IAGA: Interference Aware Genetic Algorithm based VM Allocation Policy for Cloud Systems

Bloch, Tarannum Alimahmad; Rajagopal, Sridaran; Ranga, Prashanth C.

doi:10.14569/ijacsa.2022.0130488

Cited by 3 publications

(1 citation statement)

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“…Service quality and lifespan are prioritised to reduce DC energy usage and resource under utilization. Mohammed et al 18 32 to lessen SLAV and performance deterioration on the IaaS platform.…”

Section: Related Workmentioning

confidence: 99%

Energy Optimized Workflow Scheduling in IaaS Cloud: A Flower Pollination based Approach

Jaiprakash,

Arya,

Gupta

et al. 2023

Preprint

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The energy consumption of cloud data centers is a critical concern that could affect both the environment and the availability of energy resources. For this, the global community and industries are taking measures to address this issue that is caused by the high electricity consumption of servers, Heating, Ventilation, and Air Conditioning (HVAC) in the data centers. With this context, this paper presents a novel approach for scheduling energy-efficient workflows (EEWS) in cloud computing using the MaxUtil model. The proposed approach incorporates the flower pollination algorithm (FPA), a popular meta-heuristic algorithm inspired by nature. The primary objectives of the proposed scheduling scheme are to minimize energy consumption and workflow processing time (makespan). The proposed algorithm involves two key phases: (i) assigning tasks to available virtual machines (VMs) and (ii) scheduling the tasks based on optimal criteria. As per our knowledge, this is the first study that focuses on optimizing energy consumption and makespan in cloud computing workflow scheduling using FPA. The proposed approch employs an effective representation of pollen and dynamic fitness function with multi-objective. The advantage of FPA lies in its speed of convergence and providing feasible solutions. Extensive studies have been conducted across five different scientific workflows from various fields. The proposed algorithm outperforms traditional workflow scheduling algorithms based on particle swarm optimization (PSO), gravitational search algorithms (GSA) and genetic algorithm (GA). The proposed algorithm outperforms GA, PSO, and GSA in the majority of cases, according to simulation findings. In addition, a well-known statistical test known as variance analysis (ANOVA) is used to validate the experimental results of the suggested algorithm. Based on the result’s of ANOVA test, the article claims that the suggested algorithm is superior to existing methods.

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Section: Related Workmentioning

confidence: 99%

Energy Optimized Workflow Scheduling in IaaS Cloud: A Flower Pollination based Approach

Jaiprakash,

Arya,

Gupta

et al. 2023

Preprint

View full text Add to dashboard Cite

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A Metaheuristic-Based Scientific Workflow Scheduling in IaaS Cloud

Jaiprakash,

Badal,

Gupta

et al. 2024

2024 1st International Conference on Cognitive, Green and Ubiquitous Computing (IC-CGU)

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Multi-Objective Hybrid Flower Pollination Resource Consolidation Scheme for Large Cloud Data Centres

et al. 2022

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Cloud Computing has rapidly emerged as a successful paradigm for providing Information and Communication Technology (ICT) infrastructure. Resource allocation is used to execute user applications in the form of requests for consolidated resources in order to minimize energy consumption and violation of the Service Level Agreement (SLA) for large-scale data centers resource utilization. The energy consumption is usually caused due to local entrapment and violation of SLA during resource assigning and execution. Several researchers have proposed solutions to reduce local entrapments and violations of SLA, to minimize the energy consumption of the entire data center. However, strategies employed in their solutions face entrapment in either local searches or at the global search level with a certain level of SLA violation. In this light, a Multi-Objective Hybrid Flower Pollination Resource Consolidation (MOH-FPRC) scheme for efficient and optimal resource consolidation of data center resources is put forward. The Local Neighborhood Search (LNS) algorithm has been employed for addressing entrapment at the local search level, while the prominent flower pollination algorithm is used to solve the problem of entrapment at the global search level. This, in turn, reduces the energy consumption of the data centers. In addition, clustering strategies have been introduced with a robust migration mechanism to minimize the violation of SLA while also satisfying minimum energy consumption. The simulation results using the MultiRecCloudSim simulator have shown that our proposed MOH-FPRC demonstrates an improved performance on the data center energy consumption, resource utilization, and SLA violation with a 20.5% decrease, 23.9% increase, and 13.5% reduction, respectively, as compared with the benchmarked algorithms. The proposed scheme has proven its efficiency in minimizing energy consumption while at the same time improving the data center resource allocation with minimum SLA violations.

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IAGA: Interference Aware Genetic Algorithm based VM Allocation Policy for Cloud Systems

Cited by 3 publications

References 0 publications

Energy Optimized Workflow Scheduling in IaaS Cloud: A Flower Pollination based Approach

Energy Optimized Workflow Scheduling in IaaS Cloud: A Flower Pollination based Approach

A Metaheuristic-Based Scientific Workflow Scheduling in IaaS Cloud

Multi-Objective Hybrid Flower Pollination Resource Consolidation Scheme for Large Cloud Data Centres

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