Abstraction (PGA) that allows network policies
to be expressed simply and independently, and leverage the
graph structure to detect and resolve policy conflicts efficiently. Besides supporting ACL policies, PGA also models
and composes service chaining policies, i.e., the sequence
of middleboxes to be traversed, by merging multiple service chain requirements into conflict-free composed chains.
Our system validation using a large enterprise network policy dataset demonstrates practical composition times even
for very large inputs, with only sub-millisecond runtime latencies.
Computer networks, be they ISPs, enterprise, datacenter,
campus or home networks, are governed by high-level policies derived from network-wide requirements. These network policies primarily relate to connectivity, security and
performance, and dictate who can have access to what network resources. Further, policies can be static or dynamic
(e.g., triggered). Traditionally, network admins translate high
level network policies into low level network configuration
commands and implement them on network devices, such as
switches, routers and specialized network middleboxes (e.g.,
firewalls, proxies, etc.). The process is largely manual, often
internalized by experienced network admins over time. In
large organizations, multiple policy sub-domains exist (e.g.,
server admins, network engineers, DNS admins, different
departments) that set their own policies to be applied to the
network components they own or manage. Admins and users
who share a network have to manually coordinate with each
other and check that the growing set of policies do not conflict and match their individually planned high level policies
when deployed together.
Given this current status of distributed network policy management, policy changes take a long time to plan and implement (often days to weeks) as careful semi-manual checking
with all the relevant policy sub-domains is essential to maintain correctness and consistency. Even so, problems are typically detected only at runtime when users unexpectedly lose
connectivity, security holes are exploited, or applications experience performance degradation.
And the situation can get worse as we progress towards
more automated network infrastructures, where the number
of entities that generate policies independently and dynamically will increase manyfold. Examples include SDN applications in enterprise networks, tenants/users of virtualized
cloud infrastructures, and Network Functions Virtualization
(NFV) environments, details in §2.1.
In all of these settings, it would be ideal to eagerly and automatically detect and resolve conflicts between individual
policies, and compose them into a coherent conflict-free policy set, well before the policies are deployed on the physical
infrastructure. Further, having a high level policy abstraction
and decoupling the policy specification from the underlying
physical infrastructure would significantly reduce the burden
CCS Concepts
•Networks → Programming interfaces; Network management; Middle boxes / network appliances; Network domains; Network manageability; Programmable networks;
Data center networks;
Keywords
Policy graphs; Software-Defined Networks
∗
This work was performed while at HP Labs.
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SIGCOMM ’15, August 17 - 21, 2015, London, United Kingdom
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DOI: http://dx.doi.org/10.1145/2785956.2787506
29
INTRODUCTION
tualized network level are created without any knowledge of
the underlying physical infrastructure, but must comply with
the network operator policies at runtime. A simple change
of operator policy may affect and conflict with thousands of
existing tenant policies.
NFV service networks aim to virtualize the service functions that are today implemented on specialized hardware
middleboxes [10, 11]. A key requirement is to provide service function chaining (SFC) in these environments, with
no or minimal knowledge of the network infrastructure and
middlebox internals. SFC can be specified as network policies where specific packets/flows have to strictly follow the
chain of service functions. Multiple chains devised by different administrative entities may be deployed on the same
network infrastructure; the logical chains need to be composed into one coherent service chain.
on the admins/users/application developers of the network
both in crafting and implementing their policies.
In this paper, we address the open problem of automatic,
correct and fast composition of independently generated network policies. Our contributions are:
1. We present a new high-level model called Policy Graph
Abstraction (PGA) for expressing network policies.
PGA is a simple and intuitive graph-based abstraction
for each sub-domain to separately express networking
policy on endpoints, independent of underlying network infrastructure. PGA naturally incorporates network middleboxes and enables automatic, eager composition.
2. We develop algorithms to automatically and scalably
compose multiple policy graphs. The composition
maintains the individually specified invariants from
each policy graph. It also systematically determines an
appropriate service order when merging service chains.
2.2
We use an example to illustrate the challenges that arise
in composing policies in the above scenarios. Consider the
two policies depicted graphically in Fig.1(a), inspired by a
real enterprise scenario. Suppose a company’s marketing
department wants to deploy a CRM (Customer Relationship
Management) application on some of the company’s servers.
The CRM admin specifies a network policy, called P1, that
allows only marketing employees to send traffic to the CRM
servers; the traffic must use TCP port 7000 and must pass
through a load balancing service (LB).
Independently, the company-wide network admin specifies another policy, called P2, that restricts company employees to access company servers only through TCP ports 80,
334, and 7000; and the traffic must pass through a firewall
service (FW). Note that Marketing employees are a subset
of all employees, just as CRM servers are a subset of all the
company servers as indicated in Fig. 1(a) by the subset symbol ⊂. These independently specified policies need to be
combined into a coherent composed policy that respects the
intent of both stakeholders.
Correctly composing P1 and P2 is actually not that simple with currently available tools and languages. First, P1
and P2 contain two different types of policies: access control whitelisting (ACL) and network service chaining (FW,
LB). Since the src/dst and port range of P1 are completely
encompassed by those in P2, one may naively compose the
ACL policies by prioritizing P1 over P2, but this incorrectly
allows non-Marketing traffic to reach CRM servers. In addition, assuming the intended order of the service chain is
FW followed by LB, the intent needs to be factored in for
[Mktg→CRM] traffic.
Network programming frameworks such as Merlin [38]
and GBP [6] are not suitable for independent policy specification. GBP does not support composition and so a single combined program needs to be written manually. Merlin
supports distributed but not fully independent policy specification. The network admin needs to explicitly delegate a
policy to the CRM admin. The CRM admin is only allowed
to modify that delegated policy in restricted ways, and in do-
3. We present the design and implementation of the PGA
system. We leverage existing SDN programming languages (e.g., Pyretic) to represent middlebox functionality and analyze service chains. The system can compose over 20K ACL policies from a real policy dataset
in under 600s, while incurring sub-millisecond latency
for the first packet of a flow when running reactively.
In this paper, we focus on policies related to network endpoints and decouple underlying network state information
from the PGA abstraction. PGA treats the underlying network as “one big switch”, which in some cases may not reflect all the low level policy requirements: e.g., traffic engineering decisions regarding specific switches/routers/network path [32]. We also do not focus on run-time network
state conflicts, which has been recently studied in [33, 39,
13]. Occasionally, there will be a need to consider lower
layer network state in making correct overall network-wide
policy decisions. We leave this interplay between the PGA
abstraction layer and the physical infrastructure state for future work.
2.
BACKGROUND
We present three scenarios where the distributed policy
specification and composition problem arises, followed by a
detailed example that demonstrates technical challenges.
2.1
Challenges in Policy Composition
Target Scenarios
Enterprise networks manage policies using the notion of
network compartments, which are defined based on administrative domains (e.g., external access network, geographical site), application/service (e.g., DNS), network protocol
(e.g., IPv6) or network technology (e.g., WLAN). There can
be hundreds of compartments governed by special policies
that are independently specified and managed; the policies
generated by multiple compartments may be applied to the
same set of network devices.
Cloud infrastructures: tenants want to have their virtual
networks with their own policies. These policies at the vir-
30
P1:
Mktg.
7000
P2:
80, 334, 7000
Employees
FW
Servers
CRM exclusively serves
only Mktg. employees
if_(match(srcip=Mktg, tcp, dstport=7000, dstip=CRM),
FW>>LB>>route,
if_(match(dstip=CRM), drop,
if_(match(srcip=Empl, tcp, dstport=80|334|7000, dstip=Servers),
FW>>route, drop)))

CRM

LB
(a) Independently specified policies.
(b) Pyretic-style composite program.
