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Logically Centralized? State Distribution Trade-offs in Software Defined Networks

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.

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Logically Centralized? State Distribution Trade-offs in Software Defined Networks

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  1. 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

  2. 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?

  3. 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

  4. Outline • Introduction • Related Work • Distributed State Management and Trade-Offs • Example Application • Experiments • Summary

  5. 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

  6. 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

  7. 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

  8. 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?

  9. 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

  10. 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

  11. Outline • Introduction • Related Work • Distributed State Management and Trade-Offs • Example Application • Experiments • Summary

  12. 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

  13. 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

  14. Outline • Introduction • Related Work • Distributed State Management and Trade-Offs • Example Application • Experiments • Summary

  15. Distributed State Management and Trade Offs

  16. 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

  17. 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

  18. 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

  19. 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

  20. 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

  21. Outline • Introduction • Related Work • Distributed State Management and Trade-Offs • Example Application • Experiments • Summary

  22. 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)

  23. 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

  24. 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

  25. Outline • Introduction • Related Work • Distributed State Management and Trade-Offs • Example Application • Experiments • Summary

  26. 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

  27. Experiments

  28. 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

  29. Experiments

  30. Experiments

  31. Experiments • Controlled Workkload – SSLBC • Next the second trade-off is examined, namely, how the SSLBC state management approach is able to handle the workload

  32. Experiments

  33. Experiments

  34. Experiments • Using a more realistic workload, the authors also demonstrated similar results

  35. Outline • Introduction • Related Work • Distributed State Management and Trade-Offs • Example Application • Experiments • Summary

  36. 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

  37. 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

  38. Questions?

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