Languages for Software-Defined Networks

Languages for Software-Defined Networks
Languages for Software-Defined Networks
Nate Foster∗ , Michael J. Freedman† , Arjun Guha∗ , Rob Harrison‡ ,
Naga Praveen Katta† , Christopher Monsanto† , Joshua Reich† , Mark Reitblatt∗ ,
Jennifer Rexford† , Cole Schlesinger† , Alec Story∗ , and David Walker†
∗ Cornell University † Princeton University ‡ U.S. Military Academy
Abstract—Modern computer networks perform a bewildering
array of tasks, from routing and traffic monitoring, to access
control and server load balancing. Yet, managing these networks
is unnecessarily complicated and error-prone, due to a heterogeneous mix of devices (e.g., routers, switches, firewalls, and
middleboxes) with closed and proprietary configuration interfaces. Software-Defined Networks (SDN) are poised to change
this by offering a clean and open interface between networking
devices and the software that controls them. In particular, many
commercial switches support the OpenFlow protocol, and a
number of campus, data-center, and backbone networks have
deployed the new technology. Yet, while SDN makes it possible to
program the network, it does not make it easy. Today’s OpenFlow
controllers offer low-level APIs that mimic the underlying switch
hardware. To reach SDN’s full potential, we need to identify
the right higher-level abstractions for creating (and composing)
applications. In the Frenetic project, we are designing simple and
intuitive abstractions for programming the three main stages
of network management: (i) monitoring network traffic, (ii)
specifying and composing packet-forwarding policies, and (iii)
updating policies in a consistent way. Overall, these abstractions
make it dramatically easier for programmers to write and reason
about SDN applications.
Traditional networks are built out of special-purpose devices
running distributed protocols that provide functionality such
as topology discovery, routing, traffic monitoring, and access
control. These devices have a tightly-integrated control and
data plane, and network operators must separately configure
every protocol on each individual device. Recent years, however, have seen growing interest in software-defined networks
(SDNs), in which a logically-centralized controller manages
the packet-processing functionality of a distributed collection
of switches. SDNs make it possible for programmers to
control the behavior of the network directly, by configuring
the packet-forwarding rules installed on each switch [1]. Note
that although the programmer has the illusion of centralized
control, the controller is often replicated and distributed for
scalability and fault tolerance [2].
SDNs can both simplify existing applications and also serve
as a platform for developing new ones. For example, to
implement shortest-path routing, the controller can calculate
the forwarding rules for each switch by running Dijkstra’s
algorithm on the graph of the network topology instead of
using a more complicated distributed protocol [3]. To conserve
energy, the controller can selectively shut down links or even
whole switches after directing traffic along other paths [4]. To
enforce fine-grained access control policies, the controller can
consult an external authentication server and install custom
forwarding paths for each user [5]. To balance the load
between back-end servers in a data center, the controller can
split flows over several server replicas and migrate flows to
new paths in response to congestion [6], [7].
But although SDNs makes it possible to program the network, it does not make it easy. Protocols such as OpenFlow [1]
expose an interface that closely matches the features of the
underlying switch hardware. Controllers such as NOX [8],
Beacon [9], and Floodlight [10] support the same low-level
interface, which forces applications to be implemented using
programs that manipulate the state of individual devices. Supporting multiple tasks at the same time—such as routing and
access control—is extremely difficult, since the application
must ultimately install a single set of rules on the underlying
switches. In addition, a network is a distributed system, and all
of the usual complications arise—in particular, control messages sent to switches are processed asynchronously. Overall,
writing applications for today’s SDN controller platforms is a
tedious exercise in low-level distributed programming.
The goal of the Frenetic project is to raise the level of
abstraction for programming SDNs. To replace the low-level
imperative interfaces available today, Frenetic offers a suite of
declarative abstractions for querying network state, defining
forwarding policies, and updating policies in a consistent way.
These constructs are designed to be modular so that individual policies can be written in isolation and later composed
with other components to create sophisticated policies. This
is made possible in part by the design of the constructs
themselves, and in part by the underlying run-time system,
which implements them by compiling them down to lowlevel OpenFlow forwarding rules. Our emphasis on modularity
and composition—the key principles behind effective design
of complicated software systems—is the key feature that
distinguishes Frenetic from other SDN controllers.
