A Task Scheduling Method for Energy-Efficient Cloud Video Surveillance System Using a Time-Clustering-Based Genetic Algorithm

Fu, Dongping; Xiong, Yan; Lu, Chengda; Wu, Min; Jiang, Keyuan

doi:10.1109/icpads.2016.0092

Cited by 2 publications

(3 citation statements)

References 15 publications

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“…x i ⟵ v i + x i (19) Evaluate ftness of all particles using objective function as per equation ( 5) (20) Update pBest i (21) end for (22) if 7 represents the graphical representation of Table 7 which shows that the ftness values achieved through the proposed approach are better than those of the standard PSO. It is performing consistently good as the number of tasks is increasing.…”

Section: Simulation and Resultsmentioning

confidence: 99%

“…A privacy model is proposed by Nabavi et al [18] where privacy and scheduling modules are defned to meet privacy and deadline of the tasks, respectively. To schedule surveillance jobs in cloud video surveillance (CVS) systems, Fu et al [19] presented a time-clustering-based genetic algorithm method. Tasks are split into multiple clusters and scheduled independently for each day based on the time period.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Modified Particle Swarm Optimization Based on Aging Leaders and Challengers Model for Task Scheduling in Cloud Computing

Chaudhary

Sharma

Thakur³

et al. 2023

Mathematical Problems in Engineering

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In cloud computing, a shared, configurable pool of computing resources is made accessible online and allocated to users according to their needs. It is essential for a cloud provider to schedule jobs in the cloud to keep up service quality and boost system efficiency. In this paper, we present a scheduling technique based on modified particle swarm optimization to combat the issues of excessively long scheduling time and high computation costs associated with scheduling jobs in a cloud environment. The modified PSO is used to allocate the jobs to virtual machines in order to minimize the objective function consisting of cost and makespan. The algorithm relies on biological changes that occur in organisms to regulate premature convergence and improve local search capability. The technique is analyzed and simulated using CloudSim, and the simulation results demonstrate that the proposed approach decreases makespan and cost effectively as compared to standard PSO.

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Section: Simulation and Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Modified Particle Swarm Optimization Based on Aging Leaders and Challengers Model for Task Scheduling in Cloud Computing

Chaudhary

Sharma

Thakur³

et al. 2023

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…For hybrid frameworks such as the ones provided by authors, Hemalatha and Valarmathi(2016), Biswas et al(2014), and Balagoni and Rao(2016), several applications including simultaneous mapping of resources to tasks, geographically segregated cloud-task mapping, and resource scheduling with SLA awareness in hybrid cloud environments, are all the examples of conceivable usage. These models are expanded further by authors Fu et al (2016), andShafiq et al (2021), which cover applications based on video surveillance, applications based on data centers, and applications based on big data analytics. These applications make use of cloud resource provisioning, and as a consequence, they provide extremely effective scheduling models as well as models that optimize resource consumption for real-time deployments.…”

Section: Literature Reviewmentioning

confidence: 99%

Ts2lbdp

Shelke

Shahapurkar²

2022

International Journal of Intelligent Information Technologies

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Few scheduling, models focus on service-level agreement (SLA) enforcement, which limits their real-time applicability. Thus, this research work proposes the design of an improved task-side service level (TS2L) agreement model for efficient task-scheduling using elephant herd optimization (EHO) with deadline (BDP) awareness. TS2LBDP incorporates pattern analysis using ensemble hierarchical, k-means, and fuzzy C means (FCM) clustering methods. A combination of these modules assists in improving task diversity and scheduling efficiency. SLA-based distribution enhances scheduling fairness and reduces mean waiting time for different clients. The proposed model was tested on parallel workload archive wherein different cloud workload logs and their respective VM configurations are described. It was observed that the proposed model improves scheduling efficiency by 20%, task diversity by 45%, and deadline hit ratio by 1%. It is scalable and can be used for several types of cloud deployments.

show abstract

A Task Scheduling Method for Energy-Efficient Cloud Video Surveillance System Using a Time-Clustering-Based Genetic Algorithm

Cited by 2 publications

References 15 publications

Modified Particle Swarm Optimization Based on Aging Leaders and Challengers Model for Task Scheduling in Cloud Computing

Modified Particle Swarm Optimization Based on Aging Leaders and Challengers Model for Task Scheduling in Cloud Computing

Ts2lbdp

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