Subject1: CRM-Access
tcp, dstport =7000 : Permit, FW-LB chain
Subject2: CRM-Block
* : Deny
Subject3: Server-Access
tcp, dstport=80|334|7000 : Permit, FW chain
Mktg
7000
FW
80, 334, 7000
Empl
Clauses (Prioritized):
1. Mktg --> CRM : CRM-Access
2. * --> CRM : CRM-Block
3. Employees --> Server : Server-Access
Mktg
LB
CRM
FW
Servers
80, 334, 7000
FW
CRM
(d) Composed policy in a graph.
(c) composite policy specified in GBP.
Figure 1: Policy composition example.
LB chain is chosen using the operator’s internal knowledge
of the service functions.
Even if such joint intents are clear to the human operator, it is impractical and error-prone to manually compose
thousands of real world policies that have more complex
super/sub-set relations and access control requirements: e.g.,
exclusive access to source/destination, conditional on other
attributes such as location and security level.
Thus, automated composition by the system, not by a human, is critical to build a practical and scalable policy framework. The key to enabling automatic composition is to explicitly capture the internal intents of the individual policy
writer in each policy. Existing policy abstractions [19, 23,
38] do not support this. For instance, they cannot express
the intent that an Allow ACL rule MUST allow its specified
traffic; thus, the Allow rule can be overridden by a Deny rule
from another policy it is combined with. Similarly, existing service chain policy work [38, 31] can only capture the
intent that certain service functions should be deployed on
the specified path, but they cannot capture the service functions/actions that MUST NOT be applied by other policies.
Hence, a human oracle is required to manually combine service function requirements from different policies.
Fig.1(d) shows a correct composition of P1 and P2 in a
simple graph. Only Mktg. employees can send to the CRM
servers through the FW-LB service chain. The other servers
except for CRM, expressed by a primitive set operator diff
(‘−’), accept traffic from all Employee devices including
Mktg. An entire policy for any endpoint pair is expressed
on the edge between two nodes representing the endpoints,
with no need to carefully walk through multiple lines of prioritized rules or if_then statements. We show how to automatically create such composed policies in §4 and §5. We
first lay out the required properties for a policy framework.
ing so must be exposed to and take into account the network
admin’s intent.
Other frameworks such as Frenetic [20] and Pyretic [34]
allow users to compose modular policies/programs into a
more complex control program. For example, Pyretic users
can cause two policies to be sequentially applied to an incoming packet using the >> operator, and this is effective
to chain multiple service functions (such as FW>>LB). The
sequential operator cannot be used to directly compose P1
and P2: e.g., P1>>P2 composition fails to allow [Empl→
Servers−CRM] traffic since the traffic cannot pass P1’s ACL.
Pyretic‘s parallel composition, P1+P2, will apply each policy to a different copy of the same packet and fail to block
non-Marketing traffic and to create the FW>>LB chain.
From these failed attempts to combine P1 and P2, we see
that a correct composition requires carefully decomposing
each of P1 and P2 into ACL and service requirements and recomposing them into a single program. In particular, Pyretic
supports an if _(match(), A, B) statement for ‘if match()
do A, else B’. With this, users can write a composite program implementing P1 and P2, as shown in Fig.1(b). However, the user has to carefully consider the flow space relations (P1 ⊂ P2) and manually compose the FW>>LB chain
for [Mktg→CRM], place P1’s match() classifier followed
by P2, and insert if _(match(dstip = CRM ), drop, ...) in
between to implement exclusive access to CRM. A similar
manually composed program for GBP is shown in Fig.1(c).
Such manual decomposition and re-composition process
is possible when done by a human operator who clearly understands the joint intent of P1 and P2. Based on such understanding, the operator can 1) resolve, the ACL conflict
between P1 and P2 and 2) decide, the order between FW
and LB. P1 and P2 ACL policies do conflict since P1 blocks
traffic from non-Mktg employees to CRM while P2 allows
the traffic (by allowing its super-set). The joint intent used
to resolve this conflict would be P1’s exclusive access policy
overrides P2’s allow policy. Similarly, the order of the FW-
2.3
Requirements for policy framework
Simple and intuitive: Anecdotally, we found that many
network admins and cloud tenants design their policies by
31
drawing diagrams on whiteboards. We believe a policy abstraction must be as simple as drawing diagrams similar to
Fig.1(a), yet expressive enough to capture their intents for
diverse and dynamic SDN, cloud and NFV applications with
sophisticated service chain requirements [34, 22, 38].
Independent and Composable: Each policy writer
should be able to write policies independently without coordinating with other policy writers; yet ensuring that their
intents are being composed and enforced correctly, or receiving notification if there are conflicts or more information is
needed.
Eager composition: Composing policies and ensuring
that individual policy intents are satisfied prior to deployment, i.e., eagerly, is highly desirable. Eager composition
can greatly reduce the number of conflicts/errors that a runtime system has to handle, enable speedy runtime operation,
and reduce the chance of system misbehavior compared to
lazy composition. However, eager composition without actual endpoints in the system can lead to exponential state
explosion because in the worst case, every combination of
input policies should be considered. The policy framework
design should enable fast composition.
Automated: The policy framework must be highly automated to free network admins from manual and error-prone
policy composition. In some cases when the system cannot
identify the best policy composition, a human may be required to provide input and pick one composition. However,
this is much less burdensome than the existing approach of
manual composition.
Well-formed: The composed policy generated from the
input policies should be well-formed such that a unique policy can be chosen without ambiguity for any given packet
and associated dynamic conditions. This would allow the
runtime operation to be deterministic.
Service chain analysis: Handling service chains for correct policy composition is crucial: e.g., a misplaced FW can
drop packets that are legitimately allowed by ACL policies.
To the extent possible, the policy framework needs to model
the behavior of service functions for composition analysis.
Our framework, PGA, provides a simple and intuitive
graphical interface that is similar to how network admins
typically visualize their policies on a whiteboard. In PGA,
each user writes policies for arbitrary selection of endpoints
based on logical endpoint properties that makes sense to
them. This gives users the flexibility to write policies independent of each other as well as the underlying physical network infrastructure. PGA achieves automated, eager composition of such policies by capturing the relationship between endpoint properties and enabling individual policies
to constrain each other during composition. Lastly, it provides abstractions to succinctly model middlebox behavior
that aids in service chain analysis.
3.
Figure 2: PGA system architecture.
specify network policies. The users/tenants/admins and SDN
applications independently generate their policies as graphs
and submit them to the Graph Composer through a PGA
User Interface (UI). The UI and composer utilize additional
information (e.g., tenant hierarchies, tenant/endpoint locations) from external sources to assist in the policy specification and eager composition. The composer automatically
composes input graphs into a combined conflict-free graph,
resolving or flagging conflicts/errors and reporting them to
users, possibly with suggested fixes.
The composed high-level policy can be compiled down
to low-level configurations/rules, either proactively or reactively; PGA’s eager policy composition is orthogonal to the
lower-level compilation methodology. Our prototype supports two different network environments. The first is an
SDN environment with OpenFlow enabled network devices,
where we use the POX OpenFlow controller to reactively
generate OpenFlow rules for pkt-in events based on a composed policy graph. To guard against possible bugs in rule
generation, and against the possibility that unrelated SDN
modules, e.g., traffic engineering, may generate conflicting
rules, we use a rule verification tool (§6) to detect and verify changes in end-to-end communication paths against the
ACL policies that PGA has generated for recent pkt-in events.
Our prototype also supports a virtual network abstraction
provided by OpenStack Neutron [8]. Our PGA service proactively configures virtual network resources – specifically,
Neutron Security Groups and service functions – to implement the polices of a composed graph, and dynamically associates VMs to the best-matching graph nodes at runtime.
Both the SDN and virtual network systems can be extended to support NFV; e.g., PGA’s composed graph can be
used to generate Network Service Headers (NSH) and Service Classifiers [4] for service function chaining.
