1. U NIVERSITY OF M ASSACHUSETTS, A MHERST – Department of Computer Science Quantifying the Benefits of Resource Multiplexing in On-Demand Data Centers Abhishek Chandra Prashant Shenoy UMASS Amherst Pawan Goyal IBM Almaden, San Jose

2. U NIVERSITY OF M ASSACHUSETTS, A MHERST – Department of Computer Science 2 Motivation On-demand Data Centers Server farms Rent computing and storage resources to applications Revenue for meeting application workload levels Goals: Satisfy dynamically changing application requirements Maximize resource utilization of the platform Robustness against “Slashdot” effects

3. U NIVERSITY OF M ASSACHUSETTS, A MHERST – Department of Computer Science 3 Dynamic Resource Allocation Existing techniques: Oceano [Appleby01], HP Utility Data Center [Rolia00], MUSE [Chase01], COD [Doyle02], SHARC [Uragaon02] Differ in allocation policies and mechanisms Common features: Periodically re-allocate resources among applications Estimate workloads for near future Statistical multiplexing of resources Question: Which techniques work best and when?

4. U NIVERSITY OF M ASSACHUSETTS, A MHERST – Department of Computer Science 4 On-demand Allocation: Practical Issues How often and how fine should the re-allocation be done? How well can the application requirements be estimated? How much “head room” should be allowed to absorb transient loads? Do large number of customers lead to better statistical multiplexing?

5. U NIVERSITY OF M ASSACHUSETTS, A MHERST – Department of Computer Science 5 Talk Outline Motivation System Model and Metrics Performance Study Conclusions and Future Work

6. U NIVERSITY OF M ASSACHUSETTS, A MHERST – Department of Computer Science 6 System Model Cluster of servers Homogeneous pool of resources No constraints on application placement Time granularity (Δt): Period of re-allocation E.g.: re-allocate once every minute, hour, day Space granularity (Δs): Resource allocation unit E.g: re-allocate partial/whole server, server group

7. U NIVERSITY OF M ASSACHUSETTS, A MHERST – Department of Computer Science 7 Optimal Resource Allocation Infinitesimally small allocation granularity Allocates precise amount of resource No resource wastage Time Resource Allocation R opt

8. U NIVERSITY OF M ASSACHUSETTS, A MHERST – Department of Computer Science 8 Practical Resource Allocation Time Resource Allocation Allocation done periodically and in fixed quanta Fixed resource allocation for next period Clairvoyant scheme: Predict peak application requirements for the next allocation period ΔtΔt ΔsΔs

9. U NIVERSITY OF M ASSACHUSETTS, A MHERST – Department of Computer Science 9 Capacity Overhead Time Resource Allocation ρ R opt R pract

10. U NIVERSITY OF M ASSACHUSETTS, A MHERST – Department of Computer Science 10 Performance Study Workload: 3 e-commerce traces 24-hour long WorkloadNumber of Requests Avg. Request Size Peak bit-rate Ecommerce11,194,1373.95 KB458.1 KB/s Ecommerce21,674,6723.85 KB1631.0 KB/s Ecommerce3251,3527.24 KB1346.9 KB/s

11. U NIVERSITY OF M ASSACHUSETTS, A MHERST – Department of Computer Science 11 Effect of Allocation Granularity Time granularity Space granularity  Fine time scale with reasonably fine resource unit desirable

12. U NIVERSITY OF M ASSACHUSETTS, A MHERST – Department of Computer Science 12 Effect of Prediction Inaccuracy Fine allocation is better even with inaccurate prediction

13. U NIVERSITY OF M ASSACHUSETTS, A MHERST – Department of Computer Science 13 Effect of Overprovisioning Finer allocation achieves same “head room” with less overhead

14. U NIVERSITY OF M ASSACHUSETTS, A MHERST – Department of Computer Science 14 Effect of Number of Customers Large number of customers provide more opportunity for statistical multiplexing

15. U NIVERSITY OF M ASSACHUSETTS, A MHERST – Department of Computer Science 15 Data Center Architectures Dedicated Allocation of whole servers Typical reallocation in order of 30 minutes Shared Fractional server resources Reallocation in seconds or minutes Fast Reallocation Reserved server pools, remote booting Reallocation in a few minutes

16. U NIVERSITY OF M ASSACHUSETTS, A MHERST – Department of Computer Science 16 Comparison of Architectures Data Center Configuration Number of customers Optimal Reqmt (Num of servers) Dedicated Architecture (Num of servers) Fast Reallocation (Num of servers) Shared Architecture (Num of servers) Small320343125 Medium15100388304148 Large301000501737591739

17. U NIVERSITY OF M ASSACHUSETTS, A MHERST – Department of Computer Science 17 Implications and Opportunities Cost of re-allocation Partial server: ~1 syscall/min Full server: Rebooting, disk scrubbing, etc. Virtual machines: Low cost of reallocation with encapsulation Prediction: Work-conserving scheduler at fine time-scales Accurate prediction possible at minutes, hours

18. U NIVERSITY OF M ASSACHUSETTS, A MHERST – Department of Computer Science 18 Conclusions and Future Work Dynamic Resource Allocation for data centers Fine allocation granularity desirable Even with inaccurate prediction To achieve more “head room” Large number of customers lead to higher multiplexing benefits Future Work: Effect of affinity, placement constraints Re-allocation overhead Stability of resource allocation