Ravana: Controller Fault-Tolerance in Software-Defined Networking

Ravana: Controller Fault-Tolerance in Software-Defined Networking
Ravana: Controller Fault-Tolerance in
Software-Defined Networking
Naga Katta, Haoyu Zhang, Michael Freedman, Jennifer Rexford
Princeton University
{nkatta, haoyuz, mfreed, jrex}@cs.princeton.edu
Despite the conceptual simplicity of centralized control, a single controller easily becomes a single point of failure, leading to
service disruptions or incorrect packet processing [1, 2]. In this
paper, we study SDN fault-tolerance under crash (fail-stop) failures. Ideally, a fault-tolerant SDN should behave the same way as
a fault-free SDN from the viewpoint of controller applications and
end-hosts. This ensures that controller failures do not adversely affect the network administrator’s goals or the end users. Further, the
right abstractions and protocols should free controller applications
from the burden of handling controller crashes.
Software-defined networking (SDN) offers greater flexibility than
traditional distributed architectures, at the risk of the controller being a single point-of-failure. Unfortunately, existing fault-tolerance
techniques, such as replicated state machine, are insufficient to ensure correct network behavior under controller failures. The challenge is that, in addition to the application state of the controllers,
the switches maintain hard state that must be handled consistently.
Thus, it is necessary to incorporate switch state into the system
model to correctly offer a “logically centralized” controller.
We introduce Ravana, a fault-tolerant SDN controller platform
that processes the control messages transactionally and exactly once
(at both the controllers and the switches). Ravana maintains these
guarantees in the face of both controller and switch crashes. The
key insight in Ravana is that replicated state machines can be extended with lightweight switch-side mechanisms to guarantee correctness, without involving the switches in an elaborate consensus
protocol. Our prototype implementation of Ravana enables unmodified controller applications to execute in a fault-tolerant fashion.
Experiments show that Ravana achieves high throughput with reasonable overhead, compared to a single controller, with a failover
time under 100ms.
It may be tempting to simply apply established techniques from
the distributed systems literature. For example, multiple controllers
could utilize a distributed storage system to replicate durable state
(e.g., either via protocols like two-phase commit or simple primary/backup methods with journaling and rollback), as done by
Onix [3] and ONOS [4]. Or, they could model each controller as a
replicated state machine (RSM) and instead consistently replicate
the set of inputs to each controller. Provided each replicated controller executes these inputs deterministically and in an identical
order, their internal state would remain consistent.
But maintaining consistent controller state is only part of the solution. To provide a logically centralized controller, one must also
ensure that the switch state is handled consistently during controller
failures. And the semantics of switch state, as well as the interactions between controllers and switches, is complicated. Broadly
speaking, existing systems do not reason about switch state; they
have not rigorously studied the semantics of processing switch events
and executing switch commands under failures.
In Software-Defined Networking (SDN), a logically centralized controller orchestrates a distributed set of switches to provide higherlevel networking services to end-host applications. The controller
can reconfigure the switches (though commands) to adapt to traffic demands and equipment failures (observed through events). For
example, an SDN controller receives events concerning topology
changes, traffic statistics, and packets requiring special attention,
and it responds with commands that install new forwarding rules
on the switches. Global visibility of network events and direct control over the switch logic enables easy implementation of policies
like globally optimal traffic engineering, load balancing, and security applications on commodity switches.
Yet one cannot simply extend traditional replication techniques
to include the network switches. For example, running a consensus protocol involving the switches for every event would be prohibitively expensive, given the demand for high-speed packet processing in switches. On the other hand, using distributed storage
to replicate controller state alone (for performance reasons) does
not capture the switch state precisely. Therefore, after a controller
crash, the new master may not know where to resume reconfiguring
switch state. Simply reading the switch forwarding state would not
provide enough information about all the commands sent by the old
master (e.g., PacketOuts, StatRequests).
In addition, while the system could roll back the controller state,
the switches cannot easily “roll back” to a safe checkpoint. After
all, what does it mean to rollback a packet that was already sent?
The alternative is for the new master to simply repeat commands,
but these commands are not necessarily idempotent (per §2). Since
an event from one switch can trigger commands to other switches,
simultaneous failure of the master controller and a switch can cause
inconsistency in the rest of the network. We believe that these is-
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Consistent Reliable Storage
Switch Broadcast
Replicated State Machines
Total Event
Table 1: Comparing different solutions for fault-tolerant controllers
sues can lead to erratic behavior in existing SDN platforms.
Ravana. In this paper, we present Ravana, an SDN controller
platform that offers the abstraction of a fault-free centralized controller to control applications. Instead of just keeping the controller
state consistent, we handle the entire event-processing cycle (including event delivery from switches, event processing on controllers, and command execution on switches) as a transaction—
either all or none of the components of this transaction are executed. Ravana ensures that transactions are totally ordered across
replicas and executed exactly once across the entire system. This
enables Ravana to correctly handle switch state, without resorting
to rollbacks or repeated execution of commands.
Figure 1: SDN system model
• We present a prototype of a transparent Ravana runtime and
demonstrate our solution has low overhead.
Ravana adopts replicated state machines for control state replication and adds mechanisms for ensuring the consistency of switch
state. Ravana uses a two-stage replication protocol across the controllers. The master replica decides the total order in which input
events are received in the first stage and then indicates which events
were processed in the second stage. On failover, the new master
resumes transactions for “unprocessed” events from a shared log.
The two stages isolate the effects of a switch failure on the execution of a transaction on other switches—a setting unique to SDN.
Instead of involving all switches in a consensus protocol, Ravana
extends the OpenFlow interface with techniques like explicit acknowledgment, retransmission, and filtering from traditional RPC
protocols to ensure that any event transaction is executed exactly
once on the switches.
While the various techniques we adopt are well known in distributed systems literature, ours is the first system that applies these
techniques comprehensively in the SDN setting to design a correct,
faul-tolerant controller. We also describe the safety and liveness
guarantees provided by the Ravana protocol and argue that it ensures observational indistinguishability (per §4) between an ideal
central controller and a replicated controller platform. Our prototype implementation allows unmodified control applications, written for a single controller, to run in a replicated and fault-tolerant
manner. Our prototype achieves these properties with low overhead
on controller throughput, latency, and failover time.