For simplicity, our discussion around prototype system
will assume the SDN-OpenFlow environment while the
graph model and composition algorithm below can be applied to diverse environments.
SYSTEM OVERVIEW
4.
Fig. 2 provides an overview of the PGA system components and their interactions with external components. As
mentioned earlier, PGA uses a graph-based abstraction to
GRAPH MODEL
In PGA, network policies are described using a graph
structure that represents: 1) allowed communication between
32
network endpoints, and 2) any required service function
chain traversal for each communication. PGA is designed so
that concise models that contain only a small number of elements are able to express policy for a much larger number of
endpoints. PGA achieves this model scalability by describing policies at the granularity of groups of endpoints (similar
to [6]) that share common properties expressed in terms of
primitives called labels. Labels can be assigned and changed
at runtime as endpoint properties (states) change, enabling a
static PGA graph model to capture all the policies that can
be dynamically assigned to an endpoint. PGA is a whitelisting model; communication must be explicitly allowed by a
PGA model, else it is implicitly denied.
We use the example graph models shown in Fig. 3 to illustrate various features of the PGA model and composition. Model (a) is given by an admin for all Departments
of a company and specifies that traffic is allowed from the
IT department to the Engineering department using specified protocol port numbers. Model (b) is given by a web
application admin. This model allows traffic from any Department to access the Web application using port 80 (HTTP
protocol) and indicates that the traffic will be load balanced.
The model also specifies that traffic is allowed from the Web
to DB tiers, and from DB to itself. Model (c) is specified
by an SDN application for DNS-based security protection.
This model requires that DNS traffic from network endpoints
with “Normal” security status must be inspected by a Deep
Packet Inspection service when it performs DNS lookups,
while network endpoints that are deemed “Quarantined” can
only send their traffic (of any type) to a Security Remediation server. Model (d) is actually two models given by
the Data Center admin. The first model specifies that traffic coming into the Data Center from the Campus must pass
through a Firewall service and a Byte Counter service, while
all east-west traffic within the data center also traverses a
Byte Counter service. The second model allows monitoring
traffic (port 9099) between endpoints within the Data Center.
PGA automatically combines multiple independently specified policy graphs into a coherent composed policy. This
requires the novel ability to merge service functions chains
in addition to access control policies. The PGA model has
three primitives that support composition.
1. The packet processing behavior of each network service function in a service function chain is explicitly
specified using a variant of the Pyretic network programming language [34] that we developed. Our composition engine (§5) analyzes these descriptions in order to automatically assemble composed service function chains that correctly combine policies from multiple policy graphs.
(b) Web application (App1) admin
(a) Departments admin
IT
22,23,5900
Engg
(c) DNS protector SDN app
Normal
53
DPI
*
Quarantined
80
Departments
LB
Web
DB
(d) Data Center admin
BC
DNS
Rmd
7000
3306
Campus
*
FW
BC
DC
*
9099
DC
Label mapping inputs:
(IT:Zone-A, Engg:Zone-B, App1:DC, Departments:DNS protector)
Figure 3: Sample input graphs and label mapping.
Tnt
Dpts
apps
App1
IT
Abbreviations:
Tnt: Tenant
DC: Data Center
Locn: Location
Rmd
Cmp
DC DNSP
Cmp: Campus
Zn-A: Zone-A
Nml Qn
Zn-A Zn-B
Zn-B: Zone-B
Locn
Engg Web
DB
Status
DNS
Dpts: Departments
DNSP: DNS Protector
Service
Nml: Normal
Qn: Quarantined
Rmd: Remedy Server
Figure 4: Sample input label namespace hierarchy.
overlapping membership not only between endpoint
groups of different graphs but also within a single graph.
3. Composition constraints are introduced to give policy
writers the flexibility to express invariants that can
never be violated under composition. Each policy writer
can independently express their intended invariants, and
the PGA system will automatically compose these individual policies while respecting the invariants. This
avoids imposing rigid universally applied conflict resolution policies (such as static priority), and minimizes
the need for human intervention during composition.
We next describe the basic PGA graph constructs, followed by the primitives that support composition.
4.1
Graph Constructs
Vertices and Labels: Each vertex in a PGA graph model
represents an endpoint group (EPG) which comprises a set of
endpoints (EPs). An EP is the smallest unit of abstraction for
which a policy is applied, e.g., a server, VM, client device,
network, subnet, or end-user. An EPG comprises all EPs
that satisfy a membership predicate specified for the EPG. In
Fig. 3, each membership predicate is given as a label, e.g.,
Web, DC, etc. In general, a membership predicate can be a
boolean expression over all labels.
Fig. 4 shows an example set of labels arranged in a hierarchy. The labels at the leaves, e.g., IT, Engg, Web, etc. are the
truly basic elements, while each non-leaf label is a composite label that is simply a convenient shorthand for the logical
disjunction (boolean OR) of all of its descendant leaf labels,
e.g., Dpts is equivalent to IT OR Engg. Though not shown
in the figure, composite labels can be defined that do not fit
into a hierarchy, and can in general represent shorthand notation for arbitrary expressions over leaf labels. The hierarchy serves another important purpose for PGA composition.
As we describe in §5, the composition process translates input graphs into a normalized form in which all EPGs have
disjoint membership. PGA therefore needs to know which
labels are mutually exclusive, i.e., cannot ever be assigned
2. A label mapping input is introduced to enable identification of endpoint groups that can have overlapping
endpoint membership. This avoids unnecessarily composing endpoint groups that are mutually exclusive,
thus greatly reducing computation time and memory
requirements. Label mapping is necessary to detect
33
box or a set of middleboxes. A directed edge from endpoint
group E to endpoint group E’ with Boolean predicate (classifier) B and path expression (service chain) P indicates that
a correct implementation will forward traffic from all hosts
in E satisfying B along paths that are a prefix of the concatenation of the network function boxes in P and some host
in E’. For example, model (c) in Fig. 3 specifies that traffic
sent on port 53 is allowed from Normal (Nml) EPs to DNS
server EPs and the packets must pass through a DPI network
service. The implementation should also take care of forwarding to the right destination EP which might be decided
at the source EP or at a network function box (e.g. load balancer). In the degenerate case where E and E’ are singletons,
B is the trivial predicate that always returns true, and P is the
empty path expression, an edge reduces to a specification of
point-to-point forwarding.
A model can have multiple directed edges from an EPG,
as long as the edge classifiers cover non-overlapping flow
spaces. Edge whitelist rules are stateful, such that the reverse traffic on established connections (e.g. TCP) is also allowed. Two types of edges can be specified. A whitelist edge
is depicted as a solid line and describes an allowed communication, e.g., from IT to Engg in Fig. 3. A conditional
edge is depicted as a dotted line and specifies a service chain
requirement which is instantiated if and only if the edge’s
match condition overlaps the classifier of a whitelist edge in
a different policy graph. A conditional edge, by itself, does
not allow communication. Fig. 3(d) illustrates the utility
of conditional edges. The Data Center admin’s intent is not
to allow all communication from Campus to DC but to have
any communication allowed by any other graph pass through
the specified service chain. If no policy graph has a whitelist
edge from Campus to DC, then the service chain requirement is not needed in the fully composed policy graph.
simultaneously to an EP. For example, in the DNS protector
app of model (c), an EP cannot be both Normal and Quarantined. The hierarchy provides this information. Specifically, in any single tree of the hierarchy, any set of labels
that do not have an ancestor relationship are mutually exclusive, e.g., {Zn-A, Zn-B}, {Cmp, DC}, and {Dpts, App1}.
