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Hedera : Dynamic Flow Scheduling for Data Center Networks

Hedera : Dynamic Flow Scheduling for Data Center Networks. Mohammad Al-Fares, Sivasankar Radhakrishnan Barath Raghavan Nelson Huang Amin Vahdat. Presented by Xuzi Zhou. Outline. Introduction Architecture of Hedera Bandwidth Demand Estimation Flow Scheduling Evaluation

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Hedera : Dynamic Flow Scheduling for Data Center Networks

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  1. Hedera: Dynamic Flow Scheduling for Data Center Networks Mohammad Al-Fares, SivasankarRadhakrishnan BarathRaghavan Nelson Huang Amin Vahdat Presented by Xuzi Zhou

  2. Outline Introduction Architecture of Hedera Bandwidth Demand Estimation Flow Scheduling Evaluation Conclusion CS 685 Fall 2013 Paper Presentation

  3. Introduction Unknown workloads Static resource allocation is insufficient Required to support traditional software and protocols e.g. Ethernet, TCP/IP Inter-rack computing Virtualization technology Different network topology Path multiplicity Properties of Data Center Networks CS 685 Fall 2013 Paper Presentation

  4. Introduction Core Aggregation Edge Common Multi-rooted Hierarchical Network Topology CS 685 Fall 2013 Paper Presentation

  5. Introduction Core Aggregation Pod Edge Fat-tree Network Topology CS 685 Fall 2013 Paper Presentation

  6. Introduction Data center applications require significant aggregate bandwidth [C]How to optimize the use of the current multi-path network topology? [P] Hash-based flow placement (e.g. ECMP) is insufficient Challenge & Problem CS 685 Fall 2013 Paper Presentation

  7. Introduction Equal Cost Multipath (ECMP) D S Multiple equal cost paths towards core switches One corresponding path down to the destination Flows are randomly allocated to a path according to the hash of the flow CS 685 Fall 2013 Paper Presentation

  8. Introduction Equal Cost Multipath (ECMP) D S Problems: Agnostic to current network utilization May cause collisions between “elephant” flows An “elephant” flow: large and long-lived CS 685 Fall 2013 Paper Presentation

  9. Introduction Collisions of “Elephant” Flows D S D S Problems: Agnostic to current network utilization May cause collisions between “elephant” flows An “elephant” flow: large and long-lived CS 685 Fall 2013 Paper Presentation

  10. Introduction Dynamic Network Scheduling D S D S Move flows from busy paths to relatively idle ones Move flows with higher bandwidth demand first How to measure the bandwidth demand? Current Bandwidth ≠ Bandwidth Demand ! CS 685 Fall 2013 Paper Presentation

  11. Introduction Hedera A dynamic flow scheduling system for data centers Goal: Maximize aggregate network utilization Main Design: Collect flow information Compute non-conflicting paths for flows Instruct switches with new routing rules CS 685 Fall 2013 Paper Presentation

  12. Outline Introduction Architecture of Hedera Bandwidth Demand Estimation Flow Scheduling Evaluation Conclusion CS 685 Fall 2013 Paper Presentation

  13. Architecture Detect Large Flows Estimate Flow Demand & Compute Good Paths Estimate flow demand according to max-min fairness Persist for some time Bandwidth demand beyond limit Configure Switches with the Computed Paths Insert new flow entries into edge and aggregate switches CS 685 Fall 2013 Paper Presentation

  14. Outline Introduction Architecture of Hedera Bandwidth Demand Estimation Flow Placement Algorithms Evaluation Conclusion CS 685 Fall 2013 Paper Presentation

  15. Bandwidth demand estimation Two types of flows: Host-limited flows Limited by host processing Network-limited flows Limited by the capacity of NICs We focus on network-limited flows only Estimate bandwidth demand using max-min fairness CS 685 Fall 2013 Paper Presentation

  16. Bandwidth demand estimation A flow’s bandwidth demand is limited by the workload of both its sender and receiver The Algorithm: Compute a demand matrix iteratively : Sender equally distributes bandwidth among outgoing flows Receiver decrease exceeded demand equally among incoming flows F: the set of source and destination pairs for large flows Guaranteed to converge in O(|F|) time CS 685 Fall 2013 Paper Presentation

