Green HPC: Power Aware Scheduling of Bag-of-Tasks Applications with Deadline Constraints on...

Green HPC:Power Aware Scheduling of Bag-of-Tasks Applications with

Deadline Constraints on DVS-Enabled Data Centers

Kyong Hoon Kim1, Rajkumar Buyya1, and Jong Kim2

1Grid Computing and Distributed Systems (GRIDS) LaboratoryDept. of Computer Science and Software EngineeringThe University of Melbourne, Australiawww.gridbus.org

2POSTECH, Korea

Gridbus Sponsors

2

Outline

Introduction Related Work System Model Job Admission Control DVS-based Cluster Scheduling

EDF-based scheduling Proportional share-based scheduling

Simulation Results Summary

3

Background

Traditionally, high-performance computing (HPC) community has focused on performance (speed).

At the same time microprocessor vendors have not only doubled the number of transistors (and speed) every 18-24 months, but they have also doubled the power densities.

Moore’s Law for Power Consumption:

4

Research Motivations of Power Aware/Energy Efficient High Performance

Computing (HPC) Rapid uptake of HPC-architecture based Data Centers for

hosting industrial applications Reducing the operational costs of powering and cooling HPC

systems The tremendous increase in computer performance has come with an even

grater increase in power usage. According to Eric Schmit, CEO of Google, what matter most to Google is

“not speed but power, because data centers can consume as much electricity as a city.”

Improving reliability As a rule of thumb, for every 10°C increase in temperature, the failure rate

of a system doubles. Computing environment affected the correctness of the results.

The 18-node Linux cluster produced an answer outside the residual (i.e., a silent error) when running in dusty 85°F warehouse but produced the correct answer when running in a 65°F machine-cooled room.

5

Reliability/Implications

Reliability of Leading Edge Supercomputer (D. Reed, 2004)

Estimated Cost of An hour of system downtime (W. Feng, (ACM Queue, 2003):

6

Power Aware Computing

Power Aware (PA) computing/communications The objective of PA computing/communications is to improve power

management and consumption using the awareness of power consumption of devices.

Power consumption is one of the most important considerations in mobile devices due to the limitation of the battery life.

System level power management Recent devices (CPU, disk, communication links, etc.) support multiple

power modes. System scheduler can use these multiple power modes to reduce the

power consumption.

7

Outline




8

Related Work (1/3)

Research on power reduction for scientific applications

Hsu and Feng (SC 2005) – Los Alamos National Lab, USA -adaptation algorithm Automatic adaptation of CPU frequencies : the intensity level of off-chip accesses

Ge, Feng, and Cameron (2005) Three DVS scheduling strategies Software framework to implement scheduling techniques

Hotta, et. al. (2006) Profile-based power-performance optimization Selection of an appropriate gear using DVS scheduling Development of power-profiling system called PowerWatch

…

9

Related Work (2/3)

Energy reduction for MPI programs Kappiah, et. al. (2005) - NC State, and Georgia Uni, USA

Inter-node bottle problem in MPI programs Selection of an appropriate gear based on slack time

Lim, et. al. (2006) – NC State, and Georgia Uni, USA Adaptive DVS of Communication Phases in MPI programs

Son, et. al. (2006) …

Two approaches for building power-aware cluster platforms

Design and develop systems with consideration of energy consumption.

BlueGene/L, Green Destiny, … Use DVS-enabled commodity systems.

Clusters with AMD Athlon64s, Pentium Ms, AMD Opterons, …

10

Related Work (3/3)

DVS (Dynamic Voltage Scaling) technique Reducing the dynamic energy consumption by lowering the supply voltage at

the cost of performance degradation Recent processors support such ability to adjust the supply voltage

dynamically. The dynamic energy consumption = * Vdd2 * Ncycle

Vdd : the supply voltage Ncycle : the number of clock cycle

An example

10 msec 25 msec

deadlinePower

5.02

(a) Supply voltage = 5.0 V

10 msec 25 msec

deadlinePower

2.02

(b) Supply voltage = 2.0 V

11

DVS-based Power Aware Cluster Scheduling

Research motivation Previous work has focused on the development of DVS-

enabled cluster systems. Few works have considered the scheduling problem in power-

aware clusters.

Problem to solve To provide scheduling algorithms in DVS-enabled cluster

systems in order to minimize the energy consumption and to meet the job deadline.

Exploit industries move towards Utility Model / SLA-based Resource Allocation…

12

Outline




13

System Model (1/2)

Cluster model A cluster system is defined as (N, Q).

