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Dynamic Scheduling of Network Updates Xin Jin Hongqiang Harry Liu, Rohan Gandhi, Srikanth Kandula, Ratul Mahajan, Ming Zhang, Jennifer Rexford, Roger Wattenhofer

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SDN: Paradigm Shift in Networking Many benefits – Traffic engineering [B4, SWAN] – Flow scheduling [Hedera, DevoFlow] – Access control [Ethane, vCRIB] – Device power management [ElasticTree] 1 Controller Direct, centralized updates of forwarding rules in switches

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Network Update is Challenging Requirement 1: fast – The agility of control loop Requirement 2: consistent – No congestion, no blackhole, no loop, etc. 2 Network Controller

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What is Consistent Network Update 3 Current State A D C B E F1: 5 F4: 5 F2: 5 F3: 10 Target State A D C B E F1: 5 F4: 5 F2: 5 F3: 10

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What is Consistent Network Update Asynchronous updates can cause congestion Need to carefully order update operations 4 Current State A D C B E F1: 5 F4: 5 F2: 5 F3: 10 Target State A D C B E F1: 5 F4: 5 F2: 5 F3: 10 A D C B E F4: 5 F1: 5 F3: 10 F2: 5 Transient State

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Existing Solutions are Slow Existing solutions are static [ConsistentUpdate’12, SWAN’13, zUpdate’13] – Pre-compute an order for update operations 5 F3 F4 F2 F1 Static Plan A Target State Current State A D C B E F4: 5 F2: 5 F1: 5 F3: 10 A D C B E F1: 5 F4: 5 F2: 5 F3: 10

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Existing Solutions are Slow Existing solutions are static [ConsistentUpdate’12, SWAN’13, zUpdate’13] – Pre-compute an order for update operations Downside: Do not adapt to runtime conditions – Slow in face of highly variable operation completion time 6 F3 F4 F2 F1 Static Plan A

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Operation Completion Times are Highly Variable 7 >10x Measurement on commodity switches

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Operation Completion Times are Highly Variable 8 Measurement on commodity switches Contributing factors – Control-plane load – Number of rules – Priority of rules – Type of operations (insert vs. modify)

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Static Schedules can be Slow 9 F3 F4 F2 F1 Static Plan B F3 F4 F2 F1 Static Plan A F1 F2 F3 F4 Time 123 4 5 F1 F2 F3 F4 Time 123 4 5 F1 F2 F3 F4 Time 123 4 5 F1 F2 F3 F4 Time 123 4 5 Target State Current State A D C B E F4: 5 F2: 5 F1: 5 F3: 10 A D C B E F1: 5 F4: 5 F2: 5 F3: 10 No static schedule is a clear winner under all conditions!

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Dynamic Schedules are Adaptive and Fast 10 F3 F4 F2 F1 Static Plan B F3 F4 F2 F1 Static Plan A Target State Current State A D C B E F4: 5 F2: 5 F1: 5 F3: 10 A D C B E F1: 5 F4: 5 F2: 5 F3: 10 No static schedule is a clear winner under all conditions! F3 F4 F2 F1 Dynamic Plan Adapts to actual conditions!

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Challenges of Dynamic Update Scheduling Exponential number of orderings Cannot completely avoid planning 11 Current State A D C B E F3: 5 F2: 5 F1: 5 F4: 5 F5: 10 Target State A D C B E F1: 5 F3: 5 F4: 5 F5: 10 F2: 5

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Challenges of Dynamic Update Scheduling Exponential number of orderings Cannot completely avoid planning 12 Current State A D C B E F3: 5 F1: 5 F4: 5 F5: 10 Target State A D C B E F1: 5 F3: 5 F4: 5 F5: 10 F2: 5 F2 Deadlock

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Challenges of Dynamic Update Scheduling Exponential number of orderings Cannot completely avoid planning 13 Current State A D C B E F3: 5 F2: 5 F4: 5 F5: 10 Target State A D C B E F1: 5 F3: 5 F4: 5 F5: 10 F2: 5 F1 F1: 5

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Challenges of Dynamic Update Scheduling Exponential number of orderings Cannot completely avoid planning 14 Current State A D C B E F2: 5 F4: 5 F5: 10 Target State A D C B E F1: 5 F3: 5 F4: 5 F5: 10 F2: 5 F1F3 F1: 5 F3: 5

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Challenges of Dynamic Update Scheduling Exponential number of orderings Cannot completely avoid planning 15 Current State A D C B E F4: 5 F5: 10 Target State A D C B E F1: 5 F3: 5 F4: 5 F5: 10 F2: 5 F1F3F2 Consistent update plan F2: 5 F1: 5 F3: 5

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Dionysus Pipeline 16 Dependency Graph Generator Update Scheduler Network Current State Target State Consistency Property Encode valid orderings Determine a fast order

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Dependency Graph Generation 17 Dependency Graph Elements Operation node Resource node (link capacity, switch table size) Node Edge Dependencies between nodes Path node Current State A D C B E F3: 5 F2: 5 F1: 5 F4: 5 F5: 10 Target State A D C B E F1: 5 F3: 5 F4: 5 F5: 10 F2: 5

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Dependency Graph Generation 18 Current State A D C B E F3: 5 F2: 5 F1: 5 F4: 5 F5: 10 Target State A D C B E F1: 5 F3: 5 F4: 5 F5: 10 F2: 5 Move F3 Move F1 Move F2

