Strategies for job allocation in a hypercube concurrent computer
Abstract
The author studies the problem of how to effectively use hypercube resources (processor) for the hypercube systems which supports multiple users. When each job arrives at hypercube, the operating system will allocate a dedicate free subcube to it. One way to attack this problem is to try to minimize finishing time of a sequence of jobs. The other way is to try to reduce the fragmentation, like memory fragmentation, occurs after subcubes allocation and release. Several strategies have been studied. These are nonpreemptive scheduling, preemptive scheduling, and virtual subcube formation. The problem of scheduling k independent jobs on an ndimensional hypercube system is to minimize finishing time, where each job J, is associates with a dimension d{sub i} and a processing time t{sub i}, meaning that J{sub i} requires a d{sub i}dimensional subcube (call J{sub i} a d{sub i}dimensional job) for t{sub i} units of time. This problem is NP  complete if no preemption is allowed. The author proposes a simple heuristic called LDLPT (Largest Dimension Largest Processing Time) for this problem and analyze its worse case performance: the ratio of the heuristic finishing time to the optimal does not exceed 21/2{sup n1}. If preemption is allowed, and O(k{supmore »
 Authors:
 Publication Date:
 Research Org.:
 Ohio State Univ., Columbus, OH (USA)
 OSTI Identifier:
 5923068
 Resource Type:
 Miscellaneous
 Resource Relation:
 Other Information: Thesis (Ph. D.)
 Country of Publication:
 United States
 Language:
 English
 Subject:
 99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE; HYPERCUBE COMPUTERS; TASK SCHEDULING; ALGORITHMS; DATA TRANSMISSION; EXECUTIVE CODES; MEMORY DEVICES; MEMORY MANAGEMENT; COMMUNICATIONS; COMPUTER CODES; COMPUTERS; DATA PROCESSING; MATHEMATICAL LOGIC; PROCESSING 990200*  Mathematics & Computers
Citation Formats
Chen, GuanIng. Strategies for job allocation in a hypercube concurrent computer. United States: N. p., 1989.
Web.
Chen, GuanIng. Strategies for job allocation in a hypercube concurrent computer. United States.
Chen, GuanIng. 1989.
"Strategies for job allocation in a hypercube concurrent computer". United States.
doi:.
@article{osti_5923068,
title = {Strategies for job allocation in a hypercube concurrent computer},
author = {Chen, GuanIng.},
abstractNote = {The author studies the problem of how to effectively use hypercube resources (processor) for the hypercube systems which supports multiple users. When each job arrives at hypercube, the operating system will allocate a dedicate free subcube to it. One way to attack this problem is to try to minimize finishing time of a sequence of jobs. The other way is to try to reduce the fragmentation, like memory fragmentation, occurs after subcubes allocation and release. Several strategies have been studied. These are nonpreemptive scheduling, preemptive scheduling, and virtual subcube formation. The problem of scheduling k independent jobs on an ndimensional hypercube system is to minimize finishing time, where each job J, is associates with a dimension d{sub i} and a processing time t{sub i}, meaning that J{sub i} requires a d{sub i}dimensional subcube (call J{sub i} a d{sub i}dimensional job) for t{sub i} units of time. This problem is NP  complete if no preemption is allowed. The author proposes a simple heuristic called LDLPT (Largest Dimension Largest Processing Time) for this problem and analyze its worse case performance: the ratio of the heuristic finishing time to the optimal does not exceed 21/2{sup n1}. If preemption is allowed, and O(k{sup 2}) algorithm is presented that decides if all k jobs can be finished by a given deadline. Using this algorithm, one may obtain a minimumfinishingtime schedule in polynomial time. The characteristic of the hypercube fragmentation is that even if a sufficient number of processors are available in the hypercube, they do not form a subcube large enough to accommodate an incoming job.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = 1989,
month = 1
}

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