The PGA system can restrict the scope of each policy
writer to a particular relevant subset of the label space; for
example, the admin of the Campus Network might only be
allowed to use labels Cmp, Zn-A, and Zn-B for defining
EPGs in its policy graphs. Each leaf label represents a collection of boolean variables, one per EP, i.e., leaf label x
represents the set of boolean variables {e.x|e ∈ E}, where
E is the set of all EPs. Assigning a leaf label to an EP sets
the value of the corresponding boolean variable to True, otherwise it is False. Labels can be split into three categories:
‘tenant’ labels identifying end-users or their applications,
e.g., IT or Web, ‘network location’ labels identifying regions
of the network topology, e.g., Zn-A or DC, and ‘status’ labels indicating dynamically changing properties, e.g., Qn for
currently quarantined EPs. To illustrate how labels are used,
say that server S is an EP that is located in the Data Center
and hosts the database of Web Application of Fig. 3. Then,
server S would be assigned labels DC and DB, setting its
boolean variables S.DC and S.DB to True.
EPs can be assigned labels dynamically at runtime, causing them to move from one EPG to another. For example, a
server that was assigned the label Nml (normal) could subsequently be relabeled Qn (quarantined) when a network monitor detects the server issuing a DNS query for a known malicious Internet domain. Thus, a static PGA graph model
actually describes a set of network policies that are applied
dynamically to each EP according to the EP’s status changes
over time (that can be programmed as a finite state machine
[28]). Moreover, analysis and composition of policy graphs
into a fully composed network policy graph is a procedure
that only needs to be invoked when policy graphs are added,
modified, or removed. Analysis is not needed when EPs
change EPG membership. Instead, the runtime system only
needs to perform the lightweight operation of looking up and
applying the correct rules for each EP depending on its current EPG membership. As EPs change membership across
EPGs, the set of addresses encompassed within an EPG also
changes. In general, an EPG can be associated with a variable representing virtual addresses indicating ‘some EP’
within the EPG – e.g., virtual IPs used for server
load-balancing – and policies can be written using the EPG
variable as well.
Edges and Service Chains: As stated earlier, PGA is a
whitelisting model; by default, no communication is allowed
between any EPs. A directed edge between EPGs is required
to specify allowed communication in a PGA policy. An edge
consists of a classifier, which matches packet header fields to
represent the security whitelisting rule. Classifiers may include virtual addresses described above. It also optionally
has a service chain consisting of a sequence of one or more
network function boxes. A network function box may correspond to an SDN controller function or to a network middle-
4.2
Primitives Supporting Composition
Network Function Box Behavior: To enable automatic
composition of service function chains, PGA models need
to specify the packet processing behavior of each network
function box. For this, PGA uses the open source Pyretic
SDN language [34], and extends it to enable programming
based on EPGs. This allows a user to specify the full behavior of a network function box as a Pyretic program; we
term this white boxing. Whiteboxing is not a hard requirement. In cases where middleboxes are used for which the
internal logic is not known, we support gray boxing. Herein,
the corresponding network function box is described by a
Pyretic program that captures only the high-level bounding
behavior of the middlebox. For example, a commercial L7aware load balancer can be gray boxed as match(dstip
= Web.virtIP) » modify(dstip = Web.RIPs),
where Web.RIPs is a set of real IP addresses of destination
web server EPs and Web.virtIP is the exposed virtual address of the web service. While the precise mapping of an
input packet to a destination IP address is not known, the
model accurately bounds the output header packet space of
the middlebox.
34
We note that whiteboxing and grayboxing may not cover
every possible NFV scenario. The current PGA framework
does not consider network functions that duplicate and forward packets along different paths; this also implies that a
network function box is allowed to modify the destination
address to only hosts within the destination EPG of the edge
that the box belongs to. Extending PGA to handle those
cases is our future work.
Label Mapping: Policy composition combines the EPGs
of one graph with EPGs of a second graph, potentially causing exponential growth in the size of the composed graph as
the number of graphs to be composed increases. Usually,
however, the vast majority of EPG combinations are mutually exclusive, i.e., it is impossible for any EP to belong to
both of the original EPGs that are being combined. For example, if all IT dept. EPs are always located in the datacenter and never in the campus network, then a combined EPG
that corresponds to EPs that are both in the IT dept. and in
the campus network is guaranteed to be the empty set and
thus does not need to be generated in the composed policy
graph. The label mapping input to PGA enables composition to avoid generating such impossible EPG combinations
by capturing the relationship between labels across different
label trees (e.g. tenant and location label trees). The label
mapping is a symmetric relation F ⊆ L × L, where L is the
set of all leaf labels. If labels (x, y) ∈ F , then a single EP
can be assigned both labels x and y. For example, the label
mapping shown in Fig. 3 indicates that an IT EP can also
be a Zone-A EP, i.e., the IT dept. is located in the network
Zone-A. For simplicity, the syntax used to specify the label
mapping can give only one side of the symmetric relation.
Also, it can use non-leaf labels as a shorthand for all of the
leaf label descendants. The detailed use of label mapping to
limit composition complexity is covered in §5.
Composition Constraints: A policy graph can flexibly
specify constraints on the policy changes that are allowed
when the policy graph is to be composed with any other policy graph. Constraints can be specified for any ordered pair
of EPGs in a policy graph. The constraints can limit the addition of new classifiers that would allow additional traffic
from source EPG to destination EPG. For example, a policy
that strictly allows only port 80 traffic from a source EPG
to a destination EPG can use constraints to prohibit additional ports from ever being allowed even if they are allowed
for the same EPG pair by whitelists in other policy graphs.
Classifier constraints can also limit the removal of allowed
traffic as a result of composition.
Regarding service chain composition, the constraints can
limit the behavior of function boxes that are added to service chains when the policy graph is composed with another graph. Specifically, the constraints can place limits on
the packet header field modifications and packet drop operations that additional function boxes can perform on packets.
Since the behavior of function boxes is modeled using our
extended Pyretic, composition analysis can check whether
adding a specific function box to a given service chain would
violate the constraints given by the policy graphs that are being composed together.
Match
port 80
port 88
*
Classifier
Add Remove
Y
N
N
Y
Drop
N
Function Box
Modify
DSCP=16,18,20
Table 1: Example composition constraints.
Table 1 shows example composition constraints from a
source EPG to a destination EPG. The table indicates that
port 80 traffic cannot be disallowed through composition
with other graphs, and function boxes that are added cannot drop packets but are allowed to modify the DSCP packet
field to a set of specific values. The table also shows that
port 88 cannot be allowed through composition, but otherwise all other traffic can be allowed, with no restriction on
function boxes. Composition constraints could more generally be specified using a constraint language such as Prolog.
A commonly needed special case for composition constraints occurs when a policy graph specifies that no additional traffic should be allowed to or from a particular EPG.
Composition constraints can express that, i.e., in a policy
graph, all EPG ordered pairs that contain the EPG have the
constraint that composition is prohibited from changing the
range of allowed traffic or the service chain. We can more
conveniently and concisely represent this set of constraints
by marking the EPG as “exclusive”. For example, the Qn
EPG in Fig. 3 is an exclusive EPG, preventing other policies
from thwarting the intention of the policy writer to redirect
all traffic from quarantined hosts to a remediation server.
5.
GRAPH COMPOSITION
A key goal of PGA is to enable policy writers to specify their policies independently and delegate the composition
process to the system. The system should compute the union
of all policies from the input graphs, subject to composition
constraints, to generate a composed graph. The composed
graph should be well-formed (§2), i.e., comprise purely mutually exclusive EPGs to allow the PGA runtime to determine the unique EPG for each EP, and then apply the associated network policies to the EP (or determine that the EP is
not in any EPG and so no communication for it is allowed).