  17. Bandwidth demand estimation Example A B C D CS 685 Fall 2013 Paper Presentation

  18. Bandwidth demand estimation Example A B C D CS 685 Fall 2013 Paper Presentation

  19. Bandwidth demand estimation Example A B C D CS 685 Fall 2013 Paper Presentation

  20. Bandwidth demand estimation Example A B C D CS 685 Fall 2013 Paper Presentation

  21. Bandwidth demand estimation Example A B C D CS 685 Fall 2013 Paper Presentation

  22. Bandwidth demand estimation Example A B C D CS 685 Fall 2013 Paper Presentation

  23. Bandwidth demand estimation Example A B C D CS 685 Fall 2013 Paper Presentation

  24. Bandwidth demand estimation Example A B C D CS 685 Fall 2013 Paper Presentation

  25. Bandwidth demand estimation Example A B C D CS 685 Fall 2013 Paper Presentation

  26. Bandwidth demand estimation Example A B C D CS 685 Fall 2013 Paper Presentation

  27. Outline Introduction Architecture of Hedera Bandwidth Demand Estimation Flow Placement Algorithms Evaluation Conclusion CS 685 Fall 2013 Paper Presentation

  28. Flow Placement Algorithms Find a good allocation of paths for the set of large flows, such that the average bisection bandwidth of the flows is maximized Two examined algorithms: Global First Fit Linearly search all paths for a path with enough capacity The flow is placed on the first one found Simulated Annealing Iteratively find a globally better placement of flows Close to optimal CS 685 Fall 2013 Paper Presentation

  29. Flow Placement Algorithms Global First Fit D S CS 685 Fall 2013 Paper Presentation

  30. Flow Placement Algorithms Global First Fit D S CS 685 Fall 2013 Paper Presentation

  31. Flow Placement Algorithms Simulated Annealing Annealing: A heat treatment that slowly cools a material to increase its ductility Simulated Annealing: Performs probabilistic searches to efficiently compute paths for flows Settle slowly CS 685 Fall 2013 Paper Presentation

  32. Flow Placement Algorithms Simulated Annealing State s: A set of mappings from destination hosts to core switches Energy E: Total exceeded capacity over all the links in the current state Temperature T: The remaining number of iterations before termination Probability P for transition from current state s to neighbor state sn: Neighbor(s): Swap assigned core switches for a pair of hosts in a pod in state s CS 685 Fall 2013 Paper Presentation

  33. Flow Placement Algorithms Simulated Annealing CS 685 Fall 2013 Paper Presentation

  34. Flow Placement Algorithms Simulated Annealing CS 685 Fall 2013 Paper Presentation

  35. Outline Introduction Architecture of Hedera Bandwidth Demand Estimation Flow Placement Algorithms Evaluation Conclusion CS 685 Fall 2013 Paper Presentation

  36. Evaluation Experiment Setup Hedera is tested on both physical testbed and simulator Hedera is tested with various traffic patterns Three flow scheduling methods, ECMP, Global First-Fit, and Simulated Annealing, are tested side by side along with an ideal network Ideal network: hosts are connected by a non-blocking gigabit Ethernet switch CS 685 Fall 2013 Paper Presentation

  37. Evaluation Experiment Results – Testbed Benchmark CS 685 Fall 2013 Paper Presentation

  38. Evaluation Experiment Results – Testbed Data Shuffle CS 685 Fall 2013 Paper Presentation

  39. Evaluation Experiment Results – Simulator Benchmark CS 685 Fall 2013 Paper Presentation

  40. Evaluation Experiment Results – Quality of Simulated Annealing CS 685 Fall 2013 Paper Presentation

  41. Evaluation Experiment Results – Complexity of Hedera CS 685 Fall 2013 Paper Presentation

  42. Outline Introduction Architecture of Hedera Bandwidth Demand Estimation Flow Placement Algorithms Evaluation Conclusion CS 685 Fall 2013 Paper Presentation

  43. Conclusion Hedera focuses on placeing large flows onto non-conflicting paths Hedera with simulated annealing placement algorithm delivers near-optimal aggregate bandwidth ECMP works well with small flows CS 685 Fall 2013 Paper Presentation

  44. QUESTIONS? CS 685 Fall 2013 Paper Presentation

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