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Reducing Energy Consumption of Disk Storage Using Power Aware Cache Management
Qingbo Zhu, Francis M. David, Christo F. Deveraj, Zhenmin Li, Yuanyuan Zhou
Department of Computer Science University of Illinois at Urbana-Champaign
Pei Cao**Cisco Systems Inc.
HPCA’04
02/17/2004
Data Centers: Service-based
Computing
router
Web Servers
Application Servers
Database Servers
switch
…
…
…
SAN
…
Storage Servers
Energy Problem Faced by Data Centers
Data centers High electricity bills: up to 25% TCO
$8M per year for a 30,000-square-foot data center [EERE news 2003]
Increase as much as 25% annually [Energy User News 2002]
Storage 27% of the total energy consumed
[Maximum Inc. 2002]
Disk Power Model
Disk power modes Active/idle/standby/sleep Spinup/down cost Breakeven time
Metrics Energy consumption Average response time
Disk Power Management Schemes
Oracle scheme (off-line)
Practical scheme (on-line)
access1 access2
IdleTime > BreakEvenTime
Idle for BreakEvenTime Wait time
Current Research Status
The idle periods in server workloads are too short to justify high spinup/down cost of server disks [ISCA’03][ISPASS’03] [ICS’03]
IBM Ultrastar 36Z15 -- 135J/10.9s Multi-speed disk model [ISCA’03]
RPMs: multiple intermediate power modes Smaller spinup/down costs Be able to save energy for server workloads
Most previous work assume that all requests go directly to physical disks
Observation
Many requests are filtered out by the storage cache
EMC Symmetrix storage system Up to 128GB storage cache
IBM ESS system Up to 64GB storage cache
Cache replacement and write policies affect the access sequences to physical disks Block-based
storage system
The Focus of Our Paper
Power-aware off-line and on-line cache replacement algorithms and write policies reduce the disk energy consumption
Clarification The underlying disk power management
scheme is NOT changed The storage cache is always active
Outline
Motivation Power aware cache management
Belady’s algorithm is NOT energy-optimal Off-line power-aware greedy algorithm On-line power-aware algorithm Four write policies
Simulations Conclusion Limitations and future work
Breakeven-Time for Multiple Power Modes
En
erg
y
Con
sum
pti
on
Idle Period Length
mode 0 mode 1 mode 2
mode 3
Spinup cost
Active mode
t1 t3t2 T
E(T)
Is Belady’s Algorithm Energy-Optimal?
Belady’s algorithm: performance-optimal Minimize the number of misses Evicting the block with the longest future refere
nce distance Answer: NO!
Only consider the access sequence Ignore requests’ arrival time Ignore multiple disk scenario
A Simple Example
t
A
B
BAC
Disk 0
D
An energy-optimal algorithm using dynamic programming
Belady’s algorithm
power-aware algorithm
C
Off-line Power-Aware Greedy Algorithm
Idea: evicting the block with the smallest energy penalty Observation: take advantage of the knowledge about future’s bound-to-happen
misses Cold misses Capacity misses due to previous evictions
D E F: bound-to-happen misses
A
B
BC E FAD
How to Calculate Energy Penalty of Evicting a Block
D E F: bound-to-happen misses
A
B
BC E FD A
E(DE)E(AE)E(DA)+ -Energy Penalty (A)
=
E(EF)E(BF)E(EB) +Energy Penalty (B)
= -
Re-viewEn
erg
y
Con
sum
pti
on
Idle Period Length
mode 0 mode 1 mode 2
mode 3
t1 t3t2
On-line Power Aware Algorithm
Idea: selectively keep blocks from inactive disks in the cache for a longer time
Make “inactive disks” more inactive
Idle Period Length
En
erg
y
Savin
g
mode 0
mode 1
mode 2
mode 3
Super Linear
t1
t2
t4
t3
energy saving
energy penalty
<<
How to Measure Disk Activeness?
Characteristics of inactive disks Small percentage of cold misses Large idle period lengths with high
probability
How to Keep Track of Cold Misses?
