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An Availability-Aware Task Scheduling Algorithm for Heterogeneous Systems Using Particle Swarm Optimization

Author: ALI QAMAR
Tutor: LI KEN LI
School: Hunan University
Course: Computer Application Technology
Keywords: Heterogeneous systems Particle Swarm Optimization Grid computing Resource scheduling Time span Multi-level applications
CLC: TP18
Type: Master's thesis
Year: 2009
Downloads: 16
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Abstract


In parallel and distributed computing , cluster , grid and peer-to-peer (P2P) systems are popular subjects . In a grid environment , resources through the Internet linked geographically distributed , managed by the organizers have a different strategy . Here we will introduce the issues of safety and resource management and scheduling strategy for heterogeneous systems . Grid computing, resource management and scheduling system need to rely on to manage resources such as consumers and owners of these factors , so as to adapt to the changing availability of resources . Task scheduling in resource availability is a challenging problem . In a heterogeneous environment , the processor speed is different , and may not be able to continue to provide the computing power to achieve a good span of time is a critical issue . With the adoption of a multi - level applications , these problems continue to expand . The existing algorithms rarely discuss the problem of multi - level applications . In this paper , we study a multi-level application of the algorithm , this algorithm (SSAC) to keep the task of good usability while maintaining a good span of time . But it tended to be located some tasks to some node high availability , and this will affect the system load balancing . We will use the particle swarm algorithm to do some optimization on some . Particle Swarm Optimization (PSO) is to find the optimal solution for the continuous nonlinear function . Each particle represents a solution . PSO is initialized by the solution of a group of machines , and constantly updates algebra to produce optimal solutions . Compare the results of this algorithm and some of the more popular algorithms such as the SSAC and MINMIN algorithm . The experiment is a selection of some of the relevant data analog simulation . The experimental results demonstrate the superiority of our algorithm to reduce the time span .

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