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Fitness Sharing Chaotic Particle Swarm Optimization (FSCPSO): A Metaheuristic Approach for Allocating Dynamic Virtual Machine (VM) in Fog Computing Architecture
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作者 Prasanna Kumar Kannughatta Ranganna Siddesh Gaddadevara Matt +2 位作者 Chin-Ling Chen Ananda Babu Jayachandra Yong-Yuan Deng 《Computers, Materials & Continua》 SCIE EI 2024年第8期2557-2578,共22页
In recent decades,fog computing has played a vital role in executing parallel computational tasks,specifically,scientific workflow tasks.In cloud data centers,fog computing takes more time to run workflow applications... In recent decades,fog computing has played a vital role in executing parallel computational tasks,specifically,scientific workflow tasks.In cloud data centers,fog computing takes more time to run workflow applications.Therefore,it is essential to develop effective models for Virtual Machine(VM)allocation and task scheduling in fog computing environments.Effective task scheduling,VM migration,and allocation,altogether optimize the use of computational resources across different fog nodes.This process ensures that the tasks are executed with minimal energy consumption,which reduces the chances of resource bottlenecks.In this manuscript,the proposed framework comprises two phases:(i)effective task scheduling using a fractional selectivity approach and(ii)VM allocation by proposing an algorithm by the name of Fitness Sharing Chaotic Particle Swarm Optimization(FSCPSO).The proposed FSCPSO algorithm integrates the concepts of chaos theory and fitness sharing that effectively balance both global exploration and local exploitation.This balance enables the use of a wide range of solutions that leads to minimal total cost and makespan,in comparison to other traditional optimization algorithms.The FSCPSO algorithm’s performance is analyzed using six evaluation measures namely,Load Balancing Level(LBL),Average Resource Utilization(ARU),total cost,makespan,energy consumption,and response time.In relation to the conventional optimization algorithms,the FSCPSO algorithm achieves a higher LBL of 39.12%,ARU of 58.15%,a minimal total cost of 1175,and a makespan of 85.87 ms,particularly when evaluated for 50 tasks. 展开更多
关键词 Fog computing fractional selectivity approach particle swarm optimization algorithm task scheduling virtual machine allocation
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TWO-STAGE SENTENCE SELECTION APPROACH FOR MULTI-DOCUMENT SUMMARIZATION
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作者 Zhang Shu Zhao Tiejun Zheng Dequan Zhao Hua 《Journal of Electronics(China)》 2008年第4期562-567,共6页
Compared with the traditional method of adding sentences to get summary in multi-document summarization,a two-stage sentence selection approach based on deleting sentences in acandidate sentence set to generate summar... Compared with the traditional method of adding sentences to get summary in multi-document summarization,a two-stage sentence selection approach based on deleting sentences in acandidate sentence set to generate summary is proposed,which has two stages,the acquisition of acandidate sentence set and the optimum selection of sentence.At the first stage,the candidate sentenceset is obtained by redundancy-based sentence selection approach.At the second stage,optimum se-lection of sentences is proposed to delete sentences in the candidate sentence set according to itscontribution to the whole set until getting the appointed summary length.With a test corpus,theROUGE value of summaries gotten by the proposed approach proves its validity,compared with thetraditional method of sentence selection.The influence of the token chosen in the two-stage sentenceselection approach on the quality of the generated summaries is analyzed. 展开更多
关键词 TWO-STAGE Sentence selection approach Multi-document summarization
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