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Resource management in the cloud
- 1. Resource management inthe cloud DRS
- 2. 2 Introduction Lead developer inParallels DRS enthusiast, the leader in resource management team in Odin Over 10 years experience in virtualization Lecturer In MIPT, Innopolis, Sbertech
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- 7. 7 Resource scheduler • Nresource producers (servers) • M resource consumers (virtual instances: VMs, containers, jobs)
- 8. 8 Resource scheduler • Estimatecurrent 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)
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- 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
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- 12. 12 Predict demand • Mathmethods • 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?
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- 14. 14 Detect overload " Heartbeat " Virtual resource usage zones " Complicated resource producer metrics
- 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 Placement (2). Inter-consumerdependencies
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- 18. 18 Placement (4). …after all filters, restrictions A set of suitable target nodes. Which to choose?
- 19. 19 Placement (4). …after all filters, restrictions • Random • Round-robin • Spread-first • Greedy (fill-first)
- 20. 20 Migration • Could betoo long (need prediction) • Could affect performance • Could affect energy consumption • Could be more or less effective • Could fail
- 21. 21 Use cases: Vmware(1) Thecost 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 Use cases: Vmware(2) howthe 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 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 Resource scheduler. Virtualizationcase • High availablility • Failover • Backup • DPM
- 25. 25 Resource scheduler. Mainprinciples • Do not migrate too often • Random is a good placement algorithm • Choose accurately counters for prediction and estimation • Maths is a pleasure!
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- 27. 27 Thank you foryou attention! annam@odin.com