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B4: Experience with a Globally-Deployed Software Defined WAN

B4: Experience with a Globally-Deployed Software Defined WAN

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B4: Experience with a Globally-Deployed Software Defined WAN

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  1. B4: Experience with a Globally-Deployed Software Defined WAN Presenter: Klara Nahrstedt CS 538 Advanced Networking Course Based on “B4: Experience with a Globally Deployed Software Defined WAN”, by Sushant Jain et al, ACM SIGCOMM 2013, Hong-Kong, China

  2. Overview • Current WAN Situation • Google Situation • User Access Network • Data Centers Network (B4 Network) • Problem Description regarding B4 Network • B4 Design • Background on Existing Technologies used in B4 • New Approaches used in B4 • Switch design • Routing design • Network controller design • Traffic engineering design • Experiments • Conclusion

  3. Current WAN Situation • WAN (Wide Area Networks) must deliver performance and reliability in Internet, delivering terabits/s bandwidth • WAN routers consist of high end specialized equipment being very expensive due to provisioning of high availability • WANs treat all bits the same and applications are treated the same • Current Solution: • WAN links are provisioned to 30-40% utilization • WAN is over-provisioned to deliver reliability at very real cost of 2-3x bandwidth over-provisioning and high end routing gear.

  4. Google WAN Current Situation • Two types of WAN networks • User-facing network peers – exchanging traffic with other Internet domains • End user requests and responses are delivered to Google data centers and edge caches • B4 network – providing connectivity among data centers • Network applications (traffic classes) over B4 network • User data copies (email, documents, audio/video files) to remote data centers for availability and durability • Remote storage access for computation over inherently distributed data sources • Large-scale data push synchronizing state across multiple data centers • Over 90% of internal application traffic runs over B4

  5. Google’s Data Center WAN (B4 Network) • Google controls • applications, • Servers, • LANs, all the way to the edge of network • 2. bandwidth-intensive apps • Perform large-scale data copies from one site to another; • Adapt transmission rate • Defer to higher priority interactive apps during failure periods or resource constraints • 3. No more than few dozen data center deployments, hence making central control of bandwidth • possible

  6. Problem Description • How to increase WAN Link utility to close to 100%? • How to decrease cost of bandwidth provisioning and still provide reliability ? • How to stop over-provisioning? • Traditional WAN architectures cannot achieve the level of scale, fault tolerance, cost efficiency, and control required for B4

  7. B4 Requirements • Elastic Bandwidth Demand • Synchronization of datasets across sites demands large amount of bandwidth, but can tolerate periodic failures with temporary bandwidth reductions • Moderate number of sites • End application control • Google controls both applications and site networks connected to B4 • Google can enforce relative application priorities and control bursts at network edges (no need for overprovisioning) • Cost sensitivity • B4’s capacity targets and growth rate led to unsustainable cost projections

  8. Approach for Google’s Data Center WAN • Software Defined Networking architecture (Open Flow) • Dedicated software-based control plane running on commodity servers • Opportunity to reason about global state • Simplified coordination and orchestration for both planned and unplanned network changes • Decoupling of software and hardware evolution • Customization of routing and traffic engineering • Rapid iteration of novel protocols • Simplified testing environment • Improved capacity planning • Simplified management through fabric-centric rather than router-centric WAN view

  9. B4 Design OFA – Open Flow Agent OFC – Open Flow Controller NCA – Network Control Application NCS – Network Control Server TE – Traffic Engineering RAP – Routing Application Proxy Paxos – Family of protocols for solving consensus in network of unreliable processors (Consensus is process of agreeing on one result among group of participants; Paxos is usually used where durability is needed such as in replication). Quagga – network routing software suite providing implementations of Open Shortest Path First (OSPF), Routing Information Protocol (RIP), Border Gateway Protocol (BGP), and IS-IS

  10. Background • IS-IS (Intermediate System to Intermediate System) routing protocol – interior gateway protocol (iBGP) routing information within AS – link-state routing protocol (similar to OSPF) • ECMP – Equal-cost multi-path routing • Next hop packet forwarding to single destination can occur over multiple “best paths” • See animation http://en.wikipedia.org/wiki/Equal-cost_multi-path_routing

  11. Equal-cost multipath routing (ECMP) • ECMP • Multipath routing strategy that splits traffic over multiple paths for load balancing • Why not just round-robin packets? • Reordering (lead to triple duplicate ACK in TCP?) • Different RTT per path (for TCP RTO)… • Different MTUs per path http://www.cs.princeton.edu/courses/archive/spring11/cos461/

  12. Equal-cost multipath routing (ECMP) • Path-selection via hashing • # buckets = # outgoing links • Hash network information (source/dest IP addrs) to select outgoing link: preserves flow affinity http://www.cs.princeton.edu/courses/archive/spring11/cos461/

  13. Now: ECMP in datacenters • Datacenter networks are multi-rooted tree • Goal: Support for 100,000s of servers • Recall Ethernet spanning tree problems: No loops • L3 routing and ECMP: Take advantage of multiple paths http://www.cs.princeton.edu/courses/archive/spring11/cos461/

  14. Switch Design • Traditional Design of routing equipment • Deep buffers • Very large forwarding tables • Hardware support for high availability • B4 Design • Avoid deep buffers while avoiding expensive packet drops • Run across relatively small number of data centers, hence smaller forwarding tables • Switch failures caused more by software than hardware, hence separation of software functions off the switch hardware minimizes failures

  15. Switch Design • Two-stage switch • 128-port 10G switch • 24 individual 16x10G non-blocking switch chips ( Spine Layer) • Forwarding Protocol • Ingress chip bounces incoming chip to Spine • Spine forwards packets to appropriate output chip (unless dest on the same ingress chip) • Open Flow Agent (OFA) • User-level process running on switch hardware • OFA connects to OFC accepting OF (OpenFlow) commands • OFA translates OF messages into driver commands to set chip forwarding table entries • Challenges: • OFA exports abstraction of a single non-blocking switch with hundreds of 10 Gbps ports. However, underlying switch has multiple physical switch chips, each with their own forwarding tables. • OpenFlow architecture considers neutral version of forwarding table entries, but switches have many linked forwarding tables of various sizes and semantics.