Controller Failures in SDN
Figure 1 shows the normal execution of an SDN in the absence
of controller failures. Recent versions of OpenFlow [5], the most
widely used control channel protocol between controllers and switches,
have some limited mechanisms for supporting multiple controllers.
In particular, a controller can register with a switch in the role of
master or slave, which defines the types of events and commands
exchanged between the controller and the switch. A switch sends
all events to the master and executes all commands received from
the master, while it sends only a limited set of events (for example,
switch_features) to slaves and does not accept commands
from them.
Combining existing OpenFlow protocols with traditional techniques for replicating controllers does not ensure correct network
behavior, however. This section illustrates the reasons with concrete experiments. The results are summarized in Table 1.
Inconsistent Event Ordering
OpenFlow 1.3 allows switches to connect to multiple controllers. If
we directly use the protocol to have switches broadcast their events
to every controller replica independently, each replica builds application state based on the stream of events it receives. Aside from
the additional overhead this places on switches, controller replicas would have an inconsistent ordering of events from different
switches. This can lead to incorrect packet-processing decisions,
as illustrated in the following example:
Experiment 1: In Figure 2a, consider a controller application
that allocates incoming flow requests to paths in order. There are
two disjoint paths in the network; each has a bandwidth of 2Mbps.
Assume that two flows, with a demand of 2Mbps and 1Mbps respectively, arrive at the controller replicas in different order (due
to network latencies). If the replicas assign paths to flows in the
order they arrive, each replica will end up with 1Mbps free bandwidth but on different paths. Now, consider that the master crashes
and the slave becomes the new master. If a new flow with 1Mbps
arrives, the new master assigns the flow to the path which it thinks
Contributions. Our fault-tolerant controller system makes the following technical contributions:
• We propose a two-phase replication protocol that extends
replicated state machines to handle consistency of external
switch state under controller failures.
• We propose extensions to the OpenFlow interface that are
necessary to handle controller failures like RPC-level ACKs,
retransmission of un-ACKed events and filtering of duplicate
• We precisely define correctness properties of a logically centralized controller and argue that the Ravana protocol provides these guarantees.
2 fwd(2)
1 fwd(2)
(a) Total Event Ordering
(b) Exactly-Once Event Delivery
(c) Exactly-Once Command Execution
Figure 2: Examples demonstrating different correctness properties maintained by Ravana
Switch Broadcast
4 t1 t2
time (s)
(a) Bandwidth Allocation
t2 1.0
time (s)
(b) linkdown Under Failures
# of Packets
Bandwidth (bps)
Bandwidth (bps)
sent from h1
received by h2
received by h3
0 t1 t2
time (s)
(c) Routing Under Repeated Commands
Figure 3: Experiments for the three examples shown in Figure 2. t1 and t2 indicate the time when the old master crashes and when the new
master is elected, respectively. In (c), delivery of commands is slowed down to measure the traffic leakage effect.
Experiment 2: Consider a controller program that runs a shortestpath routing algorithm, as shown in Figure 2b. Assume the master
installed a flow on path p1, and after a while the link between s1
and s2 fails. The incident switches send a linkdown event to the
master. Suppose the master crashes before replicating this event.
If the controller replicas are using a traditional RSM protocol with
unmodified OpenFlow switches, the event is lost and will never be
seen by the slave. Upon becoming the new master, the slave will
have an inconsistent view of the network, and cannot promptly update the switches to reroute packets around the failed link.
has 1Mbps free. But this congests an already fully utilized path, as
the new master’s view of the network diverged from its actual state
(as dictated by the old master).
Figure 3a compares the measured flow bandwidths for the switchbroadcast and Ravana solutions. Ravana keeps consistent state in
controller replicas, and the new master can install the flows in an
optimal manner. Drawing a lesson from this experiment, a faulttolerant control platform should offer the following design goal:
Total Event Ordering: Controller replicas should process events
in the same order and subsequently all controller application instances should reach the same internal state.
Note that while in this specific example, the newly elected master
can try to query the flow state from switches after failure; in general, simply reading switch state is not enough to infer sophisticated
application state. This also defeats the argument for transparency
because the programmer has to explicitly define how application
state is related to switch state under failures. Also, information
about PacketOuts and events lost during failures cannot be inferred
by simply querying switch state.
Figure 3b compares the measured bandwidth for the flow h1 →
h2 with an unmodified OpenFlow switch and with Ravana. With
an unmodified switch, the controller loses the link failure event
which leads to throughput loss, and it is sustained even after the
new master is elected. In contrast, with Ravana, events are reliably
delivered to all replicas even during failures, ensuring that the new
master switches to the alternate path, as shown by the blue curve.
From this experiment, we see that it is important to ensure reliable
event delivery. Similarly, event repetition will also lead to inconsistent network views, which can further result in erroneous network
behaviors. This leads to our second design goal:
Exactly-Once Event Processing: All the events are processed,
and are neither lost nor processed repeatedly.
Unreliable Event Delivery
Two existing approaches can ensure a consistent ordering of events
in replicated controllers: (i) The master can store shared application
state in an external consistent storage system (e.g., as in Onix and
ONOS), or (ii) the controller’s internal state can be kept consistent
via replicated state machine (RSM) protocols. However, the former
approach may fail to persist the controller state when the master
fails during the event processing, and the latter approach may fail
to log an event when the master fails right after receiving it. These
scenarios may cause serious problems.
Repetition of Commands
With traditional RSM or consistent storage approaches, a newly
elected master may send repeated commands to the switches because the old master sent some commands but crashed before telling
the slaves about its progress. As a result, these approaches cannot
guarantee that commands are executed exactly once, leading to serious problems when commands are not idempotent.
At least once events
At most once events
Total event order
Replicated control state
Switch events are not lost
No event is processed more than once
Replicas process events in same order
Replicas build same internal state
At least once commands
At most once commands
Controller commands are not lost
Commands are not executed repeatedly
Buffering and retransmission of switch events
Event IDs and filtering in the log
Master serializes events to a shared log
Two-stage replication and deterministic replay of
event log
RPC acknowledgments from switches
Command IDs and filtering at switches
Table 2: Ravana design goals and mechanisms
Experiment 3: Consider a controller application that installs
rules with overlapping patterns. The rule that a packet matches
depends on the presence or absence of other higher-priority rules.
