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improving the Scalability of Data Center Networks with Traffic-aware Virtual Machine Placement

B99705021 資管三 李奕德 http://ppt.cc/41rH. improving the Scalability of Data Center Networks with Traffic-aware Virtual Machine Placement. Outline . Introduction Background Virtual machine placement Algorithm Algorithm evaluation Result Discussion and future work. introduction.

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improving the Scalability of Data Center Networks with Traffic-aware Virtual Machine Placement

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  1. B99705021 資管三 李奕德 http://ppt.cc/41rH improving the Scalability of Data Center Networks with Traffic-aware Virtual Machine Placement

  2. Outline • Introduction • Background • Virtual machine placement • Algorithm • Algorithm evaluation • Result • Discussion and future work

  3. introduction • Scalability issue • Aim to solve different problem - Dcell, Bcube, PortLand, VL2…… • No thinking of traffic issue - high traffic from end to end

  4. introduction • three character of all traffic • average pairwise traffic rate & end-to-end cost has low correlation • Uneven between VMs • Stays almost the same • Traffic-aware placement may be beneficial

  5. introduction • Traffic-aware VM Placement Problem (TVMPP) • given: traffic matrix , cost matrix • Goal: minimize cost • Cost can be: Total switch used/Compute Time • An algorithm that solve the NP-hard problem • Architecture difference

  6. NP- hard • NP: by nondeterministic algorithms in polynomial time • nondeterministic -Every “guess by hunch” is right • at least as hard as the hardest problems in NP

  7. Outline • Introduction • Background • Virtual machine placement • Algorithm • Algorithm evaluation • Result • Discussion and future work

  8. Background – traffic analysis • Data set I : • IBM Global Services’ data warehouse • About 17000 virtual machines • Data set II: • Server cluster • About Hundreds of virtual machines • round-trip latency measurement at 68 VM

  9. Background- traffic analysis • Uneven between VMs • 80% of VM’s traffic < 800kb/sec • 4% of VM’s traffic > 8mb/sec

  10. Background- traffic analysis • Stays almost the same

  11. Background- traffic analysis • Low correlation between average pairwise traffic rate & end-to-end cost • Correlation : -0.32

  12. Background - Achitecture • Old style

  13. Background - Achitecture • VL2

  14. Background - Achitecture • Portland • Bcube

  15. Outline • Introduction • Background • Virtual machine placement • Algorithm • Algorithm evaluation • Result • Discussion and future work

  16. Virtual machine placement- cost function • n VM to assign • n slot for VM • static and single-path routing • Cost and traffic matrix from historical data

  17. Virtual machine placement- cost function • is equivalent of finding • Dummy VM is assigned when no. slot > no. VM

  18. Virtual machine placement- complexity • Quadratic Assignment Problem (NP-hard) • Impossible to find optimality when size > 15 • TVMPP is a special case of QAP • reduction from Balanced Minimum K-cut Problem (BMKP) • BMKP: extended problem from the Minimum Bisection Problem (MBP) • BMKP & MBP are NP-hard

  19. Outline • Introduction • Background • Virtual machine placement • Algorithm • Algorithm evaluation • Result • Discussion and future work

  20. Algorithm • approximation algorithm Cluster-and-Cut • Divide VM into VM cluster • Divide slot into slot cluster • Put VM cluster into slot cluster • A smaller problem • Feasible when size is sufficient small

  21. Algorithm – pseudo code

  22. Algorithm – pseudo code

  23. Algorithm - complexity • Complexity determine by SlotClustering and VMMinKcut • Slotclustering: O(nk) • VMMinKcut: O(n4) • Total complexity = O(n4)

  24. Outline • Introduction • Background • Virtual machine placement • Algorithm • Algorithm evaluation • Result • Discussion and future work

  25. Algorithm evaluation- cluster and cut • Cluster and cut VS. other benchmark algorithms • Local Optimal Pairwise Interchange (LOPI) • Simulated Annealing (SA) • hybrid traffic model • Gravity model • compute the GLB for each settings

  26. Algorithm evaluation - result

  27. Outline • Introduction • Background • Virtual machine placement • Algorithm • Algorithm evaluation • Result • Discussion and future work

  28. Result • Cost matrix • Compare with random assign

  29. Result • Traffic is assumed to be in normal distribution • Variance is change to show difference • Different architecture & variance affect result

  30. Result • View as VM cluster • GLB prediction

  31. Result • GLB prediction VS. optimal solution

  32. conclusion • Thing that brings better performance: - bigger variance - smaller cluster (less VM in a group) - Architecture difference (generally) Bcube > tree > fat-tree > VL2 • Good scenario: multiple service in a data center • Bad scenario: single service / map-reduce

  33. Outline • Introduction • Background • Virtual machine placement • Algorithm • Algorithm evaluation • Result • Discussion and future work

  34. Discussion and future • Dynamic VM placement • Other VM placement with different goal

  35. Thank you for your attention Q&A

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