The following information was submitted:
Transactions: WSEAS TRANSACTIONS ON SYSTEMS
Transactions ID Number: 52-617
Full Name: Paulraj Ranjithkumar
Position: Assistant Professor
Age: ON
Sex: Male
Address: Deptof ECE/KSRCT,Tiruchengode
Country: INDIA
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E-mail address: p_ranjith_kumar@rediffmail.com
Other E-mails: p_ranjith_kumar@yahoo.co.in
Title of the Paper: An Efficient Energy and Schedule Length Model for Multiprocessor Computers
Authors as they appear in the Paper: Paulraj Ranjithkumar and Dr.Sankaran Palani
Email addresses of all the authors: p_ranjith_kumar@rediffmail.com,keeranur_palani@yahoo.co.in
Number of paper pages: 13
Abstract: Multiprocessors have emerged as a powerful computing means for running real-time applications, especially where an uniprocessor system would not be sufficient enough to execute all the tasks. The high performance and reliability of multiprocessors have made them a powerful computing resource. Such computing environment requires an efficient algorithm to determine when and on which processor a given task should be executed. In multiprocessor systems, an efficient scheduling of parallel tasks onto the processors is known to be NP- Hard problem. With growing of applications of the embedded system technology, energy efficiency and timing requirement are becoming important issues for designing real time embedded systems. This paper focuses the combinational optimization problem, namely, the problem of minimizing schedule length with energy consumption constraint and the problem of minimizing energy consumption with schedule length constraint for independent parallel tas!
ks on multiprocessor computers. These problems emphasize the tradeoff between power and performance and are defined such that the power-performance product is optimized by fixing one factor and minimizing the other and vice versa. A three-level energy/time/power allocation scheme is adopted such that the schedule length is minimized by consuming given amount of energy or the energy consumed is minimized without missing a given deadline. The performance of the proposed algorithm with optimal solution is validated analytically and compared with Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). Even though, the PSO and GA achieve the expected energy consumption and schedule length with choice of optimal power supply, the solution obtained using PSO is much quicker with minimum iterations compared with GA.
Keywords: Dynamic voltage scaling, Evolutionary Algorithm, Energy miminization, Scheduling and Multiprocessor
EXTENSION of the file: .doc
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