International Conference on Autonomic Computing 2005 1 Governor: Autonomic Throttling for Aggressive...

International Conference on Autonomic Computing 2005

1

Governor: Autonomic Throttling for Aggressive Idle Resource Scavenging

Jonathan Strickland (1)Vincent Freeh (1)Xiaosong Ma (1 & 2)Sudharshan Vazhkudai (2)

(1) Department of Computer Science, NC State Univ. (2) Mathematics and Computer Science Division, Oak Ridge National Laboratory

International Conference on Autonomic Computing 2005 2

Presentation Roadmap Introduction Model and approach System implementation Performance results Conclusion and future work


Aggregating Desktop Computer Resources

Personal computers pervasive Easily updated and well equipped Under-utilized

Consolidate scattered resources by resource scavenging (resource stealing)

Computing resources Condor, Entropia SETI@home, Folding@home Creating massive compute power

Storage resources Farsite, Kosha, FreeLoader Aggregate distributed spaces into

shared storage

(Courtesy: SETI@home)

(Courtesy: Folding@home)


Impact on Workstation Owners Foremost concern of resource donors

Security and privacy impact Virtual machine/sandbox solutions

Performance impact Existing approaches often too conservative “Stop” approach

Stop scavenging when user activity detected Unable to utilize small pieces of idle time Does not overlap scavenging with native workload

Priority-based approach Works for cycle-stealing Implicit, “best-effort” Range and granularity limited by operating system


Objectives and Contributions Goal: systematic performance impact control

framework Contributions: Governor

Explicit, quantified approach toward performance impact control

Extensible framework for arbitrary scavenging applications and native workloads

User-level, OS-independent implementation Evaluation with two types of scavenging applications


System Entities Active on donated workstations

Resource scavenging application (scavenger) Native workload Governor process

Controls execution of scavenger Limits impact on native workload to target level α (e.g.,

20%)


Performance Impact Performance impact

Caused by resource scavenging application on workstation owner’s native workload

Metrics: slow-down factor(Timescavenged – Timeoriginal) / Timeoriginal

May not reflect resource owner perceived impact

Main approach: resource throttling Throttle level (β, 0<=β<1)

Timescavenging / Timetotal

Major challenge: to select appropriate β value


Impact Benchmarking Characterize scavenger S against system resources Native workload as combination of resource consumption

components Resource vector

R = (r1, r2, …, rn) Benchmark vector

B = (B1, B2, …, Bn) Measure S’ impact on Bi with various throttle levels

Store impact curve Calculate target throttle level βi with given impact level α


Native Workload Monitoring Native workloads typically complex and dynamic Online workload monitoring

Activate corresponding β when non-trivial native resource consumption detected

Resource trigger vector Т = (τ1, τ2, …, τn)

For each resource Ri

βi’ =

Overall β = min (β11’ , , β22

’ , , … … βnn’ ) )

Picking most restrictive β across resources

βi, if consumption ≥ τi

1, if consumption < τi


Governor Architecture

scavenger

systemresourcesResource

vectors

0. impactbenchmarking

1. monitorresource activity

2. computeoverall

3. throttlescavenger

Governor

Usertarget

Adaptive Extensible and generic


Dynamic Throttling Mechanism Fixed throttle interval “I”

1 second in our implementation Within each I, Governor

Runs scavenger application for β*I Monitors native workload during (1-β)*I Adjust β for next I

0.2 …

Scavenger phases Monitoring phases

β=0.5

I

β=0.3

I I

β=0.6


Resource Usage Monitoring and Triggers

At beginning and end of each monitoring phase (1-β)*I Monitor resource usage

CPU: /proc/stat (cycles) Disk: /proc/partitions (blocks) Network: /proc/net/dev (bytes)

Triggers (τ array)

Resource Trigger value (τ)

τ CPU1% utilization

τ IO0

τ network0


Applications, Benchmarks, and Configurations Scavenger applications

SETI@home Search for signals in slices of radio telescope data Computation-intensive

FreeLoader Prototype for aggregating storage in LAN environments I/O- and network-intensive

Single-resource benchmarks CPU: EP from NAS benchmark suite I/O: large sequential file read Network: repeated downloading with wget

Linux workstation 2.8GHz Pentium 4, 512MB memory, 80GB disk


Impact Benchmarking Results

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

1.4

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

Beta

Impa

ct

cpudisk readsnetwork requests

-0.5

0

0.5

1

1.5

2

2.5

3

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

Beta

Impa

ct

cpudisk readsnetwork requests

SETI FreeLoader

Resource Impact level (α)0.05

0.10

0.20

0.25

βCPU 0.02 0.05 0.10 0.2

βIO 1.0 1.0 1.0 1.0

βnetwork 1.0 1.0 1.0 1.0

Resource Impact level (α)0.05

0.10

0.20

0.25

βCPU 0.30 0.40 0.70 0.90

βIO 0.05 0.10 0.20 0.25

βnetwork 0.10 0.20 0.30 0.50


Multi-resource Workload: Kernel Compile

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

Beta

Impa

ct

SETI FreeLoader

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

BetaSc

aven

ger P

erfo

rman

ce

SETI FreeLoader

Impact on native workload

Impact on scavenger app.


Synthetic Composite Workload Simulate common intermittent user activities

Short sleep time between operations Writing 80MB data to file Browsing arbitrary directories in search of file Compressing data written previously and send via

networks Browsing more directories Removing files written

Takes about 150 seconds without concurrent user load


Composite Exec. Time and Impact

Resource Impact level (α)

0.0 0.05 0.10 0.20 0.25 1.0

SETI@home % impact

1420%

1484.0%

1548.4%

16818.5%

18026.8%

26183.8%

FreeLoader% impact

1420%

1505.6%

15710.6%

17221.1%

18026.8%

21148.6%

Combine impact benchmarking results with real-time monitoring of composite workload

Governor closely approximates target performance impact (α)


Comparison with Priority Based Method (SETI@home)

20

40

60

80

100

120

140

160

150 160 170 180 190 200 210 220 230 240 250 260 270

Workload execution time (s)

Idea

l SET

I com

plet

ion

time

(s) nice

Governor


Comparison with Priority Based Method (FreeLoader)

22.5

33.5

44.5

55.5

66.5

7

150 160 170 180 190 200 210 220

Workload execution time (s)

Idea

l SET

I com

plet

ion

time

(s) nice

Governor


Conclusion and Future Work Governor: extensible framework for quantitative performance

impact control Contains actual performance impact Proactively consume idle resources Self-adaptive OS-independent and low-overhead

Future work Connect impact control with user interfaces Studying memory resource throttling Evaluating with more scavengers


Resource Utilization and β for Composite

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