N: the number of processors Q: the processing performance of each PE in terms of MIPS

Job model A job is considered to be a bag-of-tasks application. The deadline is used as a QoS parameter of a job. A job = (p, {l1, l2, …, lp}, d)

p : the number of sub-tasks li : the length in MI of the i-th task d: the job deadline

14

System Model (2/2)

Energy model Energy consumption of a task execution

E = V2L L : the task length V : the supply voltage : a proportional constant

Dynamic Voltage Scaling {V1, …, Vm} : m different voltage levels Qi : the processor speed (MIPS) under the associated voltage level Vi Si : the normalized speed of each voltage level Vi (Si = Qi/Qm)

An Example

Voltage (Vi) MIPS (Qi) Relative Speed (Si)0.9 V 4,000 0.41.1 V 6,000 0.61.3 V 8,000 0.81.5 V 10,000 1.0

15

Outline




16

Proposed Cluster RMS System Architecture with Energy-Efficient

Resource Allocation

(1) Job submission (2) Schedulability test & Energy estimation (3) Acknowledgement of schedulability and energy amount (4) Selection of PEs

ResourceController

Processor

EnergyEstimator

Job-Queue

PE1

Processor

EnergyEstimator

Job-Queue

PE2

Processor

EnergyEstimator

Job-Queue

PEN

User

User

User

(1)(2)

(3)

(4)

Cluster

17

Application Admission and Resource Allocation Algorithm

Can task be completed in this PE?

How to estimate energy consumption?

We propose two DVS scheduling algorithms based on • Space-shared policy (EDF)• Time-shared policy

Algorithm Admission_Resource_Allocation (J = (p, {l1, …, lp}, d)) 1: for i from 1 to p do 2: PEalloc null; 3: energymin MAX_VALUE; 4: for k from 1 to N do 5: if schedulable (PEk, li, d) == true then 6: energyk energy_estimate (PEk, li, d); 7: if energyk < energymin then 8: energymin energyk; 9: PEalloc PEk;10: endif11: endif12: endfor13: if PEalloc != null then14: Allocate the i-th task of J to PEalloc;15: else16: Cancel all tasks of J.17: return reject;18: endelse19: endfor20: return accept;

18

Outline




19

EDF-based on DVS scheduling (1/4)

Basics Tk = {k,i (ek,i, dk,i) | i = 1, …, nk}

The current available task set in the k-th PE nk : the current number of tasks

k,i (ek,i, dk,i) : the i-th task in Tk

ek,i : the remaining execution time

dk,i : the remaining deadline

EDF (Early Deadline First) policy Tk is sorted by the deadline so that dk,i dk,i+1

The scheduler always executes the earliest-deadline task in the queue.

20

EDF-based DVS scheduling (2/4)

The temporary utilization, uk,i

The required processor utilization for task k,i by EDF

The continuous speed level of the highest-priority task, sk

The supply voltage level of the highest-priority task, vk

ik

i

j jk

ik d

eu

,

1 ,

,

}{max~,1 ik

nik us k

}~|{min 1 kiimik sSVv

~

21


An example Tk = {k,1(1, 4), k,2(2, 6), k,3(2, 10)} Temporary utilizations at time 0

uk,1 = 1/4 uk,2 = (1 + 2)/6 = 1/2 uk,3 = (1 + 2 + 2)/10 = 1/2

Scaling factors At time 0

sk = max{uk,1, uk,2, uk,3} = 1/2 vk = 1.1V

Energy model to use

0.60.4 k,1

5 10

Speed level

0

k,2 k,3

vk = 1.1V 1.1V10/6

0.9V

~

Voltage (Vi)

MIPS (Qi)Relative Speed

(Si)0.9 V 4,000 0.41.1 V 6,000 0.61.3 V 8,000 0.81.5 V 10,000 1.0

22


Schedulability test of EDF Energy estimation of EDF

Algorithm schedulable_EDF (PEk, l, d)Tk’ Tk {(l/Qm, d)};Sort Tk’ in the order of deadline.for i from 1 to nk + 1 do uk’,i ek’,i / dk,i ; if uk’,i > 1 then return false;endforreturn true;

Algorithm energy_estimate_EDF (PEk, l, d)Ecurrent energy_consumption (Tk, nk);Tk’ Tk {(l/Qm, d)};Enew energy_consumption (Tk’, nk+1);return (Enew – Ecurrent);function energy_consumption (T, n)Energy 0;time the current time;for i from 1 to n do for j from i to n do uj ek /dj; s’ max {uj} v min {Vj | Sj s’} s min {Sj | Sj s’} Energy Energy + v2eiQm; time time + ei/s; for j from i to n do dj dj – ei/s;endforreturn Energy;

23

Proportional Share-based DVS scheduling (1/2)

The proportional share scheme Multiple tasks share the processor performance in proportion to each task’s

weight. Each task should be given at least ek,i/dk,i under the maximum processor

speed to meet the deadline. The continuous processor speed level, sk

The supply voltage level of the highest-priority task, vk

The proportional share of each task, sharek

61 8 15

kn

i ikikk des1 ,, /~

k

k

n

j ikik

ikikn

j jk

jkk

ik

ik

kik

de

de

d

es

d

e

sshare

1 ,,

,,

1 ,

,

,

,,

/

/)(

1

}~|{min 1 kiimik sSVv

~

24

Proportional Share-based DVS scheduling (2/2)

An example Tk = {k,1(1, 4), k,2(2, 6), k,3(2, 10)}

Schedulability test and energy estimation is similar to EDF algorithm.