Composition in PGA is different from the union (parallel)
and sequential composition operators used in NetKAT [12]
and Pyretic [34]. Their union (parallel) operator applies each
of the constituent policies to a different copy of the input
packet and then computes the union of their output packets
while the sequential operator applies the output of one policy as input to the other. In PGA, we compute the union of
the policies themselves based on set theoretic Venn diagram
analysis. A PGA policy in a simplified form is a combination of match + action: EPG labels and edge classifiers form
the match space while edge types and service chains constitute the action part. Conceptually, PGA composition takes
the union of the match spaces of the input graphs: inheriting
actions from the input graphs for the non-overlapping match
spaces, and combining actions for the overlapping (intersecting) match spaces subject to composition constraints. Note
35
term and check the label mapping to identify all other potentially related labels, i.e., labels that are transitively related
to the labels in the term. For example, if the term is label1
AND label2, we check the label mapping starting from each
of these labels and add the result to the conjunction. Suppose this adds label3 AND label4 AND label5. If any of the
resulting labels are mutually exclusive, e.g., Nml and Qn,
we split the terms accordingly. For example, if label4 and
label5 are mutually exclusive, we form two terms: 1) label1
AND label2 AND label3 AND label4, and 2) label1 AND
label2 AND label3 AND label5 . We continue splitting the
terms until no term has mutually exclusive labels. This final
set of terms corresponds to globally disjoint EPGs.
Once the normalized EPGs are generated, composition
constraints from the input graph need to be replicated to the
normalized graph. In particular, suppose that EPG S in the
original input graph is translated to normalized EPGs S1 , S2 ,
..., Sm , and input graph EPG D is normalized to EPGs D1 ,
D2 , ..., Dn . If the input graph has constraints for source
EPG S and destination EPG D, then the constraints must
be replicated in the normalized graph for ordered EPG pairs
(Si , Dj ), ∀i = 1..m, ∀j = 1..n.
Composition constraints may need to be merged in addition to being replicated. If the original graph has constraints
for EPGs (G, H), and these EPGs overlap with EPGs (S, D),
then any constraint specified for (G, H) must be merged with
the constraint for (S, D) in order to construct the constraints
for the normalized EPGs that constitute the overlap. Merging composition constraints entails adopting the union of restrictive invariants of two overlapping constraints. However,
conflicts may be detected if an invariant from one composition constraint, e.g., never allow port 80 traffic, is opposed
by an invariant from another composition constraint, e.g.,
never deny port 80 traffic. Such conflicts are error conditions flagged to operators.
Finally, normalization replicates and merges edges from
the original graph’s EPG pairs to the normalized graph’s
EPG pairs to express equivalent policies. If EPG S has an
edge to EPG D in the original graph, then the original edge
is replicated to the normalized graph EPGs (Si , Dj ), ∀i =
1..m, ∀j = 1..n. Any edges for (G, H) need to be merged
with those of (S, D) on the normalized EPG pairs that constitute the overlap. This merging is governed by the (merged)
composition constraints.
that EPGs can have overlapping EP membership specified as
arbitrary Boolean expressions over the label space.
We accomplish this composition in two steps. We first
normalize input graphs (§5.1) by transforming their EPGs
into an equivalent set of disjoint EPGs to easily identify the
overlapping space and generate well-formed policies in the
composed graph. We then compute the union of the normalized graphs (§5.2) by creating directed edges equivalent
to the union of the original policies, except where doing
so would violate the invariants given by composition constraints. The overlapping policies, identified by a common
classifier for the same src-dst EPG pair in the normalized
graphs, can require two service chains to be merged in the
union. We use composition constraints to select the network
function boxes to include in the combined service chain.
To select an appropriate service order, PGA detects dependencies between function boxes based on modeling of their
packet processing behaviors, and use the dependencies to determine valid orderings. Additionally, we detect possible remaining conflicts that should be flagged to policy writers.
5.1
Normalization of Input Graphs
In normalization, we compute a set of globally disjoint
EPGs which represent the equivalence classes of endpoints
to which the same set of policies should be applied. We
then transform each input graph into an equivalent normalized form where policies are expressed only with respect to
the newly computed EPGs.
The first step is to translate input graph EPGs into EPGs
with globally disjoint membership. Second, composition
constraints from the input graph must be replicated and
merged to the normalized graph. Third, edge policies from
the input graph must be replicated and merged to the normalized graph, subject to the composition constraints added
in the previous step.
The abstractions of label hierarchy and label mapping facilitate the translation of input graph EPGs into globally disjoint EPGs in the normalized graph. As described in §4,
label hierarchy captures the sets of mutually exclusive labels. Each EPG in the input graph is first split into locally
disjoint EPGs by algebraically rewriting the EPG’s membership predicate expression into an equivalent positive disjunctive normal form. Each term in the resulting expression describes a locally disjoint EPG. More specifically, we replace
each composite label in the expression with its leaf label
equivalents. Then, we expand the expression and remove
any negated labels by replacing them with the disjunction of
all the other sibling leaf labels in the hierarchy (since they
are mutually exclusive). Finally, we convert the expression,
which is now using only leaf labels in positive form, into
disjunctive normal form, i.e., ORing a collection of terms
where each term is the AND of leaf labels. Any conjunctive term that has any mutually exclusive labels must be the
empty set, and so we delete these empty terms from the expression. Each remaining conjunctive term defines an EPG
that is disjoint within the same input graph.
To obtain globally disjoint EPGs, we may need to further
divide each locally disjoint EPG. We take each conjunctive
5.2
Graph Union
In graph union, for each globally disjoint EPG that may
be in multiple normalized graphs with different policies, we
compute the union of those policies to obtain the final policy that should be applied to its endpoints. This involves
merging whitelists and service chains from multiple edges
without violating each other’s constraints.
In normalized form, EPGs in two different graphs are either disjoint or equal. Partial overlap is not possible. This
property enables multiple normalized graphs to be composed
together using a simple union operation. The composed graph
has the union of all the EPGs of the individual graphs. The
union operation also copies and merges composition con-
36
straints and directed edges from the individual graphs to the
composed graph. Merging composition constraints is performed identically as described in §5.1.
As edges from the individual graphs are added to the composed graph, they are checked against the composition constraints for the source and destination EPGs. This check determines whether an edge’s classifier satisfies the constraints
or needs to be narrowed to be in compliance with them. If
a new edge passes this test with a non-null surviving classifier, then it may be added or merged with existing edges
from source to destination EPGs.
The first step in merging the new edge is to find the intersection of its classifier flow space with the existing classifiers. For the non-intersecting classifier space of the new
edge, the new edge and its function boxes can be added directly, subject to function box composition constraints. For
any intersecting classifier space, merging is needed. We
break down the intersecting space into a matching set of subspaces in the existing policies and for the new edge. This allows us to merge, for each subspace, one existing edge with
one new edge.
For each pair of edges that need to be merged, if either
edge has a service chain for the intersecting space, then service function composition is required that combines function
boxes from the service chains of both edges. An important
challenge is to determine a proper ordering of the service
functions in the composed chain. PGA determines the order
by analyzing function box actions on different flow spaces.
The analysis identifies input-output dependencies between
different function boxes and constructs an ordering that is
consistent with the dependencies. In addition, the analysis
identifies potential function box conflicts in which two boxes
perform apparently incompatible operations on a common
packet space. For example, if one function box drops all
packets in a packet space for which the other function box
has a byte counting action, then there is likely a conflict. Finally, a set of heuristics is used to further improve ordering
after satisfying detected dependencies and flagging possible
conflicts.
The key to enabling PGA to compose and merge service
function chains automatically is that the packet processing
behavior of function boxes is explicitly exposed to the PGA
system for analysis. PGA models network function white
boxes and gray boxes behavior expressed explicitly in an extended version of Pyretic. The Pyretic compiler can take
these descriptions and convert each network function box’s
behavior into a set of prioritized match-action rules. PGA
analyzes these rules to characterize the In Packet Space and
an Out Packet Space for each rule of every service function.
The In Packet Space of a rule is defined as the flow space
that would match and thus be processed by the rule, while
the Out Packet Space is the outcome of processing the In
Packet Space by the rule.
