Electronic Theses and Dissertations

Identifier

1416

Author

Tong Shu

Date

2015

Date of Award

7-15-2015

Document Type

Thesis

Degree Name

Master of Science

Major

Computer Science

Committee Chair

Chase Qishi Wu

Committee Member

Lan Wang

Committee Member

Scott D. Fleming

Abstract

An increasing number of high-performance networks provision dedicated channels through circuit-switching or MPLS/GMPLS tunneling techniques to support large data transfer. The link bandwidths of these networks are typically shared by multiple users through advance scheduling and reservation. The sheer volume of data transfer across such networks in a national or international scope requires a significant amount of energy on a daily basis. However, most existing bandwidth scheduling algorithms only concern traditional objectives such as data transfer time minimization, and very limited efforts have been devoted to energy efficiency in high-performance networks. In this paper, we adopt a practical power model and formulate two advance instant bandwidth scheduling problems according to power-down and speed scaling models to minimize energy consumption under data transfer deadline and packet loss constraints. After proving these two problems’ NP-completeness, we design an approximation algorithm for the bandwidth scheduling problem in the power-down model and the polynomial time optimal solution for its simplified version, and also design an pseudo polynomial time approximation scheme and a fast heuristic algorithm for the bandwidth scheduling problem in the speed scaling model in view of the tradeoff between optimality and time cost in practice. The performance superiority of the proposed solution in terms of energy saving is illustrated by extensive results based on both simulated and real-life networks in comparison with existing methods.

Comments

Data is provided by the student.

Library Comment

dissertation or thesis originally submitted to the local University of Memphis Electronic Theses & dissertation (ETD) Repository.

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