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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.

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reducing energy consumption of disk storage using power aware cache management

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

Application Servers

Storage Servers

Web Servers

SAN

router

switch

Database Servers

Data Centers: Service-based Computing
energy problem faced by data centers
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 Model
  • Disk power modes
    • Active/idle/standby/sleep
    • Spinup/down cost
    • Breakeven time
  • Metrics
    • Energy consumption
    • Average response time
disk power management schemes
Disk Power Management Schemes
  • Oracle scheme (off-line)
  • Practical scheme (on-line)

IdleTime > BreakEvenTime

access2

access1

Idle for BreakEvenTime

Wait time

current research status
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
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
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
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

mode 0

mode 1

mode 2

mode 3

E(T)

t1

t2

t3

T

Breakeven-Time for Multiple Power Modes

Active mode

Energy Consumption

Spinup cost

Idle Period Length

is belady s algorithm energy optimal
Is Belady’s Algorithm Energy-Optimal?
  • Belady’s algorithm: performance-optimal
    • Minimize the number of misses
    • Evicting the block with the longest future reference distance
  • Answer: NO!
    • Only consider the access sequence
    • Ignore requests’ arrival time
    • Ignore multiple disk scenario
a simple example

A

B

Belady’s algorithm

power-aware algorithm

A Simple Example

A

B

t

Disk 0

C

C

D

An energy-optimal algorithm using dynamic programming

off line power aware greedy algorithm

A

B

C

D

A

E

B

F

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

how to calculate energy penalty of evicting a block

E(EB)

E(DA)

E(AE)

E(BF)

Energy Penalty (B)

=

+

E(EF)

A

How to Calculate Energy Penalty of Evicting a Block

-

Energy Penalty (A)

=

+

E(DE)

-

A

B

C

D

E

B

F

D E F: bound-to-happen misses

re view

mode 0

mode 1

mode 2

mode 3

t1

t2

t3

Re-view

Energy Consumption

Idle Period Length

on line power aware algorithm

energy saving

energy penalty

t3

t1

t2

On-line Power Aware Algorithm

mode 3

  • Idea: selectively keep blocks from inactive disks in the cache for a longer time
    • Make “inactive disks” more inactive

Energy Saving

mode 2

Super Linear

<<

mode 1

mode 0

t4

Idle Period Length

how to measure disk activeness
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
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%
how to keep track of the distribution of idle period lengths
How to Keep Track of the Distribution of Idle Period Lengths?

Idle Period Length

Histogram-based estimation

case study pa lru
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 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
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
Energy (OLTP)

OPG: energy saving 2% - 9% over Belady’s algorithm

PA-LRU: energy saving 16% over LRU

average response time oltp
Average Response Time (OLTP)

OPG: 4% better than belady’s algorithm

PA-LRU: 50% better than LRU (avoid expensive spinup)

conclusion
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
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?
slide29

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

offline energy optimal algorithm
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 algorithm32
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:

simulation results cello96
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

A

B

OPG is heuristic

A

B

C

D

E

D E: bound-to-happen misses

a step further
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 computing38
Data Centers: Service-based Computing

Web

Servers

Database

Servers

Storage

Servers

Ethernet

SAN

Internet

Local

Storage