Our initial work on Frenetic rethinks how to support the
three main pieces of the “control loop” for running a network:
• Querying network state: Frenetic offers a high-level query
language for subscribing to streams of information about
network state, including traffic statistics and topology
changes. The run-time system handles the details of
polling switch counters, aggregating statistics, and responding to events.
• Expressing policies: Frenetic offers a high-level policy
language that makes it easy for programs to specify
the packet-forwarding behavior of the network. Different
modules may be responsible for (say) topology discovery,
routing, load balancing, and access control. Individual
A. Query Language Design Considerations
High-level predicates: Many monitor applications classify
traffic using packet headers. For example, suppose the programmer wants to tally all web server traffic excluding the host
with IP source address To represent the negation
in a switch flow table, we would need to use two rules—
a high-priority rule matching packets from with
TCP source port 80, and a lower-priority rule matching all
remaining traffic with TCP source port 80. Frenetic allows
programmers to specify predicates like “srcip!=
& srcport=80,” leaving the details of how to construct
and optimize switch-level rules to the run-time system. In
general, Frenetic programmers can specify sets of packets from
primitive predicates over standard OpenFlow headers (like
srcip, dstip, vlan, etc.), their location in the network
(switch and ingress port), and ordinary set-theoretic operators
(union, intersection, difference, complement, etc.).
Dynamic unfolding: Switches have limited space for rules,
which can make it difficult to install all the necessary rules
in advance. For example, suppose a programmer wants to
collect a histogram of traffic by source IP address. Rather
than installing rules for each of the 232 possible IP addresses, a
typical SDN application would install rules reactively as traffic
arrives from different sources. In Frenetic, the programmer can
register a query that uses operators such as “Select(bytes)”
and “GroupBy([srcip]),” and the run-time system dynamically
generates the appropriate rules. To do this, it initially sends all
traffic to the controller. Upon receiving the first packet from
a specific source IP address, the run-time system generates
and installs a rule matching future traffic from that host. After
receiving a packet from another source IP address, the runtime system generates and installs a second rule. These rules
process future traffic from those hosts using efficient hardware,
and the counters maintain the necessary information needed to
implement the query.
Limiting traffic: A common idiom in SDN programming
is to send the first packet of a traffic aggregate to the
controller, and reactively install rules for handling future
such packets. However, since the controller and the switch
do not communicate instantaneously, multiple packets may
arrive at the controller before the rules are installed. Rather
than force programmers to handle these unexpected packets,
Frenetic allows a query to specify the number of packets it
wants to see using operators such as “Limit(1)”. The run-time
system automatically handles any extra packets by applying
the forwarding policy registered by the application.
Polling and combining statistics: Many programs need
to receive periodic information about traffic statistics. Rather
than requiring the programs to manually poll switch-level
counters, and register callbacks to query those counters again
in the future, Frenetic queries can specify a query interval
using operators such as “Every(60).” The run-time system
automatically queries the traffic counters periodically and
aggregates the resulting values, returning a stream of results
to the application.
Frenetic’s query language allows programmers to control
the information they receive using a collection of high-level
operators for classifying, filtering, transforming, and aggregating the stream of packets traversing the network.
B. Example Frenetic Queries
To illustrate how Frenetic supports querying of network
state, we present two simple examples. These example use
modules register these policies with the run-time system,
which automatically composes, compiles, and optimizes
them with programmer-specified queries.
• Reconfiguring the network: Frenetic offers abstractions
for updating the global configuration of the network.
These abstractions allow a programmer to reconfigure
the network without having to manually install and uninstall packet-forwarding rules on individual switches—a
tedious and error-prone process. The run-time system
ensures that during an update, all packets (or flows) are
processed with the old policy or the new policy, and
never a mixture of the two. This guarantee ensures that
important invariants such as loop freedom, connectivity,
and access control are never violated during periods of
transition between policies.
Together, these abstractions enable programmers to focus
on high-level network management goals, instead of details
related to handling low-level rules and events. The following
sections describe each of these components in more detail.
Readers interested in using the system may download our
compiler, which is available online [11]. The Frenetic web site
also contains technical papers and reports which discuss the
language design, compiler infrastructure, update mechanisms,
and other technology in further detail [12].
