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Title page for ETD etd-12022010-131755

Type of Document Dissertation
Author Roy, Nilabja
Author's Email Address nilabjar@dre.vanderbilt.edu
URN etd-12022010-131755
Title QoS assurance and control of large scale distributed component based systems
Degree PhD
Department Computer Science
Advisory Committee
Advisor Name Title
Aniruddha Gokhale Committee Chair
Douglas C. Schmidt Committee Member
Gautam Biswas Committee Member
Janos Sztipanovits Committee Member
Larry Dowdy Committee Member
  • queuing models
  • component based systems
  • analytical model
  • simulation model
  • performance engineering
  • Resource allocation
  • colored petri net
  • lookahead control
  • workload modeling
Date of Defense 2010-11-12
Availability unrestricted
Large scale distributed component based applications provide

a number of different services to its clients. Such

applications normally serve huge number of concurrent clients

and need to provide a decent Quality of Service (QoS). A

deployment domain composed of several machines are used to host

these applications. The application components are distributed

across the machines and communicate among themselves. An

important objective of the owner of such a deployment will be

to handle as much clients as possible during any given time

which will obviously maximize the revenue earned. But this also

needs to be done by keeping the costs down and also by providing

every customer a minimum amount of QoS. The cost can be reduced

by minimizing the number of machines used and using less power.

This thesis works towards a solution to the above and comes up

with novel application component placement heuristics which makes

sure that the overall resources of the domain is utilized in the

best possible way. The intuition behind this work is that

components are the smallest elements of an application from the

perspective of resource usage. By distributing the components in

a judicious way across the machines, it is possible to ensure that

a minimum of resources is wasted. The work presented here uses a

three phase strategy to come up with a solution. In the first

phase, the component resource requirement is identified using

profiling and workload modeling techniques. In the second phase

detailed performance estimation of the application is carried

out using analytical methods. In the third and final phase heuristics

are proposed which uses the component resource requirement and

the performance estimation methods to come up with placing the

components across the machines. It ensures that such a placement

will waste the least of resources.

In the final part of this work, it applies this work in the

context of modern data center planning. The most important challenge

in modern day data centers is to support large customer bases with

high expectation of performance. The incoming workload to the

application is highly varying with periodic increase and decrease

of workload. If resources are allocated for average workload then

performance suffers during peak workload while planning for

peak workload keeps resources idle during less workload. Cloud

computing is an emerging trend which allows the elastic

configuration of resources where machines can be acquired and

released on the go. This work proposes a dynamic capacity

planning framework for cost minimization based on a look ahead

control algorithm which combines performance modeling,

workload forecasting and cost optimization to plan for resource

allocation in a dynamic environment. The results show how the

resources can be allocated just-in-time with workload

fluctuations. The dissertation also presents the various way

resource is allocated as the various cost components change.

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