Low Power QoS Scheduling for Heterogeneous Wireless Sensor ...

Low Power QoS Scheduling for Heterogeneous Wireless Sensor ...

Energy Management in Virtualized Environments
Gaurav Dhiman, Giacomo Marchetti, Raid Ayoub, Tajana Simunic Rosing (CSE-UCSD)

Inside
Inside Xen
Xen Hypervisor
Hypervisor

Motivations
Motivations and
and Goals
Goals

Online Learning Algorithm

Performs dynamic evaluation of a set of DPM and DVFS
policies
at run time and selects the best suited for the current
workload
Guarantees convergence and performance close to that
of the best
available policy in the set

Working Set

Expert 1

Expert 2

Expert 3

Expert selection

Manages Power
Device

DVFS
DVFS

Expert N

:Dormant Experts

Controller
:Operational Expert

Scheduler
-

Virtualization

Lower datacenter energy
Virtual Machine Power Oriented
Scheduling
consumption
Workload migration across
Handle non-stationary
physical machine
workloads

Minimize impact on performance

Service
VM
Customization
Online Learning Algorithm

Workload
characterization

Energy Oriented

-

- I/O Intensiveness:
Implements a scheduler capable
Maintain metrics
of adapting to workload (guest)
for I/O accesses per guest
characteristics
- CPU Intensiveness: Use
Migration: Guest balancing and AppsCPU
Apps
Apps
clustering
performance
counters
OS
OS
OS
Co-locate guests to free up
resources
Guest 1
Guest 2
Guest n
Online Learning Algorithm

Workload
Characterization

Online Learning
Algorithm
Credit
Scheduler

VM Scheduling

I/O Intensive?

Hypervisor

CPU Intensive?

Hardware

For qsort
I/O
N/W

CPU intensive ( ->1) vs Memory intensive (
-> 0)
Experimental Setup
= measure of CPU intensiveness
Workloads: qsort, djpeg, blowfish, dgzip
Leakage impact ()
80
low

Frequency of Selection

OS implementation and
Results

70

m edium

60

high

Lower
Perf
Delay

CPU Xscale

Higher
energy
savings

Identifies both CPU-intensive and
memory intensive phases correctly

50
40
30

25%

Avg.

20

CPU intensive

0.75

10
0
208MHz

312MHz

416MHz

75%

Energy Saving/Performance Delay
Results for CPU
time

DPM
DPM

DPM
DPM &
& DVFS
DVFS

Trace Name

tRI

tRI

HDD

HP-1Trace

20.5

29

HP-2 Trace

5.9

8.4
2

HP-3 Trace

17.2

tRI : Average Request Inter-arrival Time (in sec)

Policy

Description

PM-1

switch CPU to ACPI state C1 (remove clock supply) and move to lowest voltage setting

PM-2

switch CPU to ACPI state C6 (remove power)

PM-3

switch CPU to ACPI state C6 and switch the memory to self- refresh mode

Benchmark

mcf

HP1 Trace

HP2 Trace

%energy

%delay

%energy

%delay

%energy

Oracle

0

68.17

0

65.9

0

71.2

Timeout

4.2

49.9

4.4

46.9

3.3

55

Ad Timeout

7.7

66.3

8.7

64.7

6

67.7

TISMDP

3.4

44.8

2.26

36.7

1.8

42.3

Predictive

8

66.6

9.2

65.2

6.5

68

Characteristics

Fixed Timeout

Timeout = 7*Tbe

Adaptive Timeout
(Douglis, USENIX95)

Initial timeout = 7*Tbe;
Adjustment = +1Tbe/-1Tbe

Exponential Predictive
(Hwang, ICCAD97)

In+l = a in + (1 a).In
with a = 0.5

Low delay

TISMDP
(Simunic, TCAD01)

