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Optimizing Cloud Storage Costs: Introducing the Pre-Evaluation-Based Cost Optimization (PECSCO) Mechanism

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dc.citedby0
dc.contributor.authorAlomari M.F.en_US
dc.contributor.authorMahmoud M.A.en_US
dc.contributor.authorGharaei N.en_US
dc.contributor.authorRasool S.M.en_US
dc.contributor.authorHasan R.A.en_US
dc.contributor.authorid57350402200en_US
dc.contributor.authorid55247787300en_US
dc.contributor.authorid57194518462en_US
dc.contributor.authorid57350402300en_US
dc.contributor.authorid58487876600en_US
dc.date.accessioned2025-03-03T07:45:29Z
dc.date.available2025-03-03T07:45:29Z
dc.date.issued2024
dc.description.abstractInnovative cloud computing system offers cutting-edge storage models that prioritize the importance of data, adaptive algorithms for controlling data flow, and cost-effective computational procedures. Current models often encounter difficulties in effectively managing the trade-off between cost reduction and performance enhancement, especially when dealing with substantial amounts of data and unexpected access patterns. Nonetheless, cloud service providers impose fees on users based on the volume of data transmitted to and from cloud storage, resulting in elevated storage costs. Consequently, assessing and confirming the significance of packets (data) before its synchronization with cloud storage becomes imperative. The major contribution of this work lies in the develop of Prior Evaluation Cloud Storage Cost Optimization called (PECSCO) mechanism to optimize the cloud cost with least overhead. The proposed algorithm aimed at reducing cloud storage cost by strategically determining the best locations for evaluators within a network of nodes for efficient monitoring, particularly in surveillance contexts indicated by the mention of CCTVs. The core of the algorithm utilizes a Genetic Algorithm (GA) to find the optimal position for the first evaluator by minimizing the total distance between this evaluator and all CCTV nodes, aiming for surveillance efficiency. A similar process is undertaken for the second evaluator, with the goal of minimizing the distance to critical logic output nodes, ensuring crucial areas are under effective oversight. The Evaluation of the effectiveness of PECSCO mechanism was done by comparing it with existing algorithms like ODAF-TS and OCOA. The results revealed that PECSCO has demonstrated the ability to excel in sensor networks and computational jobs that need both high efficiency and the capacity to handle a significant number of operations. The use of evaluators in the PECSCO mechanism to pre-assess data prior to synchronization with cloud storage has been shown to substantially decrease cloud expenses. Nevertheless, this economical approach poses the difficulty of preserving system responsiveness and the expandability of the assessment procedure. ? 2024 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1109/ICSINTESA62455.2024.10748165
dc.identifier.epage569
dc.identifier.scopus2-s2.0-85211611118
dc.identifier.spage564
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85211611118&doi=10.1109%2fICSINTESA62455.2024.10748165&partnerID=40&md5=982d2d0ce03548fb948cade5f087d74e
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/36885
dc.pagecount5
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitleICSINTESA 2024 - 2024 4th International Conference of Science and Information Technology in Smart Administration: The Collaboration of Smart Technology and Good Governance for Sustainable Development Goals
dc.subjectCloud platforms
dc.subjectCloud storage
dc.subjectCost reduction
dc.subjectCloud cost
dc.subjectCloud storages
dc.subjectCloud-computing
dc.subjectCosts Optimization
dc.subjectData pre-evaluation
dc.subjectGenetic algorithm
dc.subjectOptimisations
dc.subjectPre-evaluation
dc.subjectSensors network
dc.subjectStorage costs
dc.subjectGenetic algorithms
dc.titleOptimizing Cloud Storage Costs: Introducing the Pre-Evaluation-Based Cost Optimization (PECSCO) Mechanismen_US
dc.typeConference paperen_US
dspace.entity.typePublication
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