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November 15 - 19, 2009 SERVICE COMPUTATION 2009
Analysis of Energy Efficiency in Clouds
H. AbdelSalam K. Maly ([email protected])
R. Mukkamala M. ZubairDepartment of Computer Science,
Old Dominion University
D. KaminskyIBM, Raleigh, North Carolina
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Outline
• Cloud Computing• Change Management• Power Management
– Pro-active approach– Minimize total power consumption– Constraints:
• SLAs• Prior change management commitments
– Compute possible time slots for change management task
November 15 - 19, 2009 SERVICE COMPUTATION 2009 2
Cloud Computing
• A cloud can be defined as:– a pool of computer resources that can host a variety
of different workloads, including batch-style back-end jobs and interactive user applications.
• A cloud computing platform dynamically provisions, configures, reconfigures, and deprovisions servers as needed.
• Servers in the cloud can be physical machines or virtual machines.
• Customers have Service Level Agreements to buy computing services from cloud manager
November 15 - 19, 2009 SERVICE COMPUTATION 2009 3
Change Management
• Managing large IT environments such as computing clouds is expensive and labor intensive.
• Servers go through several software and hardware upgrades.
• IT organizations handle change management through human group interactions and coordination.
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Pro-active Approach
• We proposed earlier and implemented an infrastructure-aware autonomic manager for change management– scheduler that computes possible open time slots in
which changes can be applied without violating any of SLAs reservations.
• Here we propose pro-active energy-aware technique for change management in a cloud computing environment.
November 15 - 19, 2009 SERVICE COMPUTATION 2009 5
Job distribution
• applications in a cloud computing: – intensive compute processing, non-
interactive applications– user interactive: Web applications and Web
services are typical examples.
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Non-interactive applications
• dedicate one or more servers to each of these applications, number of dedicated servers depends on the underlying SLA and the availability of servers in the cloud
• servers should be run at their top speed (frequency) so the application can finish as soon as possible
November 15 - 19, 2009 SERVICE COMPUTATION 2009 8
Job distribution
• Assume that, based on its SLA, Job X requires s seconds response time for u users.
• From the historical data for Job X, we estimate the average processing required for a user query to be l instructions.
• Assume that job X is to be run on a server that runs on frequency f and on the average requires CPI clock ticks (CPU cycles) to execute an instruction.
• the server can execute q=(s*f)/(l*CPI) user queries within s seconds.
• If q<u , then the remaining (u-q) user requests should be routed to another server.
November 15 - 19, 2009 SERVICE COMPUTATION 2009 9
System model
• estimate the computing power (MIPS) needed to achieve the required response time
• client provides a histogram that shows the frequency of each expected query
• replace the minimum average response time constraint in SLA by the minimum number of instructions that the application is allowed to execute every second
November 15 - 19, 2009 SERVICE COMPUTATION 2009 10
System model
• Conversion of response time to MIPS
– If user query has average response time of t1 seconds when it runs solely on a server configuration with x MIPS (million instructions per second), this can be benchmarked for each server configuration), then
– to have an average response time of t2 seconds, it is required to run the query such that it can execute a minimum of (t1*x)/t2 million instructions per second.
• Power management of server
– Minimum Fmin
– Maximum Fmax
– Discrete values in between
• Power – frequency relation
November 15 - 19, 2009 SERVICE COMPUTATION 2009 12
3BfAP
Mathematical analysis
• given k servers that should run on frequencies respectively, such that total compute load is:
the total energy consumption is given by,
November 15 - 19, 2009 SERVICE COMPUTATION 2009 13
kk ffff ,, 121
kT fffL 21 .
].)([** 31
1
31
32
31
k
jjTk fLfffBAkP
Mathematical analysis
• the number of servers k, that should run to optimize power consumption, is (assuming continuous frequency spectrum):
• Each server should run at frequency
November 15 - 19, 2009 SERVICE COMPUTATION 2009 14
TLA
Bk 3 *
2
kLf T /
Sample cloud load
November 15 - 19, 2009 SERVICE COMPUTATION 2009 15
Actual and Approximated Load due to several SLAs.
Servers available for change management
• in each time segment, – the number of idle servers in the cloud equals the difference
between the total number of cloud servers and kt.
– idle server is a candidate for change management.
November 15 - 19, 2009 SERVICE COMPUTATION 2009 16
)(*2
3 tLA
Bk Tt
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Servers Available for changes as a function of time
Scenario comparison
• Total energy consumption during one period (one day) using the pro-active approach is 37305 Watt-Hour, for an average of 1554 Watt.
• Total and the average energy consumption when using 5 % over-provisioning at various frequencies:
November 15 - 19, 2009 SERVICE COMPUTATION 2009 18
Frequency Total (Watt.Hour) Average (Watt) 1.0 GHZ 64861 2703 2.0 GHZ 39324 1639 2.4 GHZ 42742 1781 3.0 GHZ 58246 2427
November 15 - 19, 2009 SERVICE COMPUTATION 2009
Conclusion
• Pro-active management is the computation of when servers will be idle so they can be scheduled for change maintenance.
• Pro-active power management leads to considerable saving in total energy consumed, for specific examples ranging from 5-75%.
• Can be modified to include compute intensive jobs
• Can be modified to include hardware failure rates
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