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ADAPTIVE RESOURCE MANAGEMENT IN DISTRIBUTED SYSTEMS
by
Abhishek Bhan Sharma
A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Ful llment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(COMPUTER SCIENCE)
December 2010
Copyright 2010 Abhishek Bhan Sharma
Object Description
| Title | Adaptive resource management in distributed systems |
| Author | Sharma, Abhishek Bhan |
| Author email | absharma@usc.edu; absharma@gmail.com |
| Degree | Doctor of Philosophy |
| Document type | Dissertation |
| Degree program | Computer Science |
| School | Viterbi School of Engineering |
| Date submitted | 2010 |
| Restricted until | Unrestricted |
| Date published | 2010-11-22 |
| Advisor (committee chair) |
Govindan, Ramesh Golubchik, Leana |
| Advisor (committee member) | Neely, Michael J. |
| Abstract | In this dissertation, we focus on resource management in distributed systems. The essence of resource management is to match the requirements of computing tasks with the available resources. We propose and develop approaches to resource management in three qualitatively different systems: (1) server clusters providing computing-as-a-service, (2) tiered-architecture (of servers) hosting web services, and (3) networks of wireless sensors. These systems differ from each other along multiple dimensions: available resources, system dynamics, workload, etc. Still, a common theme in effective resource management for these systems (as demonstrated in this dissertation) is that we must be cognizant of the system heterogeneity (computing resources as well as workload), and adapt to system dynamics. Our work improves upon the state-of-the-art in the three systems in the following way. For systems providing computing-as-a-service, we design and implement a service model that provides predictability in job finish times and prioritized service to delay sensitive jobs. We also develop a machine learning based workload characterization technique for web services that categorizes users' request based on their resource usage. Such categorization is useful in improving the accuracy of performance models for these systems. In the context of wireless sensor networks, we make the following two contributions: (1) we design an online algorithm that makes joint compression and transmission decisions to save energy, and (2) we explore techniques for detecting anomalies in data collected using these networks. |
| Keyword | computing-as-a-service; wireless networks; stochastic network optimization |
| Language | English |
| Part of collection | University of Southern California dissertations and theses |
| Publisher (of the original version) | University of Southern California |
| Place of publication (of the original version) | Los Angeles, California |
| Publisher (of the digital version) | University of Southern California. Libraries |
| Provenance | Electronically uploaded by the author |
| Type | texts |
| Legacy record ID | usctheses-m3550 |
| Rights | Sharma, Abhishek Bhan |
| Repository name | Libraries, University of Southern California |
| Repository address | Los Angeles, California |
| Repository email | http://www.usc.edu/isd/libraries/services/ask_a_librarian/email/ |
| Filename | etd-Sharma-4219 |
| Archival file | uscthesesreloadpub_Volume40/etd-Sharma-4219.pdf |
Description
| Title | Page 1 |
| Full text | ADAPTIVE RESOURCE MANAGEMENT IN DISTRIBUTED SYSTEMS by Abhishek Bhan Sharma A Dissertation Presented to the FACULTY OF THE USC GRADUATE SCHOOL UNIVERSITY OF SOUTHERN CALIFORNIA In Partial Ful llment of the Requirements for the Degree DOCTOR OF PHILOSOPHY (COMPUTER SCIENCE) December 2010 Copyright 2010 Abhishek Bhan Sharma |
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