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Logically Centralized? State Distribution Trade-offs in Software Defined Networks. Written By Dan Levin, Andreas Wundsam, Brandon Heller, Nikhil Handigol, and Anja Feldmann Presented By Michael Over. Abstract. - PowerPoint PPT Presentation
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Logically Centralized? State Distribution
Trade-offs in Software Defined NetworksWritten By Dan Levin, Andreas
Wundsam, Brandon Heller, Nikhil Handigol, and Anja Feldmann
Presented By Michael Over
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Abstract Software Defined Networks (SDNs) give
network designers freedom to refactor the network control plane
Enables the network control logic to be designed and operated on a global network view
Control plane state and logic must inevitably be physically distributed to achieve responsiveness, reliability, and scalability goals
Motivating Question: How does distributed SDN state impact the performance of a logically centralized control application?
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Abstract
Characterize the state exchange points in a distributed SDN control plane
Two key state distribution trade-offs simulated in the context of an existing SDN load balancer application The impact of inconsistent global network
view on load balancer performance Compare different state management
approaches Results show that SDN control state
inconsisntency significantly degrades performance of logically centralized control applications
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Outline
Introduction Related Work Distributed State Management and
Trade-Offs Example Application Experiments Summary
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Introduction
SDNs have sparked significant interest in rethinking classical approaches to network architecture and design
SDN enables the network control plane logic to be decoupled from the network forwarding hardware
Enables the ability to design and reason about the network control plane as a centrally controlled application operating on a global network view (GNV) as its input
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Introduction
Freedom to refactor the network control plane – logically centralized
New choices: How centralized or distributed should the network control plane be? Fully physically centralized control is
inadequate – limits responsiveness, reliability, and scalability
Resort to a physically distributed control plane, on which a logically centralized control plane operators
Trade-offs between different consistency models and associated liveness properties
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Introduction
Strongly consistent control designs always operate on a consistent world view Limits responsiveness which can lead to
suboptimal decisions Eventually consistent designs
integrade information as it becomes available React faster and can cope with higher
update rates Potentially present a temporarily
inconsistent world view and thus may cause incorrect behavior
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Introduction
Need to understand how physically distributed control plane state will impact the performance and correctness of a control application logic designed to operate as though it were centralized
When the underlying distributed control plane state leads to inconsistency or staleness in the global network view, how much does the network performance suffer?
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Introduction
Characterize the state exchange points in a distributed SDN control plane
Two key state distribution trade-offs arise: Control application performance
(optimality) vs. state distribution overhead
Application logic complexity vs. robustness to inconsistency
These trade-offs are simulated in the context of an existing SDN application for flow-based load balancing
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Introduction
Two different control application approaches: A simple approach that is ignorant to
potential inconsistency A more complex approach that
considers the potential inconsistency when making a load balancing decision
Initial results demonstrate that global network view (GNV) inconsistency significantly degrades the performance of the simple approach, while the more complex approach is more robust
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Outline
IntroductionRelated Work Distributed State Management and
Trade-Offs Example Application Experiments Summary
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Related Work
Three main categories: SDN applications that motivate this
study Control frameworks that provide a
logically centralized view to SDN applications
Previous studies on routing state distribution trade-offs
Onix is a control plane platform designed to enable scalable control applications – abstract away the task of network state distribution
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Related Work Hyperflow is a distributed event-based
control plane for OpenFlow that allows control applications to make decisions locally by passively synchronizing network-wide views of the individual controller instances
Consistent Updates focuses on state management between the physical network and the network information base to enforce consistent forwarding state at different levels
Consensus Routing presents a consistency-first approach to adopting forwarding upates in the context of inter-domain routing
Probabilistically Bounded Staleness is a set of models that predicts the expected consistency of an eventually-consistent data store
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Outline
Introduction Related WorkDistributed State Management and Trade-Offs
Example Application Experiments Summary
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Distributed State Management and Trade Offs
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Distributed State Management and Trade Offs
Each controller maintains a Network Information Base (NIB), a view of the global network state presented to an application.
The NIB at each controller is periodically and independently updated with state collected from the physical network.