As shown in Figure 2c, the switch starts with a forwarding table
with two rules that both match on the source address and forward
packets to host h2. Suppose host h1 has address, which
matches the first rule. Now assume that the master sends a set of
three commands to the switch to redirect traffic from the /16 subnet
to h3. After these commands, the rule table becomes the following:
master. In fact, when an old master fails, the new master may not
know whether the commands triggered by past events have been
executed on the switches. The second alternative is that the master might choose to replicate an event only after it is completely
processed (i.e., all commands for the event are executed on the
switches). However, if the original switch and later the master fail
while the master is processing the event, some of the commands
triggered by the event may have been executed on several switches,
but the new master would never see the original event (because of
the failed switch) and would not know about the affected switches.
The situation could be worse if the old master left these switches
in some transitional state before failing. Therefore, it is necessary
to take care of these cases if one were to ensure a consistent switch
state under failures.
In conclusion, the examples show that a correct protocol should
meet all the aforementioned design goals. We further summarize
the desired properties and the corresponding mechanisms to achieve
them in Table 2.
If the master crashes before replicating the information about
commands it already issued, the new master would repeat these
commands. When that happens, the switch first removes the first
rule in the new table. Before the switch executes the second command, traffic sent by h1 can match the rule for
and be forwarded erroneously to h3. If there is no controller failure and the set of commands are executed exactly once, h3 would
never have received traffic from h1; thus, in the failure case, the correctness property is violated. The duration of this erratic behavior
may be large owing to the slow rule-installation times on switches.
Leaking traffic to an unexpected receiver h3 could lead to security
or privacy problems.
Figure 3c shows the traffic received by h2 and h3 when sending
traffic from h1 at a constant rate. When commands are repeated
by the new master, h3 starts receiving packets from h1. No traffic
leakage occurs under Ravana. While missing commands will obviously cause trouble in the network, from this experiment we see
that command repetition can also lead to unexpected behaviors. As
a result, a correct protocol must meet the third design goal:
Exactly-Once Execution of Commands: Any given series of
commands are executed once and only once on the switches.
Ravana Protocol
Ravana Approach: Ravana makes two main contributions. First,
Ravana has a novel two-phase replication protocol that extends
replicated state machines to deal with switch state consistency. Each
phase involves adding event-processing information to a replicated
in-memory log (built using traditional RSM mechanisms like viewstamped replication [6]). The first stage ensures that every received
event is reliably replicated, and the second stage conveys whether
the event-processing transaction has completed. When the master
fails, another replica can use this information to continue processing events where the old master left off. Since events from a switch
can trigger commands to multiple other switches, separating the
two stages (event reception and event completion) ensures that the
failure of a switch along with the master does not corrupt the state
on other switches.
Handling Switch Failures
Second, Ravana extends the existing control channel interface
between controllers and switches (the OpenFlow protocol) with
mechanisms that mitigate missing or repeated control messages
during controller failures. In particular, (i) to ensure that messages are delivered at least once under failures, Ravana uses RPClevel acknowledgments and retransmission mechanisms and (ii) to
guarantee at most once messages, Ravana associates messages with
unique IDs, and performs receive-side filtering.
Unlike traditional client-server models where the server processes
a client request and sends a reply to the same client, the eventprocessing cycle is more complex in the SDN context: when a
switch sends an event, the controller may respond by issuing multiple commands to other switches. As a result, we need additional mechanisms when adapting replication protocols to build
fault-tolerant control platforms.
Suppose that an event generated at a switch is received at the
master controller. Existing fault-tolerant controller platforms take
one of two possible approaches for replication. First, the master
replicates the event to other replicas immediately, leaving the slave
replicas unsure whether the event is completely processed by the
Thus, our protocol adopts well known distributed systems techniques as shown in Table 2 but combines them in a unique way
to maintain consistency of both the controller and switch state under failures. To our knowledge, this enables Ravana to provide the
first fault-tolerant SDN controller platform with concrete correctness properties. Also, our protocol employs novel optimizations to
execute commands belonging to multiple events in parallel to decrease overhead, without compromising correctness. In addition,
Ravana provides a transparent programming platform—unmodified
control applications written for a single controller can be made automatically fault-tolerant without the programmer having to worry
about replica failures, as discussed in Section 6.
Ravana has two main components—(i) a controller runtime for
each controller replica and (ii) a switch runtime for each switch.
These components together make sure that the SDN is fault-tolerant
if at most f of the 2 f + 1 controller replicas crash. This is a direct
result of the fact that each phase of the controller replication protocol in turn uses Viewstamped Replication [6]. Note that we only
handle crash-stop failures of controller replicas and do not focus on
recovery of failed nodes. Similarly we assume that when a failed
switch recovers, it starts afresh on a clean slate and is analogous
to a new switch joining the network. In this section, we describe
the steps for processing events in our protocol, and further discuss
how the two runtime components function together to achieve our
design goals.
event ack
cmd ack
5 event log
2 4 6 7
Figure 4: Steps for processing a packet in Ravana.
these commands to switches; that is, the slaves merely simulate the
processing of events to update the internal application state.
When the master controller fails, a standby slave controller will
replace it following these steps:
Protocol Overview
To illustrate the operation of a protocol, we present an example of
handling a specific event—a packet-in event. A packet arriving at a
switch is processed in several steps, as shown in Figure 4. First, we
discuss the handling of packets during normal execution without
controller failures:
1. A leader election component running on all the slaves elects
one of them to be the new master.
2. The new master finishes processing any logged events that
have their event-processed messages logged. These events
are processed in slave mode to bring its application state upto-date without sending any commands.
1. A switch receives a packet and after processing the packet, it
may direct the packet to other switches.
2. If processing the packet triggers an event, the switch runtime
buffers the event temporarily, and sends a copy to the master
controller runtime.
3. The new master sends role request messages to register with
the switches in the role of the new master. All switches send
a role response message as acknowledgment and then begin
sending previously buffered events to the new master.