Many SDN programs react to changes in network state,
such as topology changes, link failures, traffic load, or the
arrival of particular packets at specific switches. To monitor
traffic, the controller can poll the counters associated with the
rules installed on switches, which maintain a counter for every
forwarding rule that keeps track of the number of packets and
bytes processed using that rule. However, programmers must
ensure that the rules installed on switches are fine-grained
enough to collect the desired traffic statistics. For example, to
monitor the total amount of web traffic, the programmer must
install rules that process (and count) traffic involving TCP port
80 separately from all other traffic. Managing these rules is
tedious and anti-modular—rules installed by one module may
be too coarse to be executed side-by-side with rules installed
by a different module.
Frenetic’s query language allows programmers to express
what they want to monitor, leaving the details of how to
actually collect the necessary traffic statistics to the run-time
system. This not only makes it easy for programmers to
specify a single query, if that is all they need, but also allows
them to write many different queries without worrying about
their interactions—the run-time system selects rules at the
appropriate granularity to satisfy all of the queries registered
with the system.
a syntax that closely resembles SQL, including constructs for
selecting, filtering, splitting, and aggregating the streams of
packets flowing through the network.
MAC learning: An Ethernet switch performs MAC learning
to identify what interface to use to reach a host. MAC learning
can be expressed in Frenetic as follows:
Select(packets) *
GroupBy([srcmac]) *
SplitWhen([inport]) *
The Select(packets) clause states that the program
needs to receive actual packets (as opposed to traffic statistics).
The GroupBy([srcmac]) subdivides the set of queried
packets into subsets based on the srcmac header field,
resulting in one subset for all packets with the same source
MAC address. The SplitWhen([inport]) clause, like
a GroupBy, subdivides the set of selected packets into
subsets; however, whereas GroupBy produces one subset
of all packets with particular values for the given header
fields, SplitWhen([inport]) does not—it generates a
new subset each time the header values change (e.g., when
the inport changes). Together, the GroupBy([srcmac])
and SplitWhen([inport]) clauses state that the program
wants to receive a packet only when a source MAC address
appears at a new ingress port on the switch. The Limit(1)
clause says that the program only wants to receive the first such
packet, rather than all packets from that source MAC address
at the new input port. The result is a stream of packets that
the program can use to update a table mapping each MAC
address to the appropriate ingress port.
Traffic histogram: As another example, consider the following query, which measures the traffic volume by destination
IP address on a particular link:
Select(bytes) *
Where(inport=2 & srcport=80) *
GroupBy([srcip]) *
The Select(bytes) clause states that the program wants
to receive the total number of bytes of traffic, rather
than the packets themselves. The Where(inport=2 &
srcport=80) clause restricts the query to Web traffic arriving on ingress port 2 on the switch. The
GroupBy([srcip]) states that the program wants to aggregate traffic based on the source IP address. The Every(60)
says that the traffic counts should be collected every 60
seconds. The result is a stream of traffic statistics that the
program can use as input to any other control logic.
Frenetic’s query language is expressive, but also gives the
programmer a reasonable sense of the cost of evaluating a
query. For example, the MAC-learning query sends a packet
to the controller whenever a host appears at a new input port,
whereas the traffic-measurement query sends a packet to the
controller to generate a forwarding rule, and subsequently
polls the counter associated with that rule once per minute.
The run-time system takes care of the details of installing
rules in the switches. For the MAC-learning query, the run-
time system initially directs all packets to the controller, but
gradually installs packet-forwarding rules (as specified by the
controller program) after the first packet of a new MAC
address has been seen. If additional packets arrive at the
controller—say, because of delays in installing these rules—
the run-time system handles these packets automatically, rather
than exposing them to the controller program. Similarly, for
the traffic-monitoring query, the run-time system installs rules
that match on destination IP addresses, to ensure the switches
maintain separate counters for different IP addresses.
Most networks perform multiple tasks, such as routing,
monitoring, and access control. Ideally, programmers would
be able to implement these tasks independently, using separate
modules. But the programming interfaces available today make
this difficult, since packet-handling rules installed by one
module often interfere with overlapping rules installed by
another module. Frenetic’s policy language has a number of
features that are designed to make it easy to construct and
combine policies in a modular way.
A. Creating Modular Programs
As an example, consider a simple program that combines
repeater functionality (i.e., code for forwarding packets that
arrive in one interface out the other) with web-traffic monitoring functionality. Abstractly, these tasks are completely
orthogonal, so we should be able to implement them as
independent modules and combine them into a program that
provides both pieces of functionality.