8 15

0.60.4

k,1 : 0.32

Speed level

0

vk = 1.3V 1.1V

3.906

0.9V

0.8

k,2 : 0.425

k,3 : 0.255

0 5.712

k,2 : 0.62

k,3 : 0.38

7.69

k,3 : 1.0

k,i : sharek,i

25

Outline




26

Simulation Environment

Using the GridSim toolkit A cluster system with 32 DVS-enabled processors

Operating points of simulated processors based on Athlon-64

1000 bag-of-tasks applications Task characteristics

Task length : 600,000 MIs ~ 7,200,000 MIs The number of tasks : 2 ~ 32 Deadline : 20% ~ 100% more than average execution time

Frequency Voltage MIPS Relative Speed

0.8 GHz 0.9 V 4,000 0.41.0 GHz 1.0 V 5,000 0.51.2 GHz 1.1 V 6,000 0.61.4 GHz 1.2 V 7,000 0.71.6 GHz 1.3 V 8,000 0.81.8 GHz 1.4 V 9,000 0.92.0 GHz 1.5 V 10,000 1.0

27

Simulated algorithms

DVS-based scheduling EDF-DVS PShare-DVS

Scheduling at maximum processor speed EDF-1.5V PShare-1.5V

Scheduling at minimum processor speed EDF-0.9V PShare-1.5V

28

Job Acceptance Rate

(a) EDF

0

20

40

60

80

100

2 3 4 5 6 7 8

Inter- arrival time (mins)

Acc

epta

nce

rate

(%

)

EDF- 1.5VEDF- DVSEDF- 0.9V

(b) PShare

0

20

40

60

80

100

2 3 4 5 6 7 8

Inter- arrival time (mins)

Acc

epta

nce

rate

(%

)

PShare- 1.5VPShare- DVSPShare- 0.9v

0

20

40

60

80

100

2 3 4 5 6 7 8

inter- arrival time (min)

EDF- DVSEDF- 0.9VEDF- 1.5VPShare- DVSPShare- 0.9VPShare- 1.5V

Acceptance rate (%)

29

Energy consumption

Normalized to EDF-1.5V at inter-arrival time of 2 mins.

Energy consumpation per accepted task

0

0.2

0.4

0.6

0.8

1

1.2

2 3 4 5 6 7 8

EDF- 1.5VPShare- 1.5VEDF- DVSPShare- DVSEDF- 0.9VPShare- 0.9V

Inter-arrival time (min)

Normalizedvalue

30

Normalized performance of DVS

Inter-arrival time (min)

EDF-DVS vsEDF-1.5V

PShare-DVS vsPShare-1.5V

Energy Reduction

(%)

Acceptance Degradatio

n (%)

Energy Reduction

(%)

Acceptance Degradatio

n (%)

2 13.6 13.3 33.8 14.3

3 21.3 13.0 34.4 12.8

4 31.2 11.2 36.3 9.7

5 34.4 7.9 38.8 9.5

6 38.6 6.3 42.8 7.0

7 41.5 4.0 43.7 5.6

8 44.3 2.7 45.2 5.2

31

Impact of granularity/number of controllable voltage levels

Normalized performance of EDF Normalized performance of PShare

0

0.2

0.4

0.6

0.8

1

1 2 3 4 7 13

Number of voltage levels

Normalized acceptance ratio

Normalized energy consumption

0

0.2

0.4

0.6

0.8

1

1 2 3 4 7 13

Number of voltage levels

Normalized acceptance ratio

Normalized energy consumption

Normalizedvalue

Normalizedvalue

32

Outline




33

Summary

Two primary drivers for Power-Aware HPC Operational cost Reliability

Power-aware scheduling with deadline constraints Reducing energy consumption Meeting jobs’ deadlines

The proposed scheduling algorithms DVS-based scheduling based on

Space-shared policy EDF Time-share policy / Proportional Resource Sharing

Minimizing cost under the constraint of the job deadline Future work

Budget-constrained power-aware scheduling Power-aware workflow scheduling

34

Thanks for your attention!

We Welcome Cooperation in Research and Development!http:/www.gridbus.org

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