3. The master runtime stores the event in a replicated in-memory
log that imposes a total order on the logged events. The slave
runtimes do not yet release the event to their application instances for processing.
4. The new master starts to receive events from the switches,
and processes events (including events logged by the old master without a corresponding event processed message), in
master mode.
4. After replicating the event into the log, the master acknowledges the switch. This implies that the buffered event has
been reliably received by the controllers, so the switch can
safely delete it.
Protocol Insights
The Ravana protocol can be viewed as a combination of mechanisms that achieve the design goals set in the previous section. By
exploring the full range of controller crash scenarios (cases (i) to
(vii) in Figure 5), we describe the key insights behind the protocol
5. The master feeds the replicated events in the log order to the
controller application, where they get processed. The application updates the necessary internal state and responds with
zero or more commands.
Exactly-Once Event Processing: A combination of temporary event buffering on the switches and explicit acknowledgment
from the controller ensures at-least once delivery of events. When
sending an event e1 to the master, the switch runtime temporarily stores the event in a local event buffer (Note that this is different from the notion of buffering PacketIn payloads in OpenFlow
switches). If the master crashes before replicating this event in the
shared log (case (i) in Figure 5), the failover mechanism ensures
that the switch runtime resends the buffered event to the new master. Thus the events are delivered at least once to all of the replicas.
To suppress repeated events, the replicas keep track of the IDs of
the logged events. If the master crashes after the event is replicated in the log but before sending an acknowledgment (case (ii)),
the switch retransmits the event to the new master controller. The
new controller’s runtime recognizes the duplicate eventID and filters the event. Together, these two mechanisms ensure exactly once
processing of events at all of the replicas.
6. The master runtime sends these commands out to the corresponding switches, and waits to receive acknowledgments
for the commands sent, before informing the replicas that the
event is processed.
7. The switch runtimes buffer the received commands, and send
acknowledgment messages back to the master controller. The
switches apply the commands subsequently.
8. After all the commands are acknowledged, the master puts
an event-processed message into the log.
A slave runtime does not feed an event to its application instance
until after the event-processed message is logged. The slave runtime delivers events to the application in order, waiting until each
event in the log has a corresponding event-processed message before proceeding. The slave runtimes also filter the outgoing commands from their application instances, rather than actually sending
Control Application
2 send_event(e1)
4 ack_event(e1)
3 write_log(e1r)
5 app_proc (e1)
6 send_cmd(c1)
7 ack_cmd(c1)
8 write_log(e1p)
e1r e1p
Figure 6: In SDN, control applications and end hosts both observe
system evolution, while traditional replication techniques treat the
switches (S) as observers.
Figure 5: Sequence diagram of event processing in controllers:
steps 2–8 are in accordance with in Figure 4.
pletes. Ravana must ensure that the new master sees the event, so
the new master can update its internal application state and issue
any remaining commands to the rest of the switches. However, in
this case, since the failed switch is no longer available to retransmit
the event, unless the old master reliably logged the event before issuing any commands, the new master could not take over correctly.
This is the reason why the Ravana protocol involves two stages of
replication. The first stage captures the fact that event e is received
by the master. The second stage captures the fact that the master has
completely processed e, which is important to know during failures
to ensure the exactly-once semantics. Thus the event-transaction
dependencies across switches, a property unique to SDN, leads to
this two-stage replication protocol.
Total Event Ordering: A shared log across the controller replicas (implemented using viewstamped replication) ensures that the
events received at the master are replicated in a consistent (linearized) order. Even if the old master fails (cases (iii) and (iv)), the
new master preserves that order and only adds new events to the
log. In addition, the controller runtime ensures exact replication
of control program state by propagating information about nondeterministic primitives like timers as special events in the replicated log.
Exactly-Once Command Execution: The switches explicitly acknowledge the commands to ensure at-least once delivery.
This way the controller runtime does not mistakenly log the eventprocessed message (thinking the command was received by the
switch), when it is still sitting in the controller runtime’s network
stack (case (iv)). Similarly, if the command is indeed received by
the switch but the master crashes before writing the event-processed
message into the log (cases (v) and (vi)), the new master processes
the event e1 and sends the command c1 again to the switch. At this
time, the switch runtime filters repeated commands by looking up
the local command buffer. This ensures at-most once execution of
commands. Together these mechanisms ensure exactly-once execution of commands.
While the protocol described in the previous section intuitively
gives us necessary guarantees for processing of events and execution of commands during controller failures, it is not clear if they
are sufficient to ensure the abstraction of a logically centralized
controller. This is also the question that recent work in this space
has left unanswered. This led to a lot of subtle bugs in their approaches that have erroneous effect on the network state as illustrated in section 2.
Thus, we strongly believe it is important to concretely define
what it means to have a logically centralized controller and then analyze whether the proposed solution does indeed guarantee such an
abstraction. Ideally, a fault-tolerant SDN should behave the same
way as a fault-free SDN from the viewpoint of all the users of the
Consistency Under Joint Switch and Controller Failure: The
Ravana protocol relies on switches retransmitting events and acknowledging commands. Therefore, the protocol must be aware of
switch failure to ensure that faulty switches do not break the Ravana
protocol. If there is no controller failure, the master controller treats
a switch failure the same way a single controller system would
treat such a failure – it relays the network port status updates to
the controller application which will route the traffic around the
failed switch. Note that when a switch fails, the controller does
not fail the entire transaction. Since this is a plausible scenario in
the fault-free case, the runtime completes the transaction by executing commands on the set of available switches. Specifically,
the controller runtime has timeout mechanisms that ensure a transaction is not stuck because of commands not being acknowledged
by a failed switch. However, the Ravana protocol needs to carefully handle the case where a switch failure occurs along with a
controller failure because it relies on the switch to retransmit lost
events under controller failures.
Observational indistinguishability in SDN: We believe the
correctness of a fault-tolerant SDN relies on the users—the endhost and controller applications—seeing a system that always behaves like there is a single, reliable controller, as shown in Figure 6.
