Conserving Energy in RAID Systems with Conventional Disks

1. Conserving Energy in RAID Systems with Conventional Disks Dong Li, Jun Wang Dept. of Computer Science & Engineering University of Nebraska-Lincoln Peter Varman Dept. of Electrical and Computer Engineering Rice University

2. References [1] S. Gurumurthi, A. Sivasubramaniam, M. Kandemir, and H. Franke, “DRPM: dynamic speed control for power management in server class disks,” ISCA’03 [2] D. Colarelli and D. Grunwald, “Massive arrays of idle disks for storage archives,” in Proceedings of Super Computing’ 2002 [3] E. Pinheiro and R. Bianchini, “Energy conservation techniques for disk array-based servers,” in Proceedings of the 18th International Conference on Supercomputing, 2004 [4] E. Varki, A. Merchant, J. Z. Xu, and X. Z. Qiu, “Issues and challenges in the performance analysis of real disk arrays,” IEEE Transactions on Parallel and Distributed Systems, 2004. [5] D. Li and J. Wang, “EERAID: Energy-efficient redundant and inexpensive disk array,” in Proceedings of 11th ACM SIGOPS European Workshop, 2004. [6] D. Li, H. Cai, X. Yao, and J. Wang, “Exploiting redundancy to construct energy-efficient, high-performance RAIDs,” Tech. Rep. TR-05-07-04, Computer Science and Engineering Department, University of Nebraska Lincoln, 2005.

3. Outline • Introduction • Motivation • eRAID Design • Evaluation • Leveraging eRAID • Conclusions

4. Introduction Motivation Design Leveraging Conclusions Evaluation Introduction • Energy-efficient storage system, total cost of ownership (TCO), … • Short request inter-arrival time • Long disk state switch time of conventional disks • Current solutions: multi-speed disks[1] • Create long idle period for conventional disks • unbalance workloads • Two approaches • Relocating data: MAID[2], PDC[3] • Redirecting requests: EERAID[5]

5. Introduction Motivation Design Leveraging Conclusions Evaluation Motivation • Major limitations of state of the art • few workable solutions for conventional disk based systems • single performance measurement • no differentiation of workload time criticality • Three observations • redundant information of RAID systems • spare service capacity • queueing model

6. Introduction Motivation Design Leveraging Conclusions Evaluation eRAID Design • Main idea • spin down, partially or entirely, mirror disks to standby • read, write • Features • soft solution --- no hardware change • consider two performance metrics • Research issue • maximize energy saving • without violating predefined performance degradation limits for both throughput and response time • assume workloads have little change between two consecutive time windows

7. Introduction Motivation Design Leveraging Conclusions Evaluation Solving for Performance Degradation • Our approach: using queueing models to do predictions • model RAID-1 system and get performance measures • examine how the input parameters are changed • get new performance measures with changed input parameters • compare these two results • Four workloads: synchronous read (SR), asynchronous read (AR), synchronous write (SW) and asynchronous write (AW) • Real system: HP SureStore E Disk Array FC60

8. Introduction Motivation Design Leveraging Conclusions Evaluation Read Load Models

9. Introduction Motivation Design Leveraging Conclusions Evaluation Read Load Performance Computing • The possible changes of input parameters: • disk access probability • disk service time --- negligible • Synchronous read load: • Mean Value Analysis (MVA) technique • eRAID --- double access probabilities of corresponding primary disks • Asynchronous read load: • no throughput degradation for stable systems • eRAID --- double work loads of corresponding primary disks

10. Introduction Motivation Design Leveraging Conclusions Evaluation Write Load Model • Controller cache • write back policy • FC60: two-threshold write back policy • destage_threshold, max_ditry • Disk array: M/M/1/K queueing model[4]

11. Introduction Motivation Design Leveraging Conclusions Evaluation Write Load Performance Computing • Dirty data arrival rate d • SW load: d= * cache_miss_rate • : max throughput with infinite cache size • AW load: d= * cache_miss_rate •  independent with the system • The possible changes of input parameters: • service rate: N/2 => (N-2i)/2 • maximum queue length • cache miss rate --- unnoticeable

12. Ebase = Eactive+Eidle N disks EeRAID= Eactive+Eidle+Estandby+Eswitch (N-i) disks i disks Introduction Motivation Design Leveraging Conclusions Evaluation Solving for Energy Saving • N-disk RAID1 • Time window length T • Request number R • Mean service time t • Asyn. load: 2=1 • Sync. load: 2<1

13. Introduction Motivation Design Leveraging Conclusions Evaluation Control Algorithm • Time-window • Solve multi-constraint problem: • select LFU disks • Conservative control

14. Introduction Motivation Design Leveraging Conclusions Evaluation Evaluation • Disk power model: IBM Ultrastar 36Z15 • Simulator: augmented Disksim • Traces: Cello99 and TPC-C20 • 8-disk RAID1 system • Two scenarios

15. Introduction Motivation Design Leveraging Conclusions Evaluation Preliminary Results

16. Introduction Motivation Design Leveraging Conclusions Evaluation Leveraging eRAID • Associate a load threshold f (1/2<f<1) for each disk • when primary disk load exceeds f, spin up mirror disk to share the load • conventional mirrored layout: spin up one mirror disk • our new layout: spin up less than one mirror disk • Layout files of one primary disk to a set of mirror disks

17. Introduction Motivation Design Leveraging Conclusions Evaluation An example: N=10 and f=2/3

18. Introduction Motivation Design Leveraging Conclusions Evaluation Conclusions • An energy saving policy, eRAID, for conventional disk based RAID-1 systems • 30% energy-saving without violating predefined performance constraints • A new data layout scheme for further energy-saving • Limitations • circumscribed by the accuracy of queueing models • approximated input parameters, e.g. process number and mean process delay • conservative control

19. Thank you! Questions?

20. Creating Disk Idle Period in RAID-5: An Example • 4-disk RAID 5 system • A parity group containing data stripe 1, 2, 3 and parity stripe p that are saved in disk 1, 2, 3 and 4 respectively • There is a read request for stripe 1. To service such a read, we could either read stripe 1 from disk 1, or read stripe 2, 3 and p, then calculate stripe 1 on the fly by an XOR calculation. • More details can be found in our technical report[6]