Suppose that the repeater is implemented by a module that
generates rules that match all traffic coming in on ingress
port 1 and that forward it to output 2, and vice versa. The
monitoring component is implemented by a rule that matches
all traffic with TCP source port 80 arriving on ingress port
2. The monitoring component does not care how packets
matching the rule are forwarded—it only needs to access the
counters associated with the rule.
Now, consider a Python program, roughly following the
NOX controller API, that combines these two components.
def switch_join(s):
pat1 = {inport:1}
pat2web = {inport:2, srcport:80}
pat2 = {inport:2}
install(s, pat1, DEFAULT, [fwd(2)])
install(s, pat2web, HIGH, [fwd(1)])
install(s, pat2, DEFAULT, [fwd(1)])
query_stats(s, pat2web)
def stats_in(s, xid, pat, pkts, bytes):
print bytes
query_stats(s, pat)
When a switch joins the network, the controller invokes the
switch join event handler, which installs three rules to
handle (i) traffic arriving on ingress port 1, (ii) web traffic
arriving on ingress port 2, and (iii) non-web traffic arriving
on ingress port 2. The second rule has HIGH priority, so the
web traffic matches this rule rather than the lower-priority third
rule; this ensures the switch correctly collects traffic statistics
for web traffic. Note that, in isolation, the pat2 pattern would
match all incoming traffic, including the web traffic. But the
presence of the higher-priority pat2web rule ensures that
only non-web traffic matches the pat2 pattern. Having nonweb traffic “fall through” to this lower-priority rule is a much
more concise way to represent the forwarding policy than (say)
having separate rules for every possible TCP source port.
The call to query stats generates a request for the
counters associated with the pat2web rule. When the controller receives the reply, it invokes the stats in handler.
This function prints the statistics polled on the previous
iteration of the loop, waits 30 seconds, and then issues a
request to the switch for statistics matching the same rule.
The interesting aspect of this program is that it is a “mashup” of the logic for the repeater and the monitor. The first
and third rules come from the repeater program, and the
second rule and the stats in handler from the monitoring
program. The second rule needs HIGH priority (to ensure
the correct functioning of the monitoring logic) and actions
[fwd(1)] (to ensure the correct functioning of the repeater
logic). Ideally we would be able to tease apart the code for
the monitor and repeater and place each in a separate module.
This would allow the monitor to be reused with many different forwarding policies, and the repeater to be reused with
many different monitoring queries. Unfortunately, doing this
separation is impossible in NOX and other similar controller
platforms—both the monitoring specification and forwarding
specification are too tightly-coupled to the OpenFlow interface
for manipulating low-level rules to be separated or abstracted
out. Consequently, even for this simple example, the logic
becomes quite complicated, as the programmer must think
about multiple tasks (and their interactions) at the same time.
In more sophisticated examples, the programmer would need
to use multiple levels of priorities, and perform even more
complex combinations of policies.
By contrast, Frenetic’s policy language makes it easy for
programmers to write and compose independent modules.
Here is the same program implemented in Frenetic:
def repeater():
rules=[Rule(inport:1, [fwd(2)]),
Rule(inport:2, [fwd(1)])]
def web monitor():
q = (Select(bytes) *
Where(inport=2 & srcport=80) *
q >> Print()
def main():
In the code above, the repeater function implements the
repeater and only the repeater. The command to register
the forwarding policy generated by this code passes it to the
run-time system for processing. Likewise, the web monitor
function implements the monitor and only the monitor. It
defines a query q and then pipes the results of that query
into a print function (the >> operator pipes a stream generated from one component into another). The main function
assembles these components into a single program. It could
easily swap out the given network monitor for another one,
without touching repeater code, or change the forwarding
logic in the repeater without touching the monitor. Importantly,
the responsibility for installing specific OpenFlow rules that
realize both components simultaneously is delegated to the
run-time system. For this example, the run-time system would
generate the same rules as the manually-constructed rules in
the switch join function listed above.