This is what it means to be a logically centralized controller. Of
course, controller failures could affect performance, in the form of
additional delays, packet drops, or the timing and ordering of future
events. But, these kinds of variations can occur even in a fault-free
setting. Instead, our goal is that the fault-tolerant system evolves
in a way that could have happened in a fault-free execution, using observational indistinguishability [7], a common paradigm for
comparing behavior of computer programs:
Suppose the master and a switch fail sometime after the master
receives the event from that switch but before the transaction com6
Definition of observational indistinguishability: If the trace of
observations made by users in the fault-tolerant system is a possible trace in the fault-free system, then the fault-tolerant system is
observationally indistinguishable from a fault-free system.
An observation describes the interaction between an application
and an SDN component. Typically, SDN exposes two kinds of observations to its users: (i) end hosts observe requests and responses
(and use them to evolve their own application state) and (ii) control applications observe events from switches (and use them to
adapt the system to obey a high-level service policy, such as loadbalancing requests over multiple switches). For example, as illustrated in section 2, under controller failures, while controllers fail to
observe network failure events, end-hosts observe a drop in packet
throughput compared to what is expected or they observe packets
not intended for them.
Commands decide observational indistinguishability: The
observations of both kinds of users (Figure 6) are preserved in
a fault-tolerant SDN if the series of commands executed on the
switches are executed just as they could have been executed in the
fault-free system. The reason is that the commands from a controller can (i) modify the switch (packet processing) logic and (ii)
query the switch state. Thus the commands executed on a switch
determine not only what responses an end host receives, but also
what events the control application sees. Hence, we can achieve
observational indistinguishability by ensuring “command trace indistinguishability”. This leads to the following correctness criteria
for a fault-tolerant protocol:
Safety: For any given series of switch events, the resulting series
of commands executed on the switches in the fault-tolerant system
could have been executed in the fault-free system.
Liveness: Every event sent by a switch is eventually processed
by the controller application, and every resulting command sent
from the controller application is eventually executed on its corresponding switch.
Transactional and exactly-once event cycle: To ensure the
above safety and liveness properties of observational indistinguishability, we need to guarantee that the controller replicas output a
series of commands “indistinguishable" from that of a fault-free
controller for any given set of input events. Hence, we must ensure
that the same input is processed by all the replicas and that no input
is missing because of failures. Also, the replicas should process all
input events in the same order, and the commands issued should be
neither missing nor repeated in the event of replica failure.
In other words, Ravana provides transactional semantics to the
entire “control loop” of (i) event delivery, (ii) event ordering, (iii)
event processing, and (iv) command execution. (If the command
execution results in more events, the subsequent event-processing
cycles are considered separate transactions.) In addition, we ensure
that any given transaction happens exactly once—it is not aborted
or rolled back under controller failures. That is, once an event is
sent by a switch, the entire event-processing cycle is executed till
completion, and the transaction affects the network state exactly
once. Therefore, our protocol that is designed around the goals
listed in Table 2 will ensure observational indistinguishability between an ideal fault-free controller and a logically centralized but
physically replicated controller. While we provide an informal argument for correctness, modeling the Ravana protocol using a formal specification tool and proving formally that the protocol is indeed sufficient to guarantee the safety and liveness properties is out
of scope for this paper and is considered part of future work.
Figure 7: Optimizing performance by processing multiple transactions in parallel. The controller processes events e1 and e2, and the
command for e2 is acknowledged before both the commands for e1
are acknowledged.
Performance Optimizations
In this section, we discuss several approaches that can optimize the
performance of the protocol while retaining its strong correctness
Parallel logging of events: Ravana protocol enforces a consistent ordering of all events among the controller replicas. This is
easy if the master were to replicate the events one after the other sequentially but this approach is too slow when logging tens of thousands of events. Hence, the Ravana runtime first imposes a total
order on the switch events by giving them monotonically increasing log IDs and then does parallel logging of events where multiple
threads write switch events to the log in parallel. After an event is
reliably logged, the master runtime feeds the event to its application instance, but it still follows the total order. The slaves infer the
total order from the log IDs assigned to the replicated events by the
Processing multiple transactions in parallel: In Ravana, one
way to maintain consistency between controller and switch state is
to send the commands for each event transaction one after the other
(and waiting for switches’ acknowledgments) before replicating the
event processed message to the replicas. Since this approach can
be too slow, we can optimize the performance by pipelining multiple commands in parallel without waiting for the ACKs. The runtime also interleaves commands generated from multiple independent event transactions. An internal data structure maps the outstanding commands to events and traces the progress of processing
events. Figure 7 shows an example of sending commands for two
events in parallel. In this example, the controller runtime sends the
commands resulting from processing e2 while the commands from
processing e1 are still outstanding.
Sending commands in parallel does not break the ordering of
event processing. For example, the commands from the controller
to any given individual switch (the commands for e1) are ordered
by the reliable control-plane channel (e.g., via TCP). Thus at a
given switch, the sequence of commands received from the controller must be consistent with the order of events processed by the
controller. For multiple transactions in parallel, the runtime buffers
the completed events till the events earlier in the total order are also
completed. For example, even though the commands for e2 are
acknowledged first, the runtime waits till all the commands for e1
are acknowledged and then replicates the event processed messages
for both e1 and e2 in that order. Despite this optimization, since the
event processed messages are written in log order, we make sure
that the slaves also process them in the same order.
Clearing switch buffers: The switch runtime maintains both an
event buffer (EBuf) and a command buffer (CBuf). We add buffer
clear messages that help garbage collect these buffers. As soon as
the event is durably replicated in the distributed log, the master controller sends an EBuf_CLEAR message to confirm that the event is
persistent. However, a CBuf_CLEAR is sent only when its corresponding event is done processing. An event processed message
is logged only when all processing is done in the current protocol,
so a slave controller gets to know that all the commands associated
with the event are received by switches, and it should never send
the commands out again when it becomes a master. As a result,
when an event is logged, the controller sends an event acknowledgment, and at the same time piggybacks both EBuf_CLEAR and
Unique transaction IDs: The controller runtime associates every command with a unique transaction ID (XID). The XIDs are
monotonically increasing and identical across all replicas, so that
duplicate commands can be identified. This arises from the controllers’ deterministic ordered operations and does not require an
additional agreement protocol. In addition, the switch also needs
to ensure that unique XIDs are assigned to events sent to the controller. We modified Open vSwitch to increment the XID field
whenever a new event is sent to the controller. Thus, we use 32-bit
unique XIDs (with wrap around) for both events and commands.
