Reducing Energy Consumption of Disk Storage Using Power-Aware Cache Management

1. Reducing Energy Consumption of Disk Storage Using Power-Aware Cache Management Q. Zhu, F. David, C. Devaraj, Z. Li, Y. Zhou, P. Cao* University of Illinois at Urbana Champaign & *Cisco Systems Inc. HPCA ‘04 Presented by: Justin Kliger & Scott Schneider

2. Motivation • Reduce Energy Consumption • Targeting large data center such as EMC Symmetrix (right) • 10-50 TBytes • 128 GB of non-volatile memory cache

3. Motivation • Dealing with large data storage and large caches separated from application servers

4. Motivation • Consume huge amounts of power: • Currently consuming 150-200 W/ft2 • Expect an increase up to 25% annually • Storage devices already account for 27% of power consumption at data center • Significance of reducing energy consumption: • Can limit costs to these data centers • Keeps energy costs from becoming prohibitive and preventing data center expansion • Positive environmental impacts

5. Motivation • Focusing on cache replacement algorithm, conserve energy by changing the average idle time of disks • Create Power-Aware algorithm • Designate priority disks to allow some disks to greatly increase idle time • Selectively keep blocks in cache from priority disks to decrease power usage

6. Outline • Background for Disk Power Model • Off-line analysis • Online algorithm • Evaluation & Results • Write Policies • Conclusion, Impact & Further Research

7. Disk Power Model • Conventional disks have three states • Active and Idle consume full power • Standby consumes less power, but requires a spin up to satisfy a request • Gurumuthi et al. proposed multi-speed disks • Lower rotational speeds consume less energy • Transition from lower speed to next higher speed is smaller than switching from standby to active

8. Disk Power Model • Their disk model uses these proposed multi-speed disks • Multi-speed disks can be configured to service requests at all speeds or only the highest speed • Only servicing requests at highest speed makes the disks essentially multi-state disks, as opposed to two-state disks • Their model uses 4 intermediate lower power modes

9. Disk Power Management • Oracle disk power management (DPM) • Term for when entire sequence is known ahead of time, perfect power management is possible • Provides upper bound on energy saved • Upon request completion • Oracle DPM examines interval length t between requests • If t is greater than break-even time, spin disk down immediately

10. The minimum energy consumption is the curve that intersects with the consumption line for each state Online algorithms uses these crossover points as thresholds Disk Power Management

11. DPM and Cache Replacement Disk Disk Disk Disk Disk Disk Disk

12. DPM and Cache Replacement DPM DPM DPM DPM DPM Disk Disk Disk Disk Disk Disk Disk

13. DPM and Cache Replacement Cache Replacement Policy DPM DPM DPM DPM DPM Disk Disk Disk Disk Disk Disk Disk

14. Power-Aware Off-line algorithms • Optimal cache-hit algorithm (Belady’s) can be suboptimal for power-consumption [Figure 3: An example showing Belady’s algorithm is not energy-optimal] 6 misses; 24 time units at high energy consumption

15. Power-Aware Off-line algorithms • Optimal cache-hit algorithm (Belady’s) can be suboptimal for power-consumption [Figure 3: An example showing Belady’s algorithm is not energy-optimal] 7 misses; 16 time units at high energy consumption

16. Power-Aware Off-line Algorithms • Energy Optimal Algorithm • Developed polynomial-time algorithm with dynamic programming, but not applicable to aiding online algorithm, details not included • Off-line Power-Aware Greedy Algorithm (OPG) • More realistic, used as comparison in traces • Considers future deterministic misses

17. Power-Aware Off-line Algorithms OPG: • Evicts blocks with minimum energy penalty: • OL(Li) + OL(Fi) – OL(Li + Fi) = Practical DPM • LE(Li) + LE(Fi) – LE(Li + Fi) = Oracle DPM • Time complexity is O(n2) • Heuristic because only considers the current set of deterministic misses

18. Power-Aware Online Algorithm • Insight gained from off-line analysis: avoid evicting blocks with large energy penalties • Small increases in idle time can have big energy gains

19. Online Approach • Online algorithm goals • Use the cache replacement policy to reshape each disk’s access pattern • Give priority to blocks from inactive disks to increase average idle time • Allow average interval time of some disks to decrease so that interval time of already idle disks can increase • Overall energy consumption is reduced

20. Other Online Factors • Potential energy savings are also determined by • Percentage of capacity misses must be high in a workload; cold misses can’t be avoided • Distribution of accesses determine actual interval lengths; larger deviation from the mean has more opportunity for savings • An online algorithm needs to identify these properties for each disk to make good decisions

21. Tracking Cold Misses • Use a Bloom Filter to track cold misses • allocate a vector v of m bits • k independent hash functions, h1, h2, ..., hk

22. Tracking Cold Misses • Use a Bloom Filter to track cold misses • allocate a vector v of m bits • k independent hash functions, h1, h2, ..., hk • Example, m = 13, k = 4 h1() h2() h3() h4() 0 0 0 0 0 0 0 0 0 0 0 0 0