Combining the repeater and the monitor is an example of
parallel composition, where conceptually both modules act
on the same stream of packets. The repeater module applies
a forwarding policy (i.e., “writes” network state) and the
monitoring module queries the traffic (i.e., “reads” network
state). If multiple modules apply a forwarding policy, parallel
composition essentially performs a “union” of the actions
in the two policies. If one module forwards a packet out
port 1, and the other forwards the same packet out port
2, parallel composition would result in an application that
forwards the packet out both ports. While desirable in some
settings, parallel composition is not the only way to combine
modules together. In particular, for policies that express negative constraints, such as a firewall, we definitely do not want to
take the “union” of all policies—the whole point of a firewall
is to drop packets, no matter what other policies are installed
in the system! To handle such negative constraints, Frenetic’s
policy language also includes a restriction operator that allows
a programmer to filter policies using packet predicates. Recently we have also added support for sequential composition
where, conceptually, one module acts on the packets output by
the other module. For example, if the first module modifies
the packet (e.g., a load-balancing module that modifies the
destination IP address to identify a specific back-end server),
the second module matches on the modified header fields (e.g.,
a routing module that forwards packets toward that server
based on the new destination IP address).
B. Efficient Run-Time System
The run-time system ensures that each module runs correctly, independent of the other modules. To understand how
the run-time system performs composition, consider our initial Frenetic run-time system [13], which had a reactive,
microflow-based1 strategy for installing rules on switches. At
1 A microflow rule is a rule in which all header fields of a packet are
specified exactly. Microflow rules are the easiest to compile because every
pair of microflow rules are either identical or disjoint. In contrast, wildcard
rules match large groups of packets, such as all packets with source IP address
matching the prefix 1.*. When one uses wildcards over multiple packet
headers, rules may partially overlap, meaning one must manage rule priorities
and rule shadowing carefully, and the compilation problem becomes more
the start of execution, the flow table of each switch is empty,
so every packet is sent to the controller and passed to the
packet in handler. Upon receiving a packet, the run-time
system iterates through all of the queries, and then traverses
all of the registered forwarding policies to collect a list of
actions for that switch. It then processes the packet in one of
two ways: (i) if no queries depend on receiving future packets
of this sort, it installs a forwarding rule that applies the actions
to packets with the same header fields or (ii) if some queries do
depend on receiving future packets of this sort, it applies the
actions to the current packet, but does not install a rule, since
that would prevent those packets from reaching the controller.
In effect, this strategy dynamically unfolds the policy expressed in the high-level rules into switch-level rules, moving
processing off the controller and onto the switches, without
interfering with any queries. While this reactive, microflow
strategy is relatively simple to understand, sending packets to
the controller is expensive—e.g., the developers of DevoFlow
measured a total round-trip latency of 2.5ms on a HP ProCurve
5406zl switch [14]. Consequently, the current Frenetic runtime system is proactive (generating rules before packets
arrive at the switches) and uses wildcard rules (matching on
larger traffic aggregates) [15]. It uses an intermediate language,
called NetCore, for expressing packet-forwarding policies and
a compiler that proactively generates as many OpenFlow-level
rules for as many switches as possible, but where impossible
(or intractable), uses an algorithm called reactive specialization to dynamically unfold switch-level rules on demand.
There are three main situations where the NetCore compiler
cannot proactively generate all the rules it needs to implement
a policy: (i) the policy involves a query that groups by IP
address (or other header field)—such a query would require
one rule for each of the 232 IP addresses if generated ahead
of time, but only one rule for each IP address that actually
appears in the network if unfolded dynamically; (ii) the policy
involves a function that cannot be implemented natively or
efficiently on the switch hardware, and (iii) the switch does
not have space for additional wildcard rules. In these cases, the
compiler can fall back to the microflow-based strategy, and use
the plentiful exact-match rules available on switches. In other
cases, the NetCore compiler generates policies completely
proactivity and no packets are diverted to the controller.
As an example of the second situation, consider a predicate
that matches all destination IP addresses with a first octet of
90, a third octet of 70, and a fourth octet of 60. Most of
today’s OpenFlow switches do not support arbitrary wildcards
in IP addresses, and instead match only on an address prefix
like 90.80.*.*. To handle arbitrary predicates, the runtime system generates an overapproximation that matches a
superset of the traffic specified by the original predicate (e.g.,
90.*.*.*), and installs rules that direct such traffic to the
controller for further processing. For example, a packet with
destination address would go to the controller,
causing the run-time system to reactively specialize the list of
rules in the switch by installing a high-priority rule matching
destination address This ensures that the
switch handles all future packets with this destination address,
while still directing packets with other destination IP addresses
in 90.*.*.* to the controller.
