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

1. Optimal Power Management for
Server Farm to Support Green Computing
Dusit Niyato , Sivadon Chaisiri, and Lee Bu Sung, Francis
dniyato@ntu.edu.sg, siva0020@ntu.edu.sg, ebslee@ntu.edu.sg
School of Computer Engineering
Nanyang Technological University, Singapore
IEEE/ACM International Symposium on
Cluster Computing and the Grid (CCGrid 09)
May 19, 2009

2. Outline
• Introduction
• System Model
• Challenges
• Optimization Formulation
• Performance Evaluation
• Summary
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3. 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
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4. 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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5. System Model
Server
farm
broker
Job
Server
farm
users
Incoming jobs
OPM is a part of a batch
scheduler in a server farm Batch scheduler
Server in
Server in sleep mode
active mode
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6. Challenges
• Uncertainty
– Job arrival is random; users generate job randomly
– Job size and thus processing time is random
Incoming jobs
Batch scheduler
Server in
Server in sleep mode
active mode
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7. 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 ?
Incoming jobs
Batch scheduler
Server in
Server in sleep mode
active mode
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8. 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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9. 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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10. Time Slots and Actions
Action: +1
Action: 0 Action: 0 Switch one server
Do nothing Do nothing to active mode
Action: -2 Action: +1
Switch two servers Switch one server Action: …
to sleep mode to active mode
Time slot Time slot Time slot Time slot Time slot
t-2 t-1 t t+1 t+2
Time
Five servers Three servers Four servers
are active Three servers are active Three servers are active,
are active, are active, one server is
two servers are one server is switching to
switching to switching to active mode
sleep mode active mode
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11. Formulation of OPM Minimize power consumption
Waiting time requirement
Loss requirement
Bellman’s equation
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12. 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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13. Formulation of Assignment Problem for Job Broker
Minimize total cost
Total delay requirement
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14. 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 P act =400 ,P slp =40 watts
– The job dropping probability requirement Bmax =10−3
– The size of time slot T=20 seconds
– The waiting time requirement W max =150 seconds
– The min and max number of servers to be mode switched A max =2
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15. Performance of Individual Server Farm
2
1
Action
0
­1
­2 0
15
10 5
5
0 10
Number of servers in active mode
Number of jobs in queue
A set of actions given the number of jobs in a queue and
the number of servers in active mode
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16. Performance of Individual Server Farm
2100
Average power consumption (watts)
W max =150
2000 W max =200
Simulation
1900
1800
1700
1600
6 7 8 9 10 11 12 13 14 15
Total number of servers (S)
Average power consumption under different total number of servers
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17. Performance of Individual Server Farm
1400
Bmax =0.001
Bmax =0.01
Power consumption (watts)
1350
Bmax =0.02
Bmax =0.03
1300
1250
1200
150 200 250 300
Maximum waiting time (W max )
The minimum power consumption given different waiting time
and job blocking probability requirements
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18. 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 ( S1 =S 2 =10 )
• Two different scenarios
p  p
– Identical power consumption cost ( C 1 =C 2 =0 . 01 / Wh)
p  p
– Different power consumption costs ( C 1 =0 . 01 ,C 2 =0. 012 / Wh )
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19. Performance of Multiple Server Farms
User
Server farm 1
Server farm 2
Assignment of
user to server farm
The assignment of the users to the server farms
under different power consumption costs
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20. 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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21. Thank you
Contact Us
dniyato@ntu.edu.sg, siva0020@ntu.edu.sg, ebslee@ntu.edu.sg
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