Bloom Filter: a space-efficient membership test method
A vector v of m bits k independent hash functions ranging {1..m} Given an access for block a, check the bits at
position
If any of them is 0, a is cold miss and then set all bits 1
Otherwise, it is not a cold miss though we may be wrong
1.6M blocks with v = 2M bytes and k = 7 the accuracy is 99.18%
)(),...,(),( 21 ahahah k
How to Keep Track of the Distribution of Idle Period Lengths?
Histogram-based estimation
Idle Period Length
Case Study: PA-LRU
Applies to all cache replacement algorithms LRU, 2Q, MQ etc.
PA-LRU Two LRU stacks
LRU0: blocks from active disks LRU1: blocks from inactive disks Evict blocks from LRU0 first
The evaluation of disk activeness is epoch-based Adapt to workload changes
Write Policy Write back Write through Write back with eager updates (WBEU)
Eagerly write back all the dirty blocks when the target disk becomes active due to a read miss
Write through with deferred updates (WTDU) Use a log disk which is always active Write the blocks to the log disk if the target disk is
not active Flush back all the logged blocks when the target disk
becomes active due to a read miss Retain persistent semantics
Evaluation Methodology
Experiment setup DiskSim:
IBM Ultrastar 36Z15 Enhanced by a multi-speed
disk power model Enhanced by a CacheSim
Real system traces: OLTP Cello96
Synthetic traces: Exponential distribution Pareto distribution
Energy (OLTP)
OPG: energy saving 2% - 9% over Belady’s algorithm
PA-LRU: energy saving 16% over LRU
0
0.2
0.4
0.6
0.8
1
Infinite size Belady OPG LRU PA-LRU
Practical Oracle
Average Response Time (OLTP)
OPG: 4% better than belady’s algorithm
PA-LRU: 50% better than LRU (avoid expensive spinup)
0
0.2
0.4
0.6
0.8
1Infinite size Belady OPG LRU PA-LRU
Practical
Conclusion
Power aware cache management plays an important role on disk energy consumption Belady’s algorithm is NOT energy-optimal Evict the blocks with small energy penalty Make inactive disks more inactive
Future Work and Acknowledgements
Limitations and future work Design online algorithms for a single disk as well Take prefetching into account Real system experiments
Acknowledgements Anonymous reviewers Professor Lenny Pitt (UIUC) CMU Parallel Data Lab (for DiskSim) HP Lab (for Cello Trace)
Questions?
Thanks!
Backup Slides
Write Policies (Exponential Distribution)
Write back: up to 20% saving than write through
WBEU: up to 60% saving than write through
WTDU: up to 55% saving than write through
0
0.2
0.4
0.6
0.8
1Write through Write back WBEU WTDU
Practical
Energy-optimal problem
Offline Energy-optimal Algorithm Only two power state
1: active mode 0: standby mode
Virtual time Only one disk Parameters:
b: the number of disk blocks k: the number of cache blocks n: the input size m: threshold
Cache State (C, t, i) The cache contains the blocks in set C
after the first i+1 references and the last t consecutive reference were ache hit
Offline energy optimal algorithm
Minimize energy: maximize the time the disk can spend in standby mode
A(C,t,i): the maximum time that the disk spends in the standby mode until (C,t,i) is reached
Dynamic programming: Extend to multiple
disks:
Time Breakdown
Mean Inter-arrival Time
Simulation Results: Cello96
OPG: energy saving 5% - 7% over belady’s algorithm
PA-LRU: energy saving 2% - 3%
Cello96: high cold miss ratio, larger than 65% for all disks
OPG is heuristic
D E: bound-to-happen misses
A
B
BC ED A
A Step Further…
Consider both miss ratio and energy penalty
Idea: don’t differentiate among blocks whose energy penalty is smaller than a threshold T energy penalty smaller than T: round up to T T=0: pure greedy algorithm T is large enough: belady’s algorithm
Data Centers: Service-based Computing
Internet WebServers
DatabaseServers
LocalStorage
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