23. Tracking Cold Misses • Use a Bloom Filter to track cold misses • allocate a vector v of m bits • k independent hash functions, h1, h2, ..., hk • Example, m = 13, k = 4 a h1(a) h2(a) h3(a) h4(a) 0 0 0 0 0 0 0 0 0 0 0 0 0

24. Tracking Cold Misses • Use a Bloom Filter to track cold misses • allocate a vector v of m bits • k independent hash functions, h1, h2, ..., hk • Example, m = 13, k = 4 a h1(a) h2(a) h3(a) h4(a) 0 0 0 0 0 0 0 0 0 0 0 0 0

25. Tracking Cold Misses • Use a Bloom Filter to track cold misses • allocate a vector v of m bits • k independent hash functions, h1, h2, ..., hk • Example, m = 13, k = 4 a h1(a) h2(a) h3(a) h4(a) 0 1 0 0 1 0 1 0 0 1 0 0 0

26. Tracking Cold Misses • Use a Bloom Filter to track cold misses • allocate a vector v of m bits • k independent hash functions, h1, h2, ..., hk • Identifying a cold miss is always correct • False positives are possible for non-cold misses • For 1.6M blocks with v set to 2M and k = 7, false positives happen 0.82% of the time

27. Distribution Estimate • Disk access distribution is estimated using an epoch-based histogram technique • We use an approximation method to estimate the cumulative distribution function of interval length of a disk, F(x) = P[X ≤ x]

28. Distribution Estimate • In each epoch • Track interval length between consecutive disk accesses • Each interval falls into a discrete range • The sum of each range and all ranges above it approximates the cumulative probability below that range

29. Power Aware Cache Management • Dynamically track cold misses and cumulative distribution of interval lengths for each disk • Classify disks, each epoch, as • Priority disks have a “small” percentage of cold misses and large interval lengths with a “high” probability • Regular disks are the rest

30. Power Aware Cache Management • The basic idea: reshape the access pattern to keep priority disks idle • PA can be combined with other algorithms such as LIRS, ARC, MQ, etc.

31. Example: PA-LRU • PA-LRU employs two LRU stacks • LRU0 keeps regular disk blocks • LRU1 keeps priority disk blocks • Blocks are evicted from bottom of LRU0 first, then bottom of LRU1 • Parameters • α is the cold misses threshold • p is the cumulative probability • β is the CDF threshold • epoch length

32. Overall Design Cache Replacement Policy DPM DPM DPM DPM DPM Disk Disk Disk Disk Disk Disk Disk

33. Overall Design PA Classification Engine Cache Replacement Algorithm DPM DPM DPM DPM DPM Disk Disk Disk Disk Disk Disk Disk

34. Evaluation • Specifications of disk: • Additionally, 4 more low-speed power modes: 12000RPM, 9000RPM, 6000RPM, 3000RPM

35. Evaluation • Two System traces: OLTP & Cello96 • Cache Size: • For OLTP = 128 MBytes • For Cello96 = 32 MBytes

36. Evaluation • Compared 4 algorithms: • Belady’s, OPG, LRU, PA-LRU • Also measured disk energy consumption with infinite cache size – provides lower bound (only cold misses access disk) • Practical DPM use thresholds identified earlier as competitive with Oracle • PA-LRU uses 4 parameters: • Epoch length = 15 minutes, α= 50%, p = 80%, β= 5 seconds

37. Results • PA-LRU can save 16% more energy than LRU on OLTP trace • However, PA-LRU only saves 2-3% more energy than LRU for Cello96 trace

38. Results How PA-LRU improves performance: 4 14 = PA-LRU

39. Write Policies • Four write policies • WB: Write-Back only writes dirty blocks upon eviction • WT: Write-Through writes dirty blocks immediately • WBEU: Write-Back with Eager Updates writes a block immediately if that disk is active; otherwise it waits • WTDU: Write-Through with Deferred Update writes dirty blocks to a log if the target disk is in a low power mode

40. Write Policies Evaluation • Synthetic traces that varied write/read ratios and the interarrival time • WB vs. WT Write-Back consistently better up to 20% • WBEU vs. WT WBEU consistently better up to 65% • WTDU vs. WT WTDU consistently better up to 55%

41. Conclusion • Effective analysis for off-line algorithm (OPG) • Designed and evaluated Power-Aware online algorithm (PA-LRU), which can use 16% less energy than LRU • Considered write policy effects on energy savings

42. Impact of work • Theoretical off-line analysis of power-aware caching policies • Identification of requirements for an online power-aware caching algorithm • Published in 2004; 3 self citations plus Pinheiro, Bianchini ICS 04 and Papathanasiou, Scott USENIX 04

43. Further Research • Reduce # of parameters (PB-LRU) • Online algorithm applied to single disks • Prefetching • Consider storage cache energy consumption • Evaluate in real storage system context