  16. Network Control Design • Each site has multiple NCS • NCS and switches share dedicated Out of band control-plane network • One of NCS serves as leader • Paxos handles leader election • Paxos instances perform application-level failure detection among pre-configured set of available replicas for given piece of control functions. • Modified Onixis used • Network Information Base (NIB) contains network state (topology, trunk configurations, link status) • OFC replicas are warm standbys • OFA maintains multiple connections to multiple OFCs • Active communication to only one OFC at a time

  17. Routing Design • Use Open Source Quagga stack for BGP/ISIS on NCS • Develop RAP to provide connectivity between Quagga and OF switches for • BGP/ISIS route updates • Routing protocol packets flowing between switches and Quagga • Interface updates from switches to Quagga • Translation from RIB entries forming a network level view of global connectivity to low level HW tables • Translation of RIB into two Open Flow tables

  18. Traffic Engineering Architecture • TE Server operates over states • Network topology • Flow Group (FG) • Applications are aggregated to FG • {source site, dest site, QoS} • Tunnel (T) • Site-level path in network • A->B->C • Tunnel Group (TG) • Map of FGs to set of tunnels and corresponding weights • Weight specifies fraction of FG traffic to be forwarded along each tunnel

  19. Bandwidth Functions • Each application is associated with bandwidth function • Bandwidth Function • Gives Contract between app and B4 • Specifies BW allocation to app given flow’s relative priority on a scale, called fair share • Derived from admin-specific static weights (slope) specifying app priority • Configured, measured and provided to TE via Bandwidth Enforcer

  20. TE Optimization Algorithm • Two components • Tunnel Group Generation - allocates BW to FGs using bandwidth functions to prioritize at bottleneck edges • Tunnel Group Quantization – changes split ratios in each TG to match granularity supported by switch tables

  21. TE Optimization Algorithm (2) • Tunnel Group Generation Algorithm • Allocates BW to FGs based on demand and priority • Allocates edge capacity among FGs based on BW function • Receive either equal fair share or fully satisfy their demand • Preferred tunnel for FG is minimum cost path that does not include bottleneck edge • Algorithm terminates when each FG is satisfied or no preferred tunnel exists

  22. TE Optimization Algorithm (3) • Tunnel Group Quantization • Adjusts splits to granularity supported by underlying hardware • Is equivalent to solving integer linear programming problem • B4 uses heuristics to maintain fairness and throughput efficiency • Example • Split above allocation in multiples of 0.5 • Problem (a) 0.5:0.5 • Problem (b) 0.0:1.0

  23. TE Protocol • B4 switches operate in three roles: • Encapsulating switch initiates tunnels and splits traffic between them • Transit switch forwards packets based on outer header • Decapsulating switch terminates tunnels and then forwards packets using regular routes • Source site switches implement FGs • Switch maps packets to FG when their destination IP adr matches one of the prefixes associated with FG • Each incoming packet hashes to one of Tunnels associated with TG in desired ratio • Each site in tunnel path maintains per-tunnel forwarding rules • Source site switches encapsulate packet with outer IP header (tunnel-ID) whose destination IP address uniquely identifies tunnel • Installing tunnel requires configuring switches at multiple sites

  24. TE Protocol Example

  25. Coordination between Routing and TE • B4 supports both • Shortest path routing and • TE routing protocol • This is very robust solution since if TE gets disabled, underlying routing continues working • We require support for multiple forwarding tables • At OpenFlow level, we use RAP, mapping different flows and groups to appropriate HW tables – routing/BGP populates LPM (Last Prefix Match) table with appropriate entries • AT TE level, we use Access Control List (ACL) table to set desired forwarding behavior

  26. Role of Traffic Engineering Database (TED) • TE server coordinates T/TG/FG rule installation across multiple OFCs. • TE optimization output is translated to per-site TED • TED captures state needed to forward packets along multiple paths • Each OFC uses TED to set forwarding states at individual switches • TED maintains key-value store for global tunnels, tunnel groups, and flow groups • TE operation (TE op) can add/delete/modify exactly one TED entry at one OFC

  27. Other TE Issues • What are some of the other issues with TE? • How would you synchronize TED between TE and OFC? • What are other issues related to reliability of TE, OFC, TED, etc? • ….

  28. Impact of Failures • Traffic between two sites • Measurements of duration of any packet loss after six types of events • Findings: • Failure of transit router that is neighbor to encap router is bad – very long convergence • Reason? • TE server does not incur any loss • Reason?

  29. Utilization Link utilization (effectiveness Of hashing) Site-to-site edge utilization

  30. Conclusions – Experience from Outage • Scalability and latency of packet IO path between OFA and OFC is critical • Why? • How would you remedy problems? • OFA should be asynchronous and mutli-threaded for more parallelism • Why? • Loss of control connectivity between TE and OFC does not invalidate forwarding state • Why not? • TE must be more adaptive to failed/unresponsive OFCs when modifying TGs that depend on creating new Tunnels • Why? • What other issues do you see with B4 design?