Changes to OpenFlow: We modified the OpenFlow 1.3 controllerswitch interface to enable the two parties to exchange additional
Ravana-specific metadata: EVENT_ACK, CMD_ACK, EBuf_CLEAR,
and CBuf_CLEAR. The ACK messages acknowledge the receipt
of events and commands, while CLEAR help reduce the memory
footprint of the two switch buffers by periodically cleaning them.
As in OpenFlow, all messages carry a transaction ID to specify the
event or command to which it should be applied.
Implementation of Ravana
Implementing Ravana in SDN involves changing three important
components: (i) instead of controller applications grappling with
controller failures, a controller runtime handles them transparently,
(ii) a switch runtime replays events under controller failures and filters repeated commands, and (iii) a modified control channel supports additional message types for event-processing transactions.
Controller Runtime: Failover, Replication
Each replica has a runtime component which handles the controller
failure logic transparent to the application. The same application
program runs on all of the replicas. Our prototype controller runtime uses the Ryu [8] message-parsing library to transform the raw
messages on the wire into corresponding OpenFlow messages.
Transparent Programming Abstraction
Ravana provides a fault-tolerant controller runtime that is completely transparent to control applications. The Ravana runtime
intercepts all switch events destined to the Ryu application, enforces a total order on them, stores them in a distributed in-memory
log, and only then delivers them to the application. The application updates the controller internal state, and generates one or
more commands for each event. Ravana also intercepts the outgoing commands — it keeps track of the set of commands generated
for each event in order to trace the progress of processing each
event. After that, the commands are delivered to the corresponding switches. Since Ravana does all this from inside Ryu, existing
single-threaded Ryu applications can directly run on Ravana without modifying a single line of code.
Leader election: The controllers elect one of them as master
using a leader election component written using ZooKeeper [9], a
synchronization service that exposes an atomic broadcast protocol.
Much like in Google’s use of Chubby [10], Ravana leader election
involves the replicas contending for a ZooKeeper lock; whoever
successfully gains the lock becomes the master. Master failure is
detected using the ZooKeeper failure-detection service which relies
on counting missed heartbeat messages. A new master is elected by
having the current slaves retry gaining the master lock.
Event logging: The master saves each event in ZooKeeper’s distributed in-memory log. Slaves monitor the log by registering a
trigger for it. When a new event is propagated to a slave’s log, the
trigger is activated so that the slave can read the newly arrived event
Event batching: Even though its in-memory design makes the
distributed log efficient, latency during event replication can still
degrade throughput under high load. In particular, the master’s
write call returns only after it is propagated to more than half of
all replicas. To reduce this overhead, we batch multiple messages
into an ordered group and write the grouped event as a whole to
the log. On the other side, a slave unpacks the grouped events and
processes them individually and in order.
Control Channel Interface: Transactions
To demonstrate the transparency of programming abstraction, we
have tested a variety of Ryu applications [12]: a MAC learning
switch, a simple traffic monitor, a MAC table management app, a
link aggregation (LAG) app, and a spanning tree app. These applications are written using the Ryu API, and they run on our faulttolerant control platform without any changes.
Currently we expect programmers to write controller applications that are single-threaded and deterministic, similar to most
replicated state machine systems available today. An application
can introduce nondeterminism by using timers and random numbers. Our prototype supports timers and random numbers through a
standard library interface. The master runtime treats function calls
through this interface as special events and persists the event metadata (timer begin/end, random seeds, etc.) into the log. The slave
runtimes extract this information from the log so their application
instances execute the same way as the master’s. State-machine
replication with multi-threaded programming has been studied [13],
and supporting it in Ravana is future work.
Switch Runtime: Event/Command Buffers
We implement our switch runtime by modifying the Open vSwitch
(version 1.10) [11], which is the most widely used software OpenFlow switch. We implement the event and command buffers as additional data structures in the OVS connection manager. If a master fails, the connection manager sends events buffered in EBuf
to the new master as soon as it registers its new role. The command buffer CBuf is used by the switch processing loop to check
whether a command received (uniquely identified by its transaction
ID) has already been executed. These transaction IDs are remembered till they can be safely garbage collected by the corresponding
CBuf_CLEAR message from the controller.
Performance Evaluation
To understand Ravana’s performance, we evaluate our prototype to
answer the following questions:
• What is the overhead of Ravana’s fault-tolerant runtime on
event-processing throughput?
(a) Ravana Throughput Overhead
Number of Switches
(b) Throughput with Different Number of Switches
Average Response Latency (ms)
(c) CDF for cbench Latency
Batch Size
(a) Event-Processing Throughput with Batching
Latency (ms)
Throughput (R/s)
Figure 8: Ravana Event-Processing Throughput and Latency
Batch Size
(b) Event-Processing Latency with Batching
Failover Time (ms)
(c) CDF for Ravana Failover Time
Figure 9: Variance of Ravana Throughput, Latency and Failover Time
• What is the effect of the various optimizations on Ravana’s
event-processing throughput and latency?
controller replicas in a failure-free execution. We consider this as
a reasonable overhead given the correctness guarantee and replication mechanisms Ravana added.
To evaluate the runtime’s scalability with respect to multiple switch
connections, we ran cbench in throughput mode with a large number of simulated switch connections. A series of throughput measurements are shown in Figure 8b. Since the simulated switches
send events at the highest possible rate, as the number of switches
become reasonably large, the controller processing rate saturates
but does not go down. The event-processing throughput remains
high even when we connect over one thousand simulated switches.
The result shows that the controller runtime can manage a large
number of parallel switch connections efficiently.
• Can Ravana respond quickly to controller failure?
• What are the throughput and latency trade-offs for various
correctness guarantees?
We run experiments on three machines connected by 1Gbps links.
Each machine has 12GB memory and an Intel Xeon 2.4GHz CPU.
We use ZooKeeper 3.4.6 for event logging and leader election. We
use the Ryu 3.8 controller platform as our non-fault-tolerant baseline.