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Dependency Graph Generation 19 Current State A D C B E F3: 5 F2: 5 F1: 5 F4: 5 F5: 10 Target State A D C B E F1: 5 F3: 5 F4: 5 F5: 10 F2: 5 A-E: 5 Move F3 Move F1 Move F2

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Dependency Graph Generation 20 Current State A D C B E F1: 5 F4: 5 F5: 10 A-E: 5 5 Move F3 Move F1 Move F2 Target State A D C B E F1: 5 F3: 5 F4: 5 F5: 10 F2: 5 F3: 5 F2: 5

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Dependency Graph Generation 21 A-E: 5 5 5 Move F3 Move F1 Move F2 Target State A D C B E F1: 5 F3: 5 F4: 5 F5: 10 F2: 5 Current State A D C B E F3: 5 F2: 5 F1: 5 F4: 5 F5: 10

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Dependency Graph Generation 22 A-E: 5 5 5 5 Move F3 Move F1 Move F2 Target State A D C B E F1: 5 F3: 5 F4: 5 F5: 10 F2: 5 Current State A D C B E F3: 5 F2: 5 F1: 5 F4: 5 F5: 10

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Dependency Graph Generation 23 A-E: 5 5 D-E: 0 5 5 5 5 Move F3 Move F1 Move F2 Target State A D C B E F1: 5 F3: 5 F4: 5 F5: 10 F2: 5 Current State A D C B E F3: 5 F2: 5 F1: 5 F4: 5 F5: 10

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Dependency Graph Generation Supported scenarios – Tunnel-based forwarding: WANs – WCMP forwarding: data center networks Supported consistency properties – Loop freedom – Blackhole freedom – Packet coherence – Congestion freedom Check paper for details 24

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Dionysus Pipeline 25 Dependency Graph Generator Update Scheduler Network Current State Target State Consistency Property Encode valid orderings Determine a fast order

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Dionysus Scheduling Scheduling as a resource allocation problem 26 A-E: 5 5 D-E: 0 5 5 5 5 Move F3 Move F1 Move F2

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Dionysus Scheduling Scheduling as a resource allocation problem 27 A-E: 0 D-E: 0 5 5 5 5 Move F3 Move F1 Move F2 Deadlock!

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Dionysus Scheduling Scheduling as a resource allocation problem 28 A-E: 0 5 D-E: 0 5 5 5 Move F3 Move F1 Move F2

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Dionysus Scheduling Scheduling as a resource allocation problem 29 A-E: 0 5 D-E: 5 5 5 Move F3 Move F2

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Dionysus Scheduling Scheduling as a resource allocation problem 30 A-E: 0 5 5 Move F3 Move F2

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Dionysus Scheduling Scheduling as a resource allocation problem 31 A-E: 5 5 Move F2

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Dionysus Scheduling Scheduling as a resource allocation problem 32 Move F2 Done!

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Dionysus Scheduling Scheduling as a resource allocation problem NP-complete problems under link capacity and switch table size constraints Approach – DAG: always feasible, critical-path scheduling – General case: covert to a virtual DAG – Rate limit flows to resolve deadlocks 33

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Critical-Path Scheduling Calculate critical-path length (CPL) for each node – Extension: assign larger weight to operation nodes if we know in advance the switch is slow Resource allocated to operation nodes with larger CPLs 34 A-B:5 CPL=3 C-D:0 5 CPL=1 5 5 5 5 CPL=2 CPL=1 Move F4 Move F3 Move F2 Move F1

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Critical-Path Scheduling Calculate critical-path length (CPL) for each node – Extension: assign larger weight to operation nodes if we know in advance the switch is slow Resource allocated to operation nodes with larger CPLs 35 A-B:0 CPL=3 C-D:0 5 CPL=1 5 5 5 CPL=2 CPL=1 Move F4 Move F3 Move F2 Move F1

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Handling Cycles Convert to virtual DAG – Consider each strongly connected component (SCC) as a virtual node 36 CPL=1 CPL=3 weight = 1weight = 2 Critical-path scheduling on virtual DAG – Weight w i of SCC: number of operation nodes A-E: 5 5 D-E: 0 5 5 5 5 Move F3 Move F1 Move F2 Move F2

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Handling Cycles Convert to virtual DAG – Consider each strongly connected component (SCC) as a virtual node 37 CPL=1 CPL=3 weight = 1weight = 2 Critical-path scheduling on virtual DAG – Weight w i of SCC: number of operation nodes A-E: 0 5 D-E: 0 5 5 5 Move F3 Move F1 Move F2 Move F2

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Evaluation: Traffic Engineering 38 Not congestion-free Congestion-free

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Evaluation: Traffic Engineering 39 Improve 50 th percentile update speed by 80% compared to static scheduling (SWAN), close to OneShot Not congestion-free Congestion-free

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Evaluation: Failure Recovery 40

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Evaluation: Failure Recovery 41 Reduce 99 th percentile link oversubscription by 40% compared to static scheduling (SWAN) Improve 99 th percentile update speed by 80% compared to static scheduling (SWAN)

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Conclusion Dionysus provides fast, consistent network updates through dynamic scheduling – Dependency graph: compactly encode orderings – Scheduling: dynamically schedule operations 42 Dionysus enables more agile SDN control loops

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Thanks! 43

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