Electronic Theses and Dissertations
Identifier
1416
Date
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.
Library Comment
Dissertation or thesis originally submitted to the local University of Memphis Electronic Theses & dissertation (ETD) Repository.
Recommended Citation
Shu, Tong, "Advance Bandwidth Scheduling for Energy Efficiency in High-performance Networks" (2015). Electronic Theses and Dissertations. 1198.
https://digitalcommons.memphis.edu/etd/1198
Comments
Data is provided by the student.