Towards VM Consolidation Using Idle States

Transcription

1 Towards Consolidation Using Idle States Rayman Preet Singh, Tim Brecht, S. Keshav University of EE 15 1

2 Traditional Consolidation Hypervisor Hardware Hypervisor Hardware Hypervisor Hardware Re-package and save Power(idle machine) > 50% of peak power [Gandhi 09] 2

3 Consolidating Further ore inactive states Frozen [LXC] Substrate [Wang 11], Fast-resume [Zhang 11] Booted Inactive 3 1 Inactive 2

4 Example: DreamServer Booted Suspended Web-hosting Density improvement: over 46% iss penalty: ~1 sec [Knauth et al. DreamServer: Truly On-Demand Cloud Services. SYSTOR 14] 4

5 Question Goals aximize density inimize average miss penalties What policy should we adopt to manage s across the different inactive states? 5

6 Low Duty-cycle Workloads High idle times Relatively uncorrelated active times 25 % duty-cycle 1 2 Only a small fraction simultaneously active Large fraction inactive 6

7 Low Duty-cycle Workloads Notable examples Web hosting [Knauth 14] Personal servers [Elsmore 12, ortier 10] Cyber-foraging [Satyanarayanan 09, Ha 13] App EE 7

8 Problem Formulation Booted Inactive 1 T i,0 Inactive i B i Inactive N Find policy P aximize #s Average miss penalty(p) < Limit 8

9 Policy-based Resource Provisioning ulti-level cache management Eviction iss penalty = F(hit rates) Exclusive caching L1 L2 L3 emory Disk emory Hierarchy Hierarchy (LXC) Booted Frozen Suspended 9

10 Policy-based Resource Provisioning Page replacement Writeback Pinned page eviction active duration Temporal locality Reactive (demand-based) vs. Proactive (prefetching) ain emory Booted Frozen Swap space Suspended 10

11 Demand-driven Reactive Policies LRU, NRU, Second-chance, Clock, Optimal policy Belady s IN optimal demand policy [Aho et al. 71] Unknown for multi-state hierarchy[gill et al. 08] ain emory Booted Frozen Swap space Suspended 11

12 Reactive Policies: Lower Bound Booted Inactive i T i,0 B i iss penalty T i,0 Total miss penalty(p, ω) Σ h i.t i,0 Lower bound on Σ h i.t i,0 Lower bound on miss penalty 12

13 Prefetching Proactive Policies Further reduce #page faults Optimal: DPIN [Trivedi et al. 76] Time ain emory Swap space 13

14 Proactive Policy: Sliding Window Request Idle T next B 0 B 1 B 2 Time B 0 Online implementation Predict T next : next arrival per e.g., using ARA B 1 B 2 14

15 easuring odel Parameters odel input Transition times (T i,j ) State capacities (B i ) Experiments Sensitivity analysis Density analysis Example virtualization solution: LXC Open source, ainstream Linux, CCC, Dockr States: booted, suspended, frozen [enage et al. 07] B F S Experiment setup Server machine: 24 cores 3.46 GHz, 128 GB RA 15

16 easuring odel Parameters Suspended-to-booted vs. #Booted s Transition to Booted Time (ms) t 2 t 1 t 2 t 1 t 2 t 1 t 2 t 1 t 2 t 1 t 2 t 1 t 2 t 1 t 2 t 1 t 2 t Number of Booted s 16

17 easuring odel Parameters Frozen-to-booted v/s #Booted s Similar behavior with #frozen s Identify bottlenecks to LXC density Frozen-to-booted (t 1 ) Time (ms) Number of Booted s 17

18 odel Parameters for LXC ean-value analysis Similar analysis for other virtualization solutions Stochastic-value analysis 18

19 How do different policies effect miss penalty? Reactive vs. Lower bound vs. Proactive 19

20 Policy Evaluation Sample low duty cycle workload: personal servers Topic of active research [Shakimov et al. 11, Elsmore et al. 12, Ha et al. 13, Singh et al. 13, Gupta et al. 14] Request inter-arrivals and durations Inter- arrival,me Rela>vely fixed Stochas-c Stochas-c Dura,on Rela>vely fixed Rela>vely fixed Stochas-c achine generated requests vs. User-generated Periodic data uploads, ISs, cloud-offloading 20

21 Fixed Inter-arrivals + Fixed Duration Avg iss Penalty (in ms) Suspended-to-booted Frozen-to-booted density 21

22 Fixed Inter-arrivals + Fixed Duration Avg iss Penalty (in ms) Suspended-to-booted Frozen-to-booted in Φ (ω) / ω density 22

23 Fixed Inter-arrivals + Fixed Duration Avg iss Penalty (in ms) Suspended-to-booted Frozen-to-booted in Φ (ω) / ω LRU density 23

24 Fixed Inter-arrivals + Fixed Duration Avg iss Penalty (in ms) Suspended-to-booted Frozen-to-booted in Φ (ω) / ω LRU SlidingWindow+ARA SlidingWindow+Ground Truth density 24

25 Stochastic Inter-arrivals + Stochastic durations Avg iss Penalty (in ms) Suspended-to-booted Frozen-to-booted density Datasets: Newton et al. 13, Arlitt et al

26 Stochastic Inter-arrivals + Stochastic durations Avg iss Penalty (in ms) Suspended-to-booted Frozen-to-booted in Φ (ω) / ω density Datasets: Newton et al. 13, Arlitt et al

27 Stochastic Inter-arrivals + Stochastic durations Avg iss Penalty (in ms) Suspended-to-booted Frozen-to-booted in Φ (ω) / ω LRU density Datasets: Newton et al. 13, Arlitt et al

28 Stochastic Inter-arrivals + Stochastic durations Avg iss Penalty (in ms) Suspended-to-booted Frozen-to-booted in Φ (ω) / ω LRU SlidingWindow+Ground Truth density Datasets: Newton et al. 13, Arlitt et al

29 Stochastic Inter-arrivals + Stochastic durations Avg iss Penalty (in ms) Suspended-to-booted Frozen-to-booted in Φ (ω) / ω LRU SlidingWindow+Ground Truth SlidingWindow+ARA density Datasets: Newton et al. 13, Arlitt et al

30 Policy Evaluation Proactive vs. Reactive If miss penalties are small => use proactive, else reactive Proactive works well for relatively predictable workloads Upto 2.2 density, 1 ms miss penalty 30

31 Conclusion State-based consolidation improves density Legacy compatible, can leverage transient idleness Imperative to keep miss penalty low Optimize policies (control plane) and mechanisms Future work: lots! heterogeneity capacity, SLAs,.. Workloads, bottlenecks,.. Native integration of inactive states 31