Controllers synchronize their NIB state among themselves
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Distributed State Management and Trade Offs
Different manners of distributed, replicated storage models may be used to realize the NOS state distribution and management
Key Property of any NOS state distribution approach – The degree of state consistency achieved: Strong consistency Eventual consistency
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Distributed State Management and Trade Offs
A strongly consistent NOS will never present inconsistent NIB state to an application However, limits the rate at which NOS
state can be updated While an update is being processed,
applications continue to operate on a stale (but consistent) world view
Eventually consistent approaches react faster but temporarily introduce inconsistency
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Distributed State Management and Trade Offs
Trade-off #1: Consistency model of NOS state distribution vs. application objective optimality The performance of the network in
relation to the control application’s objective can suffer with inconsistent or stale global network view
The cost to achieving consistent state in the global network view entails higher rates of control synchronization and communication overhead -> decreases responsiveness
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Distributed State Management and Trade Offs
Trade-off #2: Application logic complexity vs. robustness to stale NOS state A “logically centralized” application
that is unaware of the potential staleness of its input is simpler to design
An application which is aware of the underlying distributed NOS state can take measures to separate and compare the inter-domain global network view with its own local domain view
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Outline
Introduction Related Work Distributed State Management and
Trade-OffsExample Application Experiments Summary
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Example Application
An arrival-based network load balancer control application
Objective: Minimize the maximum link utilization in the network
Two implementations featuring different state (and staleness) awareness and management approaches: Link Balancer Controller (LBC) Separate State Link Balancer Controller
(SSLBC)
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Example Application
Link Balancer Controller (LBC) – the simpler of the two approaches
Upon a dataplane triggered event, a global network view is presented by the NOS to the domain application instance
Combines both the physical network state from within the domain as well as any inter-domain link utilization updates from other controllers
A list of paths with utilizations is generated and used to choose the path with the lowest max link utilization to assign the next arriving flow
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Example Application
Separate State Link Balancer Controller (SSLBC) Keeps fresh intra-domain physical
network state separate from updates learned through inter-domain controller synchronization events
The arrival-based path selection incorporates logic to ensure convergence properties on load distribution
Calculates new link metrics and chooses the path with the minimum max link utilization
Effectively, the global network view guides each application instance to redistribute a scaled fraction of its local l ink imbalance on a flow-by-blow arrival basis
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Outline
Introduction Related Work Distributed State Management and
Trade-Offs Example ApplicationExperiments Summary
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Experiments Developed a custom simulator to
implement the key state-exchange interfaces of an SDN
Designed to capture interactions between the three SDN layers from Figure 1 – Physical Network, State Management, and Application
Very simple topology chosen for the experiment – two cooperating controller domains, with each domain having a single switch and server
Consider only downstream traffic Load balancer objective is to minimize the
different between all link utilizations; they use RMSE of the maximum link utilization along each path
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Experiments
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Experiments
Two different workloads to explore the impact of inconsistent NOS state on network load balance First, a deterministic, controlled
workload to impart a link utilization imbalance
Second, a more realistic workload First, the load balancer based upon
the LBC state management approach with different synchronization intervals: 0, 1, 2, 4, 8, and 16 timesteps
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Experiments
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Experiments
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Experiments
Controlled Workkload – SSLBC Next the second trade-off is examined,
namely, how the SSLBC state management approach is able to handle the workload
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Experiments
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Experiments
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Experiments
Using a more realistic workload, the authors also demonstrated similar results
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Outline
Introduction Related Work Distributed State Management and
Trade-Offs Example Application ExperimentsSummary
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Summary Logically centralized world views enable
simplified programming models However, as the logically centralized
world view is mapped to a physically distributed system, fundamental trade-offs emerge that affect application performance, liveness, robustness, and correctness
These trade-offs were revisisted in the context of design choices exposed by SDNs Staleness vs. optimality Application logic complexity vs. robustness to
inconsistency
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Summary
Similar trade-offs arise in other SDN control applications
Future Work: Extend the simulator to more
realistically model traffic characteristics and delays, and the overhead of synchronization
Apply tools to larger and more complex toplogies
Compare the results of different applications
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Questions?