The run-time system’s ability to generate overapproximations, and reactively refine the rules based on the actual
traffic in the network, allows Frenetic to support policies with
arbitrary functions that the switches cannot implement. The
run-time system can also customize rules to the capabilities
of the switch (e.g., whether the switch supports prefix patterns vs. arbitrary wildcards). This makes NetCore programs
more portable. For the predicate above, the run-time system
would generate a single rule when the underlying switch can
support arbitrary wildcard patterns, or the overapproximation
90.*.*.* with reactive specialization if the switch can only
support prefix patterns, or the overapproximation *.*.*.* if
the switch could not even support prefixes.
Currently, the Frenetic run-time system supports OpenFlow
1.0 (i.e., a single table in each switch). Composing multiple
modules can easily lead to a multiplicative “blow-up” in the
number of rules, particularly if the modules act on different
packet-header fields (e.g., a monitoring module that matches
on TCP port numbers combined with a forwarding module that
matches on destination IP addresses). This scalability problem
is inherent to more sophisticated programs that combine
multiple network-management tasks, whether the composition
is performed “manually” by the programmer or automatically
by a run-time system like ours. If anything, a smart run-time
system should do a better job in applying optimizations that
minimize the number of rules required to represent a policy.
Ultimately, the “rule blow-up” problem is best addressed by
having more sophisticated data-plane architectures, such as
a pipeline of tables. The newer versions of the OpenFlow
standard provide an interface to the tables available in modern
switch hardware. In our future work, we plan to extend our
run-time system to capitalize on these tables to represent
sophisticated policies in a more compact fashion.
Programs often need to transition from one policy to
another—e.g., due to topology changes, changes in network
load or application behavior, planned maintenance, or unexpected failures. From the perspective of the programmer, it
would be ideal if such transitions could be initiated via a
single command that simply declares the new, global network
configuration desired. Moreover, to avoid anomalies such as
transient outages, forwarding loops, and security breaches, every transition must be implemented gracefully: all applicationspecific connectivity invariants should be preserved during
migration from old to new policy.
We have designed high-level network update operations
that implement configuration changes while guaranteeing that
traffic will be processed consistently during the transition [16],
[17]. The semantics of these update operations provides useful
guarantees about network behavior during transitions, and yet
are relaxed enough to admit practical implementations.
A. Per-Packet Consistent Updates
The primary update abstraction supported in Frenetic is a
per-packet consistent update. A per-packet consistent update
guarantees that every packet flowing through the network is
processed with exactly one forwarding policy. For example,
if a per-packet consistent update transitions the network from
policy A to policy B, it guarantees that every packet traversing
the network is processed using the rules from A on all switches
or the rules from B on all switches, but never a mixture
of the two. A crucial consequence of this design is that if
both A and B satisfy a trace property—i.e., a property of
the paths that packets take through the network—then all
packets traversing the network either before, during, or after
the transition will be guaranteed to obey that property. For
example, if both A and B have no loops then no packet will
encounter a loop. If both A and B filter packets from source
IP address then all such packets will be dropped.
Generally speaking, trace properties encompass access control
and connectivity properties, but do not encompass properties
that concern the relationship between multiple packets, such
as in-order delivery or congestion.
The semantics of per-packet consistent update helps developers write reliable dynamic network applications because
such developers can ensure trace properties persist as network
policy evolves—before any change in policy the developer
need merely check the trace property holds of that next policy
to be installed. This insight also makes it possible to build
verification tools that automatically check trace properties as
configurations evolve. For example, we have developed a tool
that allows programmers to specify properties of networks
such as loop freedom or access control using logical formulas.
Using an off-the-shelf verification tool, we can check these
properties against static NetCore policies. To verify dynamic
policies, defined as a stream of static NetCore policies, we
simply verify each individual policy in the stream and use perpacket consistent updates to manage the transition from one
policy to the next. The per-packet consistent update ensures
there are no unusual transient states that violate the properties
of interest.
