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Resource management in the cloud

Доклад Анны Мелеховой на CodeFreeze

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Resource management in the cloud

  1. 1. Resource management in the cloud DRS
  2. 2. 2 Introduction Lead developer in Parallels DRS enthusiast, the leader in resource management team in Odin Over 10 years experience in virtualization Lecturer In MIPT, Innopolis, Sbertech
  3. 3. 3 Ресурс шедулер
  4. 4. 4 Распределитель ресурсов
  5. 5. 5 Ресурсоуправитель !
  6. 6. 6 Resource scheduler
  7. 7. 7 Resource scheduler •  N resource producers (servers) •  M resource consumers (virtual instances: VMs, containers, jobs)
  8. 8. 8 Resource scheduler •  Estimate current resource consuming level for each consumer( demand(i) ) •  Predict resource consumption level for some consumer( future_demand(i) ) •  Detect provisor overload (bottleneck(j) ) •  Optimal placement ( place(i,j) -> place(i,k) ) •  Victim selection •  Migration estimation (possibility, migration weight) •  N resource producers (servers) •  M resource consumers( virtual instances: VMs, containers, jobs)
  9. 9. 9 Estimate demand
  10. 10. 10 Estimate demand. Memory "  OS halts when it doesn’t need CPU "  OS doesn’t make I/O when it doesn’t need external devices "  OS does nothing when it doesn’t need memory
  11. 11. 11 Predict demand
  12. 12. 12 Predict demand •  Math methods •  ARIMA, regression •  Kalman & other filters •  CuSum •  RL •  Markov chains •  Bayes •  Clusterization •  Metrics •  Provisioning metrics (virtualization) •  Consumer metrics (Guest OS) •  Job/user metrics •  Time •  When to predict •  Prediction horizon (prediction accuracy) •  Lifetime prediction?
  13. 13. 13 Detect overload
  14. 14. 14 Detect overload "  Heartbeat "  Virtual resource usage zones "  Complicated resource producer metrics
  15. 15. 15 Placement (1) •  Initial (w/o estimation) •  Single •  Group •  Dynamic (with estimation) •  SLA and affinity issues •  VM-to-VM, VM-to-host •  Affinity & anti-affinity •  Resource availability
  16. 16. 16 Placement (2). Inter-consumer dependencies
  17. 17. 17 Placement (3). Geography-aware
  18. 18. 18 Placement (4). … after all filters, restrictions A set of suitable target nodes. Which to choose?
  19. 19. 19 Placement (4). … after all filters, restrictions •  Random •  Round-robin •  Spread-first •  Greedy (fill-first)
  20. 20. 20 Migration •  Could be too long (need prediction) •  Could affect performance •  Could affect energy consumption •  Could be more or less effective •  Could fail
  21. 21. 21 Use cases: Vmware(1) The cost is modeled b migrate. The migratio of the VM as well as h migration. If the VM d they must be copied DRS keeps track of the times for each VM an memory size, it calcu copying. During this t required for the migr vMotion cost is in the The vMotion process source and destinatio then computes how m due to migration. Thi writes to its memory DRS approximates thAlgorithm 1: DRS Load Balancing
  22. 22. 22 Use cases: Vmware(2) how the workload might change on both the source and destination and if this move still makes sense when the workload changes. Figure 4: Overview of DRS Cost-Benet analysis cost-benefit analysis
  23. 23. 23 Resource scheduler. Administrative •  Limits + Shares + Reservations •  Grouping of resource suppliers (clusters) •  Nested resource pool •  Custom rules (affinity and anti-affinity) •  Automative, semi-automative, manual mode •  Migration threshold
  24. 24. 24 Resource scheduler. Virtualization case •  High availablility •  Failover •  Backup •  DPM
  25. 25. 25 Resource scheduler. Main principles •  Do not migrate too often •  Random is a good placement algorithm •  Choose accurately counters for prediction and estimation •  Maths is a pleasure!
  26. 26. 26 Questions
  27. 27. 27 Thank you for you attention! annam@odin.com

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