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The presentation of my accepted paper in 2009 IEEE/ACM International Symposium on Cluster Computing and the Grid, in Shanghai, China, May 18-21, 2009
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Optimal Power Management for
Server Farm to Support Green Computing
Dusit Niyato , Sivadon Chaisiri, and Lee Bu Sung, Francis
[email protected], [email protected], [email protected] of Computer Engineering
Nanyang Technological University, Singapore
IEEE/ACM International Symposium on
Cluster Computing and the Grid (CCGrid 09)
May 19, 2009
Outline
• Introduction• System Model• Challenges• Optimization Formulation• Performance Evaluation• Summary
2
Introduction
• Green Computing is the study and practice of using computing resource efficiently, not only the performance but energy
• We firstly propose an optimal power management (OPM) used by a batch scheduler in a server farm
• This OPM observes the state of the server farm, then make the decision to switch the operation mode (active / sleep) of the server
• An optimal decision of OPM is obtained by the constrained Markov decision process (CMDP)
• We consider the system with a job broker to assign users to multiple server farms while the cost is minimized
3
Power Management
• Power management is a major approach of green computing• Power management is applied to control power consumption and
operation of computing resources• Two levels of power management
– Machine level (e.g., some components can be suspended)– Network level (e.g., a node in a server farm can be turned to
sleep mode)• Our work is based on the network level power management
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System Model
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OPM is a part of a batch scheduler in a server farm
Incoming jobs
Server in active mode
Server in sleep mode
Batch scheduler
Job
bro
ker
Serverfarm
Serverfarm
users
Challenges
• Uncertainty
– Job arrival is random; users generate job randomly
– Job size and thus processing time is random
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Incoming jobs
Server in active mode
Server in sleep mode
Batch scheduler
Challenges
• Questions to Be Answer
– When and how many servers to be switched between active and
sleep modes ?– Which server farm should be chosen for a user ?
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Incoming jobs
Server in active mode
Server in sleep mode
Batch scheduler
Power and Workload Management
• OPM is a part of a batch scheduler• An optimal decision of OPM is obtained by formulating and solving
the constrained Markov decision process (CMDP)• Markov decision process (MDP)
– a discrete time stochastic control process characterized by a set of states; in each state there are several actions from which the decision maker must choose
– For state s and action a, a state transition function Pa(s) determines the transition probabilities to the next state
– The decision maker earns a reward (incursa cost) for each state transition
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Optimization Formulation of OPM
• State Space
– (X,S): Composite state of server farm
– X: Number of jobs in queue
– S: Number of servers in active mode
• Decision Epoch
– Time slot
• Action
– Us: Number of servers to be switched between active and sleep modes
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Time Slots and Actions
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Time slot t-2
Time
Time slot t-1
Action: -2Switch two servers
to sleep mode
Five servers are active Three servers
are active,two servers are
switching to sleep mode
Action: 0Do nothing
Action: +1Switch one server
to active mode
Time slot t
Time slot t+1
Three servers are active,
one server isswitching toactive mode
Three servers are active
Time slot t+2
Four servers are active,
one server isswitching toactive mode
Action: +1Switch one server
to active mode
Action: 0Do nothing
Action: …
Formulation of OPM
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Minimize power consumption
Waiting time requirement
Bellman’s equation
Loss requirement
Job Broker• The system with multiple server farms and multiple users are
considered
• A job broker assigns the user to the appropriate server farm such that the power consumption cost and network cost of a system is minimized
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Formulation of Assignment Problem for Job Broker
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Minimize total cost
Total delay requirement
Performance Evaluation
An individual server farm with batch schedule + OPM• A discrete-time simulation is used to verify the correctness of an
analytical model• The optimal decision (or policy) is made after the state of the system
is observed• Parameter Setting
– Power consumption watts– The job dropping probability requirement – The size of time slot seconds– The waiting time requirement seconds
– The min and max number of servers to be mode switched
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Pact=400 ,P slp=40Bmax=10
−3
T=20W max=150
Amax=2
Performance of Individual Server Farm
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A set of actions given the number of jobs in a queue and the number of servers in active mode
0
5
10
150
5
10
2
1
0
1
2
Number of servers in active modeNumber of jobs in queue
Act
ion
Performance of Individual Server Farm
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6 7 8 9 10 11 12 13 14 151600
1700
1800
1900
2000
2100
Total number of servers (S)
Ave
rage
pow
er c
onsu
mpt
ion
(wat
ts)
Wmax=150
Wmax=200
Simulation
Average power consumption under different total number of servers
Performance of Individual Server Farm
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The minimum power consumption given different waiting time and job blocking probability requirements
150 200 250 3001200
1250
1300
1350
1400
Maximum waiting time (Wmax)
Pow
er c
onsu
mpt
ion
(wat
ts)
Bmax=0.001
Bmax=0.01
Bmax=0.02
Bmax=0.03
Performance Evaluation
The system with multiple server farms and a job broker• Two server farms are evaluated ( F = 2 )• Multiple users ( U = 20 ) are coming to the job broker• The network cost is represented by a distance between location of
user and location of server farm• A number of servers per server farm is 10 ( )• Two different scenarios
– Identical power consumption cost ( / Wh)– Different power consumption costs ( / Wh )
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C1 p =C 2
p=0 .01
S1=S2=10
C1 p =0 .01 ,C2
p =0.012
Performance of Multiple Server Farms
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The assignment of the users to the server farms under different power consumption costs
UserServer farm 1Server farm 2Assignment of user to server farm
Summary
• We have considered the power management issue in green computing
• We have first proposed OPM, formulated as CMDP, for an individual server farm
• Our OPM can dynamically reduce the power consumption of servers in a farm by switching them to sleep mode
• The system with multiple server farms has been also considered in which the job broker has been optimized to assign the user to the server farm
• Future work– Computing resource planning under uncertainty
– Virtualization + Cloud ...
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