When merging two service function chains, PGA analyzes
every pair of function boxes composed of one function box
from each chain to identify a full dependency graph, and
possible conflicts between different function boxes. For each
function box pair, analysis considers every pair of match-
LB:
Policy:
match: (dstip, Web.virtual_ip) >>
modify: (dstip: Web.real_ips)
Dependency
Byte Counter:
Policy:
match: (dstip, DC.real_ips) >>
count_bytes: {group_by: [dstip]}
Rules:
match: (dstip, Web.virtual_ip) ->
set([modify: (dstip, Web.real_ips)])
Rules:
match: (dstip, DC.real_ips) ->
set([count_bytes:{group_by:[dstip]}])
Identity -> set([])
Identity -> set([])
Conflicts
Figure 5: Analysis of service functions to determine order.
action rules across the two service functions to find all dependencies and possible conflicts.
A dependency is identified from one rule to another if
the first rule actively creates (through modify action) an Out
Packet Space that overlaps with the second rule’s In Packet
Space. In Fig. 5 we show the Pyretic policies for two function boxes, Load Balancer (LB) and Byte Counter. Pyretic’s
compiler has converted these policies into prioritized matchaction rules as shown below each Pyretic policy. In this example, as shown by the Dependency arrow, the In Packet
Space of the Byte Counter is dependent on the Out Packet
Space of LB, because the action of LB modifies the destination IP address to a real_ip address that is checked by the
match portion of the Byte Counter’s rule.
Possible conflicts between function boxes may be uncovered by the analysis and avoided through ordering or reported to users as possibly unresolvable conflicts. In Fig. 5,
one possible conflict is between the default drop action in the
Load Balancer and the byte counting in the Byte Counter,
because for the same In Packet Space (destination IP address is not Web.virtual_ip), the two boxes have apparently
incompatible actions (drop versus count). Another possible
conflict is between the modify destination IP address action
of the Load Balancer and the default packet drop action of
Byte Counter. For the same In Packet Space (destination IP
is Web.virtual_ip) the two actions are modify destination IP
address versus drop, which are seemingly incompatible.
The final service order is determined using topological
sort over the dependency graph. When there are more than
one possible order, we choose an order that satisfies constraints on the input edges, and finally resort to heuristics,
such as: a security service function, identified by its policy
that actively drops packets based on some criteria, should be
placed ahead of other service functions. This is consistent
with how networks are typically administered.
For reference, the final composed graph obtained for our
example from Fig. 3 is shown in Fig. 6. Note that a single
function box from an input graph may be replicated across
multiple edges in the composed graph (e.g. DPI). These are
logically the same function box.
6.
PROTOTYPE
Our prototype implementation contains ≈2.5K SLOC in
Python, including the following Pyretic extensions.
37
Abbreviations:
DD: DB.Data Center
IAN: IT.Zone-A.Normal
EBN: Engg.Zone-B.Normal IAQ: IT.Zone-A.Quarantined
EBQ: Engg.Zone-B.Quarantined
WD: Web.Data Center
DNS
DPI
DPI
53
22,23,5900
53
EBN
IAN
80
FW
LB
FW
*
EBQ
*
LB
BC
BC
BC
80
IAQ
we modify the Pyretic runtime to query the in-memory graph
upon a pkt_in event to lookup the ACL and service chain
policies, which are then compiled down to OpenFlow v1.0
rules through the Pyretic compiler and POX controller.
The underlying network topology is run on mininet [2].
The prototype system installs flow rules on the edge switches
(similar to ref. [29]). The switch/port that each endpoint
(EP) is attached to is pulled out from mininet but can be
given by external sources such as 802.1X. The path from
source EP to destination EP is given by the Pyretic
mac_learner ; more sophisticated waypoint routing
algorithms [38, 31, 26] can be used to support middleboxes
and include non-edge switches for policy enforcement.
Runtime verification: The presence of service chains in
a composed PGA graph complicates runtime policy verification. We leave it to future work; but verify only whether
the ACL policies in the composed graph are correctly realized on the network, by using VeriFlow [27] in our prototype, against potential violations due to: 1) the policy-to-rule
compiler having a bug in generating flow rules, or 2) there
being another control module in charge of other types of
policies (e.g., traffic engineering, switch firmware upgrade)
that changes flow rules without involving PGA.
VeriFlow runs as a proxy intercepting OpenFlow messages between the controller and switches to detect three
event types: reachability setup, blackhole (incomplete routing path) and forwarding loop. We modify VeriFlow to report detected events and affected flows to the PGA runtime,
which caches prior pkt_in events and the ACL decisions
made for them. PGA compares the reported VeriFlow event
with the cached ACL decisions to verify the following: reachability should be set up for allowed flows and no path should
be set up for ACL-denied flows. If the allowed path is not set
up properly (blackhole, loop, or lack of reachability setup),
it is a violation of PGA policy. Similarly, we raise an alarm
if a reachability setup event is reported for a pair of EPs for
which the composed PGA graph does not allow communication. Note that these VeriFlow verifications may not hold
if the service functions in the path drop/add/change packets.
WD
3306,
9909
7000,
9909
BC
BC
Rmd
BC
DD
9909
9909
Figure 6: Composed graph for the running example.
6.1
Abstractions
To support policy graph specification, we extend Pyretic
with three primitives: EPGs, Function boxes and Whitelists.
EPG. The user may create EPGs and specify membership
requirement for endpoints to join the EPG through labels.
EPGs also contain member variables indicating the set of L4
ports and endpoint IP addresses, which will be dynamically
updated at runtime, and virtual IP addresses (used for loadbalancing) of that EPG. The user may reference these variables while writing policies without having to assign values
to them.
Function Box. The function boxes in our prototype are expressed in our extended Pyretic programming language. The
policies are described in terms of the names of EPG variables, like the set of endpoint IPs and virtual IP, without
having to assign values to the variables a priori. Both static
and dynamic policies are supported as long as the dynamic
policies contain an expression of their bounding behavior as
described in §4. This expression is required for analyzing
dependencies and conflicts between function boxes to determine their intermixed order when merging edges. The
extended Pyretic language provides the necessary constructs
to express this behavior.
WhiteList. The user may express Whitelists as arbitrary
ranges of values for different flow header fields, e.g.
WhiteList(dstport=[80,>8000],proto=[6]).
Set operations – Union, Intersection and Difference – are
supported to express more complex Whitelists.
6.2
7.
System operation
PGA IN ACTION
We demonstrate the power of PGA in conflict detection &
resolution through two case studies on real world examples.
As shown in Fig. 2, a composer module takes all the graphs
and any auxiliary inputs and generates a composed graph by
implementing the algorithm of §5. In our SDN prototype
of the runtime system, the composed graph is stored as an
in-memory hash table keyed with the source and destination
EPGs for fast lookup of policies at runtime.
OpenFlow rule generation: In principle, the eagerly composed PGA policy could be enforced by proactively installing
an equivalent set of OpenFlow rules into the network
switches, determined using state of the art rule compilers [24,
36]. For simplicity, however, our runtime system implementation takes a fully reactive approach that evaluates the first
packet of a new flow (≈ OpenFlow pkt_in event) against the
composed policy graph and then installs rules to enforce the
ACL and service chain policies for the new flow. For this,
7.1
Conflict between SDN apps
We consider the following example inspired by a real
world anecdote of installing two SDN apps on the same network: one is the QoS app in Fig. 7 and the other is the DNS
filtering/protector app (Fig. 3). One way to handle the OpenFlow rules generated from the two apps is to compose them
into one prioritized rule table to detect potential conflicts.