Optimized for delay constraint of 3.5% on
HP-1 trace

High energy
savings

bzip2

HP3 Trace

%delay

Expert

art

DPM: With Online Learning
Preference

I
V

HP-1 Trace

CPU1

CPU2

CPUn

Experimental Setup

Policy

DPM: With Individual Experts

Device

CPU0

AMD quad core CPU
SPEC benchmarks

mem intensive

0.4

520MHz

HDD

CPUs

HP-2 Trace

sixtrack
HP-3 Trace

%delay

%energy

%delay

%energy

%delay

%energy

3.5

45

2.61

37.41

2.55

49.5

6.13

60.64

5.86

54.2

4.36

61.02

7.68

65.5

8.59

64.1

5.69

66.28

Freq

%delay

1.9

%EnergysavingsPM-i
PM-1

PM-2

PM-3

29

5.2

0.7

-0.5

1.4

63

8.1

0.1

-2.1

0.8

163

8.1

-6.3

-10.7

1.9

37

4.7

-0.6

-2.1

1.4

86

7.4

-2.4

-5

0.8

223

7.8

-9.0

-14

1.9

32

6

1

-0.1

1.4

76

7.3

-1.7

-4

0.8

202

8

-8

-13

1.9

37

5

-0.5

-2

1.4

86

6

-4.3

-7.2

0.8

227

7

-11

-16.1

Recent CPUs might perform better with a run to sleep
Power/Performance Results for HDD HP-1 trace
policy due to:
Comparison with fixed timeout experts
Improved CPU efficiency
Idle power management support

Supported by NSF-GreenLight project, CNS, Sun Microsystems, UC Micro,

Summary
Hypervisor VM scheduler
implementation
Power Management: DPM/
DVFS
Workload characterization
aware
Adaptive Behavior

Recently Viewed Presentations

  • This graph is specific to Oklahoma and mirrors the trends evident in the national data from the previous slide. Between 2015 and 2025, there will be a 50% reduction in the number of jobs available for those with a high...
  • Programming Contests

    Programming Contests

    If a "paint over" combination produces multiple words (including the same word appearing at multiple locations in the starting word), then it is counted separately from the combinations that produce individual words. Multiple words need not be separated, and a...
  • From Exodus to Exile Timeline of Major Events

    From Exodus to Exile Timeline of Major Events

    Moses led the Hebrews across the Red Sea and into the Sinai Region. On Mount Sinai, God appeared to Moses again. This time, God shared with him the Ten Commandments. The Ten Commandments served to emphasize the nature of God's...
  • Addressing the role of the length of G1

    Addressing the role of the length of G1

    FAS Center for Systems Biology, Department of Molecular and Cellular Biology, Harvard University . Discussion. References. Lengronne A., Schwob E., 2002. The yeast CDK inhibitor Sic1 prevents genomic instability by promoting replication origin licensing in late G(1).
  • Example: Data Mining for the NBA

    Example: Data Mining for the NBA

    POLICY 2004: 197-200 (IEEE) Pallabi Parveen, Jonathan Evans, Bhavani M. Thuraisingham, Kevin W. Hamlen, Latifur Khan: Insider Threat Detection Using Stream Mining and Graph Mining. SocialCom/PASSAT 2011: 1102-1110 Papers to Read for Exam #1 Bhavani M. Thuraisingham: Mandatory Security in...
  • Charge Transport in DNA - UMK

    Charge Transport in DNA - UMK

    of DNA Charge Transport Nucleobase sequence dependence Jacek Matulewski, Sergei Baranovski, Peter Thomas Faculty of Physics, Astronomy and Informatics Nicolaus Copernicus University in Toruń, Poland Departament of Physics Phillips-Universitat Marburg, Germany Toruń, 9 XI 2004
  • EMC Business Continuity and Disaster Recovery Strategies

    EMC Business Continuity and Disaster Recovery Strategies

    Recovery time objective (RTO) is the amount of time that it takes to recover the data and restart business services—including critical applications central to business operations—from recovered data before the absence of the data or applications severely impacts the organization.
  • Bay Area Money Makers IBD Meetup Saturday ,

    Bay Area Money Makers IBD Meetup Saturday ,

    Using CAN SLIM since 1985. Attended All IBD Workshops. Practice CAN SLIM as I think Bill 0'Neil teaches. This Presentation Is: For Educational Purposes Only. Not recommending any stocks/securities to buy or sell . All Investing has Substantial Risk of...