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1
OPTIMAL SERVER PROVISIONING AND FREQUENCY ADJUSTMENT
IN SERVER CLUSTERS
Presented by: Xinying Zheng09/13/2010
XINYING ZHENG, YU CAIMICHIGAN TECHNOLOGICAL UNIVERSITY
2
Outline
Introduction Related Work Optimization problem formulation
Single class Multiple classes
Overhead Analysis: DCP model Performance Evaluation Conclusion and Future Work
4
Motivation
The power consumption of enterprise data centers in the U.S. doubled between 2000 and 2005. And will likely triple in the next few years.
Servers consume 0.5 percent of the worlds total electricity usage, this number will increase to 2 percent by 2020.
6
Processor
Memory
DVS( Dynamic voltage scaling)
Feedback Control
DTM( Dynamic thermal management)Single Server
Storage and Database Servers
Web and application Servers
Non-data Movement
Data Movement
DV/FS
Feedback Control
VOVF
DTM
Virtualization
MemoryNetwork Techniques
Discs
Performance level
DVC
Economic method
Wireless sensor networks
Computer networks
Request-response service
Long-live connected service
Server Cluster
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Syetem Assumption
All servers in the cluster are identical
nodes.
Each server has two modes: active and
inactive.
Operate at a number of discrete
frequencies.
All the incoming requests are CPU
bounded.
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Performance metric modeling Incoming request follows a heavy-tailed
bounded Pareto distribution.
If we define a function: Average job size:
(1)
(2)
(4)
(5)
(3)
10
Request time in single server
Server processing capacity: c
Packets inter-arrival time follows exponential distribution with a mean of 1/λ.
According to Pollaczek-Khinchin formula, the average waiting time is :
Request time:
(6)
(8)
(7)
(9)
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Extend to server cluster
Extend to the server-cluster mode. Using Round-Robin dispatching policy, the arrival process at each server in the cluster has rate .
Processing capacity is proportional to frequency.
Request time:
/ m
(10)
(11)
12
Power consumption modeling Power-to-frequency relationship. Linear model. Cubic model:
System power consumption:
(12)
(13)
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Optimization problem formulation Minimizing total power consumption. Request time threshold. Mechanism:
VOVF: vary-on, vary-off DFS: dynamic frequency scaling.
14
Optimization problem formulation (single class)
Single class:
Computation complexity is O(NM). Complexity can be reduced to O(NM).
applying a coordinated voltage scaling.
(14)
15
Optimization problem formulation (Multiple classes)
Assuming incoming requests are classified into N classes.
The ratio of average request time between class i and j is fixed to the ratio of the corresponding differentiation parameters:
We assume class 1 is the “highest class” and set:
E[Ri]
E[Rj]
i
j
(15)
0 1
2L
w
17
Optimization problem formulation (Multiple classes)
Multiple classes:
Different class receive different performance.
(16)
18
Overhead Analysis
Server transfers from inacitve to active mode.
Transition time influence the performance.
Double Control Periods(DCP) model.
Double control periods
20
Simulation
Package
generator
•incoming request
•Inter arrival time between package
Load
dispatche
r
•Caculate the number of active servers according to workload.
•Dispatch incoming jobs to active server.
Number of servers
•Waiting queue.
•Excute the jobs in FIFO discipline.
21
Evaluation (single class)
Request time comparison between OP model and DCP model
Power consumption comparison between OP model and DCP model
22
Evaluation (multiple classes)
Request time comparison between OP model and DCP model
Power consumption comparison between OP model and DCP model
23
Evaluation(real workload single class)
Request time comparison between OP model and DCP model
Power consumption comparison between OP model and DCP model
24
Evaluation(real workload multiple classes)
Request time comparison between OP model and DCP model
Power consumption comparison between OP model and DCP model
25
Contributions
Optimization model for power reduction in server clusters.
Single class and multiple classes. Double control periods model to
compensate the transition overhead. Evaluate our models in real workload
data trace.
26
Future work
Effect of dispatching strategy. Transition overhead of frequency
adjustment. heterogeneity in data centers. Apply our model to the real Internet web
servers in the future.