An exclusive set of non-contiguous priority ranges was manually assigned to each app; e.g., the first and third highest
ranges were given to the DNS protector while the second and
the fourth highest ranges to the QoS app in one table snapshot. Since there is no global priority coordination across the
apps, it is possible for the QoS app’s rule that marks QoS bits
38
QoS App
QOS
QoS App + DNS protector
QOS
Clients
QOS
VoIP
Server
C.Nml
C.Qn *
53
types of conflicts: unmatched outgoing rules, i.e., an outgoing rule in source compartment without the incoming rule to
permit the communication in the destination compartment;
and unmatched incoming rules, i.e., the incoming rule without the matching outgoing rule. The former is more damaging since it will result in blackholing. The latter means the
incoming rules are not used since no traffic will be sent out.
In total, we detect that 4.7% of outgoing policies and 4.5%
of incoming policies are unmatched.
Rmd
DPI
DNS
QOS
VoIP
Server
Figure 7: QoS app vs. DNS protector.
of VoIP packets and forwards them to output ports to be assigned a higher priority than one of the DNS protector rules
that blocks quarantined IPs, potentially failing to block due
to the prioritized QoS rule. This potential conflict between
the two SDN apps was not initially detected because the two
rules were written for non-overlapping IP addresses in the
given snapshot of the rule table, although the two apps can
be applied to the same client devices in other settings.
Another potential conflict was detected between the QoS
rule and another DNS protector rule that redirects packets
from DNS servers to different output ports. However, the
detected conflict turned out to be a false positive as DNS
servers were not supposed to be VoIP clients, and so the two
rules actually don’t overlap. These examples demonstrate
the limitations of specifying and combining SDN apps at
OpenFlow rule level, coupled with prioritization [24] and IP
address assignment. PGA’s label-based policy specification
and graph composition enables eager conflict detection/resolution without requiring prioritized rules and IP addresses.
On the right of Fig. 7 is the composed PGA policy from
the two apps. The EPG for quarantined clients (C.Qn) is set
to ‘exclusive’; this disallows any communication specified
by the QoS app graph. And it is clear that DNS servers do
not overlap with the client devices as there is no mapping
between the two labels (omitted in the figure).
7.2
8.
SYSTEM EVALUATION
For our experiments we used three data sets
D1: the synthetic running example from §4 and §5.
D2: the large enterprise dataset as described in §7.2.
D3: D2 with randomly added function boxes.
Our primary results show that due to eager composition,
the runtime overhead of PGA is minimal even for very large
composed graphs. PGA is practical and scalable, being able
to analyze and compose thousands of policies producing
nearly a million edges in under 600s when considering only
ACLs and 800s in most cases for policy graphs with both
ACLs and service chains. As described earlier, the composed graphs are correct by design. Additionally, for D1, we
verify the correctness manually and for D2 and D3, we use
Veriflow for indirect validation with reachability analysis.
The PGA prototype system is implemented in Python and
currently runs as a single threaded program. For our evaluations, PGA is deployed on a server with 2x8 Intel Xeon
2.6 GHz cores with 132 GB memory running Linux kernel 3.13.0. We emulate switches and hosts with mininet
2.1.0 and openvswitch 2.0.2 on the server in order to create
a topology and generate packets between hosts in different
EPGs. Since the focus here is not to optimally assign flow
rules to switches, the results described use simple topologies
with one or two switches providing connectivity to the end
hosts. We randomly generate packets between hosts to analyze reachability. A POX controller running on the same
server uses PGA to look-up the policy to be applied upon receiving a pkt_in event. The input graphs are pre-composed
by PGA and the composed graph is stored in memory. The
composed graph from D1 has 8 EPGs and 11 edges. For
D2, the composed graph has nearly 4K unique EPGs and
over 76× the number of edges as the input graphs; in this
case, high edge multiplication occurred in graph normalization because some input graph EPGs were defined by toplevel labels in the label hierarchy.
Runtime Overhead. Fig. 8 shows the additional runtime latency incurred at the controller for the first packet of a flow
by relying on PGA for determining the policy from the composed graphs of D1 (Small) D2 (Large). The input samples (pairs of EPGs) were randomly picked for D1. For D2,
we picked more samples that had a large number of edges
(10-30) between the EPG pair. This is because the prototype evaluates a packet linearly against each edge between
the EPG pair until it finds the matching flow space and so
Large Enterprise ACL
Enterprise dataset: We obtain ACL policies from the
policy management system in a large enterprise network.
These policies are defined for each compartment, which are
EPs that share the same set of ACL rules. The ACLs permit
or deny the communications between the EPs within each
compartment. Policies are written by network admins or
application owners, manually reviewed for correctness and
consistency, and then deployed in over 30+ global sites.
By analyzing the policies for the entire 136 compartments,
we identified over 20K ACL policies that are written for
4916 EPGs. The sizes of EPGs vary, ranging from one IP
address to over 600 non-contiguous subnets (100M IP addresses). We construct label mapping based on the supersub set relationship between EPGs. Note that one EPG has
multiple parents since the EPG may contain non-contiguous
address blocks. We aggregate multiple policies for the same
EPG pair by listing multiple protocol fields and port ranges
for one edge, creating a total of 11,786 edges.
Redundancy and conflict are detected using PGA in this
dataset. A rule is redundant if there exists another rule in the
same compartment that completely covers the former rule’s
space with the same action. We have identified that 7% of
the aggregated policies are redundant. PGA also detects two
39
1
300
0.9
200
0.8
100
0.7
0
0
1
2
3
4
5
0.6
400
1
300
0.9
200
0.8
100
0.7
0
# of EPGs (x1000) in input graphs
1.1
0
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4
6
8
10
12
0.6
# of edges (x1000) in input graphs
1.2
composition time
Memory
500
1.1
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0
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1.1
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composition time
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500
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1.2
composition time
Memory
Memory (GB)
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600
0.6
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0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
Small
Large
CDF
Time (ms)
Figure 10: Scalability of PGA with D2.
1
0.8
0.6
0.4
0.2
0
0
tion box interactions is required to determine correct function box orderings in the composed graph. Since D2 only
contains ACL policies, we simulated a policy dataset with
function boxes (D3) by first creating a pool of 16 synthetic
function boxes, each having a bounding behavior that compiles to two match-action rules. We then randomly picked
input edges from D2 to which we added a randomly chosen function box from the pool; up to 3500 functions boxes
were added to D2, larger than 1900 middleboxes observed
from very large enterprises (>100K hosts) [37], to yield D3.
Fig. 11 shows the composition time and memory consumption for D3. As expected, composition with service
chain analysis is more expensive than composing only ACL
policies (Fig. 10). Nevertheless, for most cases the composition time is <800 seconds and memory consumption is
<20 GB. These are practical numbers for an engine eagerly
composing such large number of policies. Note that the composed graph from D3 can have millions of function boxes,
replicated from the 3500 functions boxes randomly placed
in the inputs graphs; we cached and reused the results of intermediate chain analysis to prevent redundant computations
and to save memory.
We also individually measured the time for each of the
two steps of our composition algorithm. The normalization
step is quite inexpensive and accounts for only around 1%
of the total composition time. Although the graph union operation is expensive, we note that it is done in a progressive manner. We first compute unions for pairs of smaller
graphs to get larger graphs and then repeat the procedure on
the larger graphs; this algorithm is well suited for a multithreaded implementation (future work).
5 10 15 20 25
# of edges per EPG pair
Figure 8: PGA induced
additional runtime la- Figure 9: CDF of #edges
tency at the controller for per EPG pair in composed
graph of D2.
D1 (Small), D2 (Large).
incurs additional overhead for every edge; by selecting samples with the highest number of edges, we evaluate the worst
case performance of PGA for this dataset.
Overall, the latencies are small for both cases. As expected, D2 results in a higher latency than D1 due to the
higher number of edges.1 Fig. 9 shows the CDF for number
of edges per EPG pair in the composed graph of D2. 95%
of EPG pairs have less than 4 edges between them and 99%
below 11. Even the worst case performance numbers (obtained with >25 edges between EPG pair) have a sub-ms
average latency overhead which is still small and practical
for real time operation. We thus find that due to eager composition, the runtime overhead of policy lookup is negligible
even for very large graphs. Additionally, the lookup time is
independent of the network size, number of endpoints or the
presence of service chains.
