Matches in SemOpenAlex for { <https://semopenalex.org/work/W4243739507> ?p ?o ?g. }
Showing items 1 to 76 of
76
with 100 items per page.
- W4243739507 endingPage "3049" @default.
- W4243739507 startingPage "3040" @default.
- W4243739507 abstract "Cloud computing is widely used resource sharing computational technology to provide fast, reliable, and scalable computational process for organizations and companies without the need to build and maintain their own server. The research area about cloud computing is dynamic and versatile. One may have concern on the privacy, security, networking, optimization, etc. Due to huge demand for cloud computing, it creates several problems such as makespan, energy consumption, and load balancing. Task scheduling is one of the technologies that have been applied to solve those objectivities. However, task scheduling is one of the well-known NP-hard problems, and it is difficult to find the optimum solution. In order to solve this problem, previous studies have utilized meta-heuristic method to find the best solution based on the solution spaces. This study proposed Particle Swarm Optimization (PSO) to solve the multi-objective task scheduling to achieve the optimum solution. The effectiveness of the proposed algorithm will be compared with Genetic Algorithm (GA), Clonal Selection Algorithm (CSA), and Bat Algorithm (BA). This study converts three objectivities into single objectivity optimization with each objectivity act as variable assigned with weight that present its priority and has implemented those meta-heuristics. The simulation result from ten data set shows that PSO able to outperform GA, CSA, and BA especially for makespan and energy consumption without the cost of algorithm duration since PSO has fast convergence rate compare to the other three algorithms and making it a good choice for dynamic task scheduling in data center cloud computing where the algorithm duration is one of important factor" @default.
- W4243739507 created "2022-05-12" @default.
- W4243739507 creator A5025702246 @default.
- W4243739507 creator A5087785828 @default.
- W4243739507 date "2019-11-30" @default.
- W4243739507 modified "2023-09-26" @default.
- W4243739507 title "An Optimization of Makespan, Energy Consumption, and Load Balancing on The Task Scheduling in Cloud Computing using Particle Swarm Optimization (PSO)" @default.
- W4243739507 doi "https://doi.org/10.35940/ijrte.d7738.118419" @default.
- W4243739507 hasPublicationYear "2019" @default.
- W4243739507 type Work @default.
- W4243739507 citedByCount "1" @default.
- W4243739507 countsByYear W42437395072022 @default.
- W4243739507 crossrefType "journal-article" @default.
- W4243739507 hasAuthorship W4243739507A5025702246 @default.
- W4243739507 hasAuthorship W4243739507A5087785828 @default.
- W4243739507 hasBestOaLocation W42437395071 @default.
- W4243739507 hasConcept C111919701 @default.
- W4243739507 hasConcept C11413529 @default.
- W4243739507 hasConcept C119599485 @default.
- W4243739507 hasConcept C120314980 @default.
- W4243739507 hasConcept C126255220 @default.
- W4243739507 hasConcept C127413603 @default.
- W4243739507 hasConcept C127705205 @default.
- W4243739507 hasConcept C138959212 @default.
- W4243739507 hasConcept C187691185 @default.
- W4243739507 hasConcept C206729178 @default.
- W4243739507 hasConcept C2524010 @default.
- W4243739507 hasConcept C2779907789 @default.
- W4243739507 hasConcept C2780165032 @default.
- W4243739507 hasConcept C31258907 @default.
- W4243739507 hasConcept C33923547 @default.
- W4243739507 hasConcept C41008148 @default.
- W4243739507 hasConcept C55416958 @default.
- W4243739507 hasConcept C74172769 @default.
- W4243739507 hasConcept C79974875 @default.
- W4243739507 hasConcept C85617194 @default.
- W4243739507 hasConceptScore W4243739507C111919701 @default.
- W4243739507 hasConceptScore W4243739507C11413529 @default.
- W4243739507 hasConceptScore W4243739507C119599485 @default.
- W4243739507 hasConceptScore W4243739507C120314980 @default.
- W4243739507 hasConceptScore W4243739507C126255220 @default.
- W4243739507 hasConceptScore W4243739507C127413603 @default.
- W4243739507 hasConceptScore W4243739507C127705205 @default.
- W4243739507 hasConceptScore W4243739507C138959212 @default.
- W4243739507 hasConceptScore W4243739507C187691185 @default.
- W4243739507 hasConceptScore W4243739507C206729178 @default.
- W4243739507 hasConceptScore W4243739507C2524010 @default.
- W4243739507 hasConceptScore W4243739507C2779907789 @default.
- W4243739507 hasConceptScore W4243739507C2780165032 @default.
- W4243739507 hasConceptScore W4243739507C31258907 @default.
- W4243739507 hasConceptScore W4243739507C33923547 @default.
- W4243739507 hasConceptScore W4243739507C41008148 @default.
- W4243739507 hasConceptScore W4243739507C55416958 @default.
- W4243739507 hasConceptScore W4243739507C74172769 @default.
- W4243739507 hasConceptScore W4243739507C79974875 @default.
- W4243739507 hasConceptScore W4243739507C85617194 @default.
- W4243739507 hasIssue "4" @default.
- W4243739507 hasLocation W42437395071 @default.
- W4243739507 hasOpenAccess W4243739507 @default.
- W4243739507 hasPrimaryLocation W42437395071 @default.
- W4243739507 hasRelatedWork W149786246 @default.
- W4243739507 hasRelatedWork W1987415113 @default.
- W4243739507 hasRelatedWork W2025299305 @default.
- W4243739507 hasRelatedWork W2087685365 @default.
- W4243739507 hasRelatedWork W2546696010 @default.
- W4243739507 hasRelatedWork W2564093197 @default.
- W4243739507 hasRelatedWork W2809578401 @default.
- W4243739507 hasRelatedWork W3199056070 @default.
- W4243739507 hasRelatedWork W2182744551 @default.
- W4243739507 hasRelatedWork W2189110573 @default.
- W4243739507 hasVolume "8" @default.
- W4243739507 isParatext "false" @default.
- W4243739507 isRetracted "false" @default.
- W4243739507 workType "article" @default.