Measuring Throughput and Latency
Figure 8c shows the latency CDF of our system when tested with
cbench. In this experiment, we run cbench and the master controller on the same machine, in order to benchmark the Ravana
event-processing time without introducing extra network latency.
The latency distribution is drawn using the average latency calculated by cbench over 100 runs. The figure shows that most of the
events can be processed within 12ms.
We first compare the throughput (in terms of flow responses per
second) achieved by the vanilla Ryu controller and the Ravana prototype we implemented on top of Ryu, in order to characterize Ravana’s overhead. The measurements are done using the cbench [14]
performance test suite: the test program spawns a number of processes that act as OpenFlow switches. In cbench’s throughput
mode, the processes send PacketIn events to the controller as fast as
possible. Upon receiving a PacketIn event from a switch, the controller sends a command with a forwarding decision for this packet.
The controller application is designed to be simple enough to give
responses without much computation, so that the experiment can
effectively benchmark the Ravana protocol stack.
Figure 8a shows the event-processing throughput of the vanilla
Ryu controller and our prototype in a fault-free execution. We used
both the standard Python interpreter and PyPy (version 2.2.1), a
fast Just-in-Time interpreter for Python. We enable batching with
a buffer of 1000 events and 0.1s buffer time limit. Using standard Python, the Ryu controller achieves a throughput of 11.0K
responses per second (rps), while the Ravana controller achieves
9.2K, with an overhead of 16.4%. With PyPy, the event-processing
throughput of Ryu and Ravana are 67.6K rps and 46.4K rps, respectively, with an overhead of 31.4%. This overhead includes the
time of serializing and propagating all the events among the three
Sensitivity Analysis for Event Batching
The Ravana controller runtime batches events to reduce the overhead for writing several events in the ZooKeeper event log. Network operators need to tune the batching size parameter to achieve
the best performance. A batch of events are flushed to the replicated log either when the batch reaches the size limit or when no
event arrives within a certain time limit.
Figure 9a shows the effect of batching sizes on event processing
throughput measured with cbench. As batching size increases,
throughput increases due to reduction in the number of RPC calls
needed to replicate events. However, when batching size increases
beyond a certain number, the throughput saturates because the performance is bounded by other system components (marshalling and
unmarshalling OpenFlow messages, event processing functions, etc.)
Throughput (Responses/s)
While increasing batching size can improve throughput under
high demand, it also increases event response latency. Figure 9b
shows the effect of varying batch sizes on the latency overhead.
The average event processing latency increases almost linearly with
the batching size, due to the time spent in filling the batch before it
is written to the log.
The experiment results shown in Figure 9a and 9b allow network
operators to better understand how to set an appropriate batching
size parameter based on different requirements. If the application
needs to process a large number of events and can tolerant relatively high latency, then a large batch size is helpful; if the events
need to be instantly processed and the number of events is not a big
concern, then a small batching size will be more appropriate.
+Reliable Events
+Total Ordering
+Exactly-once Cmds
(a) Throughput Overhead for Correctness Guarantees
Measuring Failover Time
When the master controller crashes, it takes some time for the new
controller to take over.
To evaluate the efficiency of Ravana
controller failover mechanism, we conducted a series of tests to
measure the failover time distribution, as shown in Figure 9c. In
this experiment, a software switch connects two hosts which continuously exchange packets that are processed by the controller in
the middle. We bring down the master. The end hosts measure the
time for which no traffic is received during the failover period. The
result shows that the average failover time is 75ms, with a standard
deviation of 9ms. This includes around 40ms to detect failure and
elect a new leader (with the help of ZooKeeper), around 25ms to
catch up with the old master (can be reduced further with optimistic
processing of the event log at the slave) and around 10ms to register
the new role on the switch. The short failover time ensures that the
network events generated during this period will not be delayed for
a long time before getting processed by the new master.
+Reliable Event
+Total Ordering
+Exactly-Once Cmd
Average Response Latency (ms)
(b) Latency Overheads for Correctness Guarantees
Figure 10: Throughput and Latency with Different Correctness
on the controller throughput. Thus adding all these mechanisms
ensures the strongest collection of correctness guarantees possible
under Ravana and the cumulative overhead is 31%. While some of
the overheads shown above can be reduced further with implementation specific optimizations like cumulative and piggybacked message ACKs, we believe the overheads related to replicating the log
and maintaining total ordering are unavoidable to guarantee protocol correctness.
Consistency Levels: Overhead
Our design goals ensure strict correctness in terms of observational
indistinguishability for general SDN policies. However, as show
in Figure 10, some guarantees are costlier to ensure (in terms of
through/latency) than the others. In particular, we looked at each of
the three design goals that adds overhead to the system compared
to the weakest guarantee.
The weakest consistency included in this study is the same as
what existing fault-tolerant controller systems provide — the master immediately processes events once they arrive, and replicates
them lazily in the background. Naturally this avoids the overhead
of all the three design goals we aim for and hence has only a small
throughput overhead of 8.4%.
Latency. Figure 10b shows the CDF for the average time it
takes for the controller to send a command in response to a switch
event. Our study reveals that the main contributing factor to the
latency overhead is the synchronization mechanism that ties event
logging to event processing. This means that the master has to wait
till a switch event is properly replicated and only then processes the
event. This is why all the consistency levels that do not involve this
guarantee have a latency of around 0.8ms on average but those that
involve the total event ordering guarantee have a latency of 11ms
on average.
Relaxed Consistency. The Ravana protocol described in this paper is oblivious to the nature of control application state or the various types of control messages processed by the application. This is
what led to the design of a truly transparent runtime that works with
unmodified control applications. However, given the breakdown in
terms of throughput and latency overheads for various correctness
guarantees, it is natural to ask if there are control applications that
can benefit from relaxed consistency requirements.
For example, a Valiant load balancing application that processes
flow requests (PacketIn events) from switches and assigns paths to
flows randomly is essentially a stateless application. So the constraint on total event ordering can be relaxed entirely for this application. But if this application is run in conjunction with a mod-
The second consistency level is enabled by guaranteeing exactlyonce processing of switch events received at the controller. This
involves making sure that the master synchronously logs the events
and explicitly sends event ACKs to corresponding switches. This
has an additional throughput overhead of 7.8%.