Scalability of PGA. Although PGA composes input graphs
eagerly, it still needs to be able to handle very large inputs in
a reasonable amount of time and with practical consumption
of resources. We exercised PGA by varying the input from
D2. We randomly selected different sets of compartments,
composed their input graphs and measured the composition
time as well as memory consumption. Fig. 10 shows the
measures plotted against number of EPGs in input graphs,
edges in input graphs, and edges in the composed graph.
We omit the measures against number of EPGs in composed
graph since it is similar to that for number of EPGs in input
graphs. The largest input (entire D2) which produced nearly
1M edges in the composed graph required less than 1.2 GB
of memory while completing in under 10 minutes.
If input graphs contain service chains, then the cost of
composition is increased because pairwise analysis of func-
9.
RELATED WORK
Abstractions. Providing powerful abstractions for programming network policies has received considerable attention
recently [20, 34, 41, 35, 12, 21, 19, 38, 6, 23, 14, 28].
However, most of these frameworks ([20, 34, 41, 35, 12,
21, 19, 38]) tie policy expression to low-level specifics such
as device IP/MAC addresses or current locations of packets
within the network. This keeps their complexity as well as
the learning curve for users from lowering, especially when
they want to express pure end-to-end communication polices
without concerning the topology or other details of the network. It also makes them non-intuitive to express dynamic
policies that change based on external events like EP status.
1
This may be reduced by using packet classification algorithms that run in logarithmic time [16].
40
30
800
20
400
10
0
0
1
2
3
4
5
0
# of EPGs (x1000) in input graphs
40
1200
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800
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400
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0
0
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4
6
8
10
12
0
# of edges (x1000) in input graphs
50
composition time
Memory
1600
40
1200
30
800
20
400
10
0
0
200
400
600
800
Memory (GB)
1200
1600
2000
Composition time (s)
40
50
composition time
Memory
Memory (GB)
1600
2000
Composition time (s)
50
composition time
Memory
Memory (GB)
Composition time (s)
2000
0
1000
# of edges (x1000) in composed graph
Figure 11: Scalability of PGA with D3 (D2 + function boxes).
require a human oracle [15] or are rigidly pre-determined for
each conflict type, making it hard to automatically handle arbitrary conflicts between diverse policy writers.
In GBP [6], users are required to manually write a composite policy connecting multiple EPG pairs. Its users write
conditions, prioritized rules etc. such that a unique and correct policy will be chosen for any traffic between endpoints
that may have varying statuses (security level, location etc).
In contrast, PGA not only supports automated, eager composition of modular policies, it is also to our knowledge,
the first to explore the model of individual policies independently constraining each other for composition.
Extensions. Specifying dynamic temporal behavior of the
network as a finite state machine has been studied in [28,
42]. Kinetic [28] can coexist with PGA; different SDN control apps programmed in Kinetic can generate access control and service chain policies in PGA graph model, which
the PGA framework can then compose while handling conflicts. I.e., Kinetic controls dynamic label assignments of
endpoints while PGA captures and composes the network
policy associated with each label (EPG).
Refs. [25, 17, 18] have explored ways to effectively model
middleboxes. In comparison, PGA’s modeling is more abstract but has been sufficient for the composition scenarios
we targeted. Our ongoing work is exploring the utility of the
richer modeling capability provided by some of these frameworks for service chain analysis and runtime verification of
service chain policies.
Corybantic, Athens and Statesman [33, 13, 39] propose
solutions to resolve conflicts on underlying network resources
or states between different SDN control modules. NEMO [3]
and ONOS [5] provide APIs to model a virtual network topology (of switches and routers) and to specify requirements on
the topology links and paths. These systems deal with problems that are orthogonal to PGA which handles end-to-end
policies that are agnostic to network specifics. PGA can be
extended to incorporate some of these solutions. TAG [30]
provides a graph abstraction capturing only bandwidth requirements across application components; extending PGA
to incorporate TAG is our future work.
CoVisor [24] composes OpenFlow rule tables, separately
compiled from individual SDN controllers, and efficiently
updates the composed rule table for a change in an input table. However, composing high-level SDN policies in prioritized OpenFlow rules is inherently inefficient; e.g., adding/re-
Instead, policy intents can be suitably captured using logical labels similar to PGA. Logical labels have been advocated or supported in both networking [6, 23, 14] and nonnetworking [40, 9, 1] contexts. For example, SELinux [9]
assigns arbitrary labels per file in extended attributes and
allows users to define access control policies based on the
label values. Among network policy frameworks, GBP [6]
defines an application-centric policy model that has a notion
of EPGs whose membership is determined by logical labels,
similar to PGA, although it does not model the relationships
between labels or provide a graph model. Flow-based Management Language (FML) [23] is a DATALOG-based query
language that specifies access control and forwarding policies on logical entities (e.g., user names queried from external authentication services) and conditions (wireless vs.
wired). FML has evolved to Nlog [29] for network virtualization and to OpenStack Congress policy language [7] that
expresses policies across multiple cloud services of compute, storage, networking, etc. Despite the expressiveness,
it is a generic language, non-trivial to map to graph models which are better suited for networking policies. Further, these abstractions do not model middlebox behavior
required for service chain analysis and they do not support
automated, eager composition.
Composition. Many frameworks support manual composition of network policies, e.g., [20, 34, 12, 21, 15, 19, 38].
Merlin [38] assumes a hierarchy of users like network admins and tenants and allows tenants to only restrictively refine policies that are explicitly delegated to them by the admin, manually composing both their intents in the process.
It is thus not suitable for composing modular policies that
can arbitrarily constrain each other where conflict resolution
is required. Some frameworks [20, 34, 24, 12, 21] allow
network operators to write modular programs and manually
compose them into one complex program. These are without
conflict resolution support as well. Further, their composition is too coarse-grained and cannot automatically decompose and re-compose complex user policies that often mix
different types of intents – ACLs, service chains etc. – in
each policy. Others have introduced composition operators
to resolve conflicts between independently specified access
control policies [15, 19] and bandwidth requirements [19].
They map the set of all input policies onto the leaf nodes of a
single hierarchical tree and assign a composition operator to
each intermediate node of the tree. Such assignments either
41
moving an SDN app/controller would require re-computation
of the entire composed table in [24]. In PGA, incremental update of a composed graph per input graph join/leave/
change events is an easy extension. In addition, PGA can
use CoVisor to proactively compile OpenFlow rules for the
composed graph and to incrementally update the rule table.
10.
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CONCLUSION
PGA provides an intuitive graph abstraction to express
and compose policies. Users (or a policy authoring tool)
can simply walk through a composed graph to verify connectivity and service chain requirements. PGA expresses
policies and resolves conflicts while minimizing operator interventions. To our knowledge, PGA is the first to model the
behavior of closed middleboxes and ensure their correct behavior in a service chain. Automatically combining multiple
service chains is another unique feature of PGA. As future
work, we plan to enhance PGA in a number of ways: e.g.,
when a large number of endpoints change their labels at the
same time, the PGA runtime should be able to update the
network in a scalable, responsive and consistent way. Supporting HW/VM middleboxes, verifying their runtime behaviors and chaining them in more flexible ways (e.g., asymmetric forward/reverse) is additional future work.
11.
ACKNOWLEDGEMENTS
We greatly appreciate Nate Foster (our shepherd) and the
anonymous reviewers for their insightful feedback. This
work is supported in part by National Science Foundation
(grants CNS-1302041, CNS-1330308 and CNS-1345249)
and the Wisconsin Institute on Software-Defined Datacenters of Madison.
12.
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42
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