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

Motivation

Reduce Energy Consumption

Targeting large data center such as EMC Symmetrix (right) 10-50 TBytes 128 GB of non-volatile

memory cache

Motivation

Dealing with large data storage and large caches separated from application servers

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

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

Outline

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

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

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

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

Disk Power Management

The minimum energy consumption is the curve that intersects with the consumption line for each state

Online algorithms uses these crossover points as thresholds

DPM and Cache Replacement

Disk Disk DiskDisk DiskDisk Disk

DPM and Cache Replacement

Disk Disk DiskDisk DiskDisk Disk

DPM DPM DPM DPM DPM

DPM and Cache Replacement

Disk Disk DiskDisk DiskDisk Disk

DPM DPM DPM DPM DPM

Cache Replacement Policy

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

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

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

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

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

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

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

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

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

0 0 0 0 0 0 0 0 0 0 0 0 0

h1() h2() h3() h4()

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

0 0 0 0 0 0 0 0 0 0 0 0 0

h1(a) h2(a) h3(a) h4(a)

a

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

0 0 0 0 0 0 0 0 0 0 0 0 0

h1(a) h2(a) h3(a) h4(a)

a

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

0 1 0 0 1 0 1 0 0 1 0 0 0

h1(a) h2(a) h3(a) h4(a)

a

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

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]

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

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

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.

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

Overall Design

Disk Disk DiskDisk DiskDisk Disk

DPM DPM DPM DPM DPM

Cache Replacement Policy

Overall Design

Disk Disk DiskDisk DiskDisk Disk

DPM DPM DPM DPM DPM

PA Classification Engine Cache Replacement Algorithm

Evaluation

Specifications of disk:

Additionally, 4 more low-speed power modes: 12000RPM, 9000RPM, 6000RPM, 3000RPM

Evaluation Two System traces: OLTP & Cello96

Cache Size: For OLTP = 128 MBytes For Cello96 = 32 MBytes

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

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

Results

How PA-LRU improves performance:

= PA-LRU

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

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%

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

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

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