The third consistency level ensures total event ordering in addition to exactly-once events. This makes sure that the order in which
the events are written to the log is the same as the order in which the
master processes them, and hence involves mechanisms to strictly
synchronize the two. Ensuring this consistency incurs an additional
overhead of 5.3%.
The fourth and strongest consistency level ensures exactly-once
execution of controller commands. It requires the switch runtime
to explicitly ACK each command from the controller and to filter repeated commands. This adds an additional overhead of 9.7%
ule that also reacts to topology changes (PortStatus events), then it
makes sense to enable to constraint just for the topology events and
disable it for the rest of the event types. This way, both the throughput and latency of the application can be improved significantly.
the effects on state external to the replicas. Fault-tolerant journaling file systems [20] and database systems [21] assume that the
commands are idempotent and that replicas can replay the log after failure to complete transactions. However, the commands executed on switches are not idempotent. The authors of [22] discuss
strategies for ensuring exactly-once semantics in replicated messaging systems. These strategies are similar to our mechanisms for
exactly-once event semantics but they cannot be adopted directly
to handle cases where failure of a switch can effect the dataplane
state on other switches.
A complete study of which applications benefit from relaxing
which correctness constraints and how to enable programmatic control of runtime knobs is out of scope for this paper. However, from
a preliminary analysis, many applications seem to benefit from either completely disabling certain correctness mechanisms or only
partially disabling them for certain kinds of OpenFlow messages.
TCP fault-tolerance: Another approach is to provide fault tolerance within the network stack using TCP failover techniques [23–
25]. These techniques have a huge overhead because they involve
reliable logging of each packet or low-level TCP segment information, in both directions. In our approach, much fewer (applicationlevel) events are replicated to the slaves.
VM fault-tolerance: Remus [26] and Kemari [27] are techniques that provide fault-tolerant virtualization environments by
using live VM migration to maintain availability. These techniques
synchronize all in-memory and on-disk state across the VM replicas. The domain-agnostic checkpointing can lead to correctness
issues for high-performance controllers. Thus, they impose significant overhead because of the large amount of state being synchronized.
Observational indistinguishability: Lime [28] uses a similar
notion of observational indistinguishability, in the context of live
switch migration (where multiple switches emulate a single virtual
switch) as opposed to multiple controllers.
Statesman [29] takes the approach of allowing incorrect switch
state when a master fails. Once the new master comes up, it reads
the current switch state and incrementally migrates it to a target
switch state determined by the controller application. LegoSDN [30]
focuses on application-level fault-tolerance caused by application
software bugs, as opposed to complete controller crash failures.
Akella et. al. [31] tackle the problem of network availability when
the control channel is in-band whereas our approach assumes a
separate out-of-band control channel. The approach is also heavyhanded where every element in the network including the switches
is involved in a distributed snapshot protocol. Beehive [32] describes a programming abstraction that makes writing distributed
control applications for SDN easier. However, while the focus in
Beehive is on controller scalability, they do not discuss consistent
handling of the switch state.
Related Work
Distributed SDN control with consistent reliable storage: The
Onix [3] distributed controller partitions application and network
state across multiple controllers using distributed storage. The switch
state is stored in a strongly consistent Network Information Base
(NIB). Controllers subscribe to switch events in the NIB and the
NIB publishes new events to subscribed controllers independently
and in an eventually consistent manner. This could violate the total event ordering correctness constraint. Since the paper is underspecified on some details, it is not clear how Onix handles simultaneous occurrence of controller crashes and network events
(like link/switch failures) that can affect the commands sent to other
switches. In addition, programming control applications is difficult
since the applications have to be conscious of the controller faulttolerance logic. Onix does however handle continued distributed
control under network partitioning for both scalability and performance, while Ravana is concerned only with reliability. ONOS [4]
is an experimental controller platform that provides a distributed,
but logically centralized, global network view; scale-out; and fault
tolerance by using a consistent store for replicating application state.
However, owing to its similarities to Onix, it also suffers from reliability guarantees as Onix does.
Distributed SDN control with state machine replication: HyperFlow [15] is an SDN controller where network events are replicated to the controller replicas using a publish-subscribe messaging paradigm among the controllers. The controller application
publishes and receives events on subscribed channels to other controllers and builds its local state solely from the network events.
In this sense, the approach to building application state is similar
to Ravana but the application model is non-transparent because the
application bears the burden of replicating events. In addition, HyperFlow also does not deal with the correctness properties related
to the switch state.
Distributed SDN with weaker ordering requirements: Early
work on software-defined BGP route control [16, 17] allowed distributed controllers to make routing decisions for an Autonomous
System. These works do not ensure a total ordering on events from
different switches, and instead rely on the fact that the final outcome of the BGP decision process does not depend on the relative ordering of messages from different switches. This assumption
does not hold for arbitrary applications.
Traditional fault-tolerance techniques: A well-known protocol for replicating state machines in client-server models for reliable service is Viewstamped Replication (VSR) [6]. VSR is not
directly applicable in the context of SDN, where switch state is as
important as the controller state. In particular, this leads to missing
events or duplicate commands under controller failures, which can
lead to incorrect switch state. Similarly, Paxos [18] and Raft [19]
are distributed consensus protocols that can be used to reach a consensus on input processed by the replicas but they do not address
Ravana is a distributed protocol for reliable control of softwaredefined networks. In our future research, we plan to create a formal
model of our protocol and use verification tools to prove its correctness. We also want to extend Ravana to support multi-threaded
control applications, richer failure models (such as Byzantine failures), and more scalable deployments where each controller manages a smaller subset of switches.
We wish to thank the SOSR reviewers for their feedback. We
would also like to thank Nick Feamster, Wyatt Lloyd and Siddhartha Sen who gave valuable comments on previous drafts of
this paper. We thank Joshua Reich who contributed to initial discussions on this work. This work was supported in part by the NSF
grant TS-1111520; the NSF Award CSR-0953197 (CAREER); the
ONR award N00014-12-1-0757; and an Intel grant.
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