Adaptive task scheduling method in multi-tenant cloud computing

Ramegowda, Ashalatha; Agarkhed, Jayashree; Patil, Siddarama R.

doi:10.1007/s41870-019-00389-5

Cited by 10 publications

(3 citation statements)

References 29 publications

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“…By comparing several different methods, taking the occupied space of the virtual resource scheduling process as the experimental index, and taking the methods of literature [2] and literature [3] as the comparison method, the simplicity of the application process of this method is verified. The experimental results are shown in Fig.…”

Section: B Experimental Resultsmentioning

confidence: 99%

“…Laith & Ali [2] presented a hybrid ant colony optimization algorithm based on elitist differential evolution is proposed to solve the multi-objective task scheduling problem in cloud computing environment, which minimizes the maximum completion time, maximizes the resource utilization, and uses elitist differential evolution as a local search technique to improve its utilization ability and avoid falling into local optimization. In [3], by satisfying the constraint of security requirements, we find an optimal scheduling algorithm with minimum completion time and cost, and users can submit their security requirements to the cloud provider during the negotiation period.…”

Section: Introductionmentioning

confidence: 99%

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An Improved Ant Colony Algorithm for Virtual Resource Scheduling in Cloud Computing

Zhong¹,

Ye²

2023

IJACSA

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In order to solve the problems of uneven spatial distribution of data nodes and unclear weight relationship of virtual scheduling features in cloud computing platform, a virtual resource scheduling method based on improved ant colony algorithm is studied and designed to improve the performance of virtual resource scheduling in cloud computing platform by this method. After analyzing the information resource sequence change of the cloud computing platform, according to the STR -Tree partition graph, a simulated annealing-based algorithm is employed to classify the resource types after optimal scheduling into IO types, middle types and CPU types, and the time span and load balance are set as the measurement indexes. The simulation results show that after applying this method, the occupied resources of the main platform are 535 MB, which are much lower than the other two comparison algorithms, and the method has improved the allocation rationality, resource balance, maximum queue length and energy consumption. This result indicates that applying this virtual resource scheduling method can effectively improve the intelligent scheduling of virtual resources in the cloud computing platform.

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Section: B Experimental Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Improved Ant Colony Algorithm for Virtual Resource Scheduling in Cloud Computing

Zhong¹,

Ye²

2023

IJACSA

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“…The Adaptive PSO method is depicted in Figure 5. APSO model increases energy efficiency using a VM consolidation strategy and dynamic VM migration process [30]. The energy consumption model includes cloud migration, CPU utilization rate, memory, disk, VM load, RAM consumed, and bandwidth availability [31].…”

Section: 1mentioning

confidence: 99%

Multi Objective Task Scheduling VM Placement Method in Cloud Computing Environment Using Resource Optimization Technique

RAMEGOWDA

2024

Preprint

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The single-tenant-based applications consume high bandwidth and energy for each client. The multi-tenancy process adopts Software as a Service (SaaS) capability that allows a single model executing the service provider's platform to be accessed by numerous clients simultaneously. The virtual machine-based optimization method is required in the cloud computing domain as a dynamic resource scheduling method situated on unanticipated workloads. The multi-tenancy method provides the access to hardware and computing resources using the Infrastructure as a Service (IaaS) model. This article proposes the Adaptive Particle Swarm Optimization (APSO) method in a multi-tenant environment. The paper defines virtual machine placement, allowing cloud providers to provide powerful resource utilization capabilities.

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An efficient task scheduling for weather forecasting suites in high performance computing

Nath

Nagaraju

2021

Int. j. inf. tecnol.

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Adaptive task scheduling method in multi-tenant cloud computing

Cited by 10 publications

References 29 publications

An Improved Ant Colony Algorithm for Virtual Resource Scheduling in Cloud Computing

An Improved Ant Colony Algorithm for Virtual Resource Scheduling in Cloud Computing

Multi Objective Task Scheduling VM Placement Method in Cloud Computing Environment Using Resource Optimization Technique

An efficient task scheduling for weather forecasting suites in high performance computing

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