To implement per-packet consistency, we use a mechanism
called two-phase update that stamps packets with a version at
the ingress to the network and tests for the version number
at all internal ports in the network. This mechanism can be
implemented in OpenFlow using a header field to encode
version numbers (e.g., VLAN tags or MPLS labels). To update
to a new configuration, the controller first pre-processes the
rules in the new configuration, adding an action to stamp
packets at the ingress and test for the next version number
elsewhere. Next, it installs the rules for internal ports, leaving the rules for the old configuration (whose rules match
the previous version number) in place. At this point, every
(internal) switch can process packets with either the old or
new policy, depending on the version number on the packet.
The controller then starts updating the rules for ports at
network ingresses, replacing their old rules with new rules
that stamp incoming packets with the new version number.
Because the ingress switches cannot all be updated atomically,
some packets entering the network are processed with the old
policy and some packets are processed with the new policy for
a time, but any individual packet is handled by a single policy.
Finally, once all packets following the “old” policy have left
the network, the controller deletes the old configuration rules
from all switches, completing the update.
This two-phase update mechanism works in any situation,
but it is not always necessary. In practice, many optimizations are possible when the new policy and old policy are
similar [17]. If the policy changes affect only a portion of
the network topology, or a portion of the traffic, the run-time
system can perform the update on a subset of the switches
and rules. If the new policy is a simple extension (e.g., adding
policy for handling some portion of traffic) or retraction (e.g.,
removing policy for handling some portion of traffic), the
updates become even simpler. Our prototype implementation
applies these optimizations, often resulting in much more
efficient mechanisms.
B. Per-Flow Consistency
Per-packet consistency, while simple and powerful, is not
always enough. Some applications require that streams of
related packets be handled consistently. For example, a server
load-balancer needs all packets from the same TCP session to
reach the same server replica, to avoid breaking connections.
A per-flow consistent update ensures that streams of related
packets are processed with the same policy—i.e., all packets in
the same flow are handled by the same configuration. Formally,
a per-flow update preserves all trace properties, just like perpacket consistency. In addition, it preserves properties that can
be expressed in terms of the paths traversed by sets of packets
belonging to the same flow.
Implementing per-flow consistent updates is more complicated than per-packet consistency because the system must
identify packets that belong to active flows. A simple mechanism can be obtained by combining versioning with rule
timeouts [7]. The run-time system can pre-install the new
configuration on internal switches, leaving the old version in
place, as in per-packet consistency. Then, on ingress switches,
the controller sets soft timeouts on the rules for the old configuration and installs the new configuration at lower priority.
When all flows matching a rule complete, the rule expires and
the rules for the new configuration take effect.
Note that if several flows match a rule, the rule may be artificially kept alive even though the “old” flows have completed—
if the rules are too coarse, then they may never die! To ensure
rules expire in a timely fashion, the controller can refine the old
rules to cover a progressively smaller portion of the flow space.
However, “finer” rules require more rules, a potentially scarce
commodity. Managing the rules and dynamically refining them
over time can be a complex bookkeeping task, especially
if the network undergoes a subsequent configuration change
before the previous one completes. However, this task can be
implemented and optimized once in a run-time system, and
leveraged over and over again in different applications.
The Frenetic language offers programmers a collection
of powerful abstractions for writing controller programs for
software-defined networks. A compiler and run-time system
implements these abstractions and ensures that programs written against them execute efficiently. Our work focuses on
the three stages of managing a network—monitoring network
state, computing new policies, and reconfiguring the network.
Yet, these abstractions are just the beginning. We are currently
exploring further ways to raise the level of abstraction for programming the network, including techniques for “slicing” the
network [18] to provide isolation between multiple programs
controlling different portions of the traffic, for monitoring and
managing end hosts, for handling failures, and for virtualizing
topologies. On the latter topic, we plan to support program
modules that operate on different “views” of the network (e.g.,
a load balancer that sees the network as a single switch,
combined with routing that considers the full details of the
underlying switches and links). We believe that these and other
abstractions will continue to lower the barrier for creating new
and exciting applications on software defined networks.
Acknowledgments: Our work is supported in part by
ONR grants N00014-09-1-0770 and N00014-09-1-0652, NSF
grants CNS-1111698 and CNS-1111520, TRUST, and gifts
from Dell, Intel, and Google. Any opinions, findings, and recommendations are those of the authors and do not necessarily
reflect the views of the ONR or NSF.
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