Cost optimization in cloud provisioning using Particle Swarm Optimization

Netjinda, Nuttapong; Sirinaovakul, Booncharoen; Achalakul, Tiranee

doi:10.1109/ecticon.2012.6254298

Cited by 15 publications

(7 citation statements)

References 10 publications

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“…There are several other optimisation algorithms for CM including artificial neural network (Maqableh et al, 2014;Pooranian et al, 2014;Heckerling et al, 2004), fuzzy logic (Abd et al, 2013;Li and Zhang, 2012;Zhang et al, 2014), particle swarm optimisation (PSO) (Pandey et al, 2010;Netjinda et al, 2012;Guo et al, 2012), bee colony optimisation (Bitam, 2012;Mizan et al, 2012), bat algorithm Malakooti et al, 2013).…”

Section: Variations Of Gamentioning

confidence: 99%

A survey on cloud-based solutions for cloud manufacturing

Brintha

Benedict

2018

IJCAET

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Cloud computing is becoming an emerging technology that is changing the traditional industry and enterprises to do their work in a new way using dynamically scalable, virtualised heterogeneous resources over the internet. Nowadays, cloud manufacturing is evolving as a new technology that transforms the traditional manufacturing business model to effective reconfigurable production lines, allows effective load balancing to dynamic customer demands and reduces the time to market. In cloud manufacturing, the resources that are geographically distributed across several sectors are incorporated as cloud-based services and they are provided to users in a centralised manner. The resources can be provisioned to the users of cloud as pay as you go model depending on the user requirements.

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Section: Variations Of Gamentioning

confidence: 99%

A survey on cloud-based solutions for cloud manufacturing

Brintha

Benedict

2018

IJCAET

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“…Netjinda, Sirinaovakul and Achalakul [16] used PSO to verify cost optimization. Wang, Zhang and Si [14] improved the outcome of the cloud provider by using PSO to allocate an optimal resource for execution tasks.…”

Section: Background and Related Workmentioning

confidence: 99%

Efficient Task Scheduling Multi-Objective Particle Swarm Optimization in Cloud Computing

Alkayal¹,

Jennings

Abulkhair³

2016

2016 IEEE 41st Conference on Local Computer Networks Workshops (LCN Workshops)

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Abstract-Task scheduling in data centers is a complex task due to their evolution in size, complexity, and performance. At the same time, customers' requirements have become more sophisticated in terms of execution time and throughput. Against this background, this work presents a new model of resource allocation that optimizes task scheduling using a multi-objective optimization (MOO) and particle swarm optimization (PSO) algorithm. In more detail, we develop a novel multi-objective PSO (MOPSO) algorithm, based on a new ranking strategy. The main insight of this algorithm is that the tasks are scheduled to the virtual machines to minimize waiting time and maximize system throughput. The algorithm leads to a reduction in execution time of 20%, a reduction the waiting time of 30%, and shows improvements of up to 40% in throughput compared to the current state of the art.

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“…Xiao, et al [21] proposed a theory of resource distribution and allocation which can easily limit the energy utilization processes by optimized planning. A PSO based provisioning technique was introduced by Netjinda, et al [22] for cost minimization and cloud provisioning and arrangement. Ding, et al [23] assessed various techniques involving the resource allocation methodology for QOS aware applications while taking the nature of resources and various other related factors into consideration.…”

Section: Introductionmentioning

confidence: 99%

Cloud based Resource Scheduling Methodology for Data-Intensive Smart Cities and Industrial Applications

Chen

Zhang

et al. 2021

SCPE

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For the data-intensive applications, resource planning and scheduling has become an important part for smart cities. The cloud computing techniques are being used for planning and scheduling of resources in data-intensive applications. The regular methodologies being used are adequately successful for giving the asset allotment yet they do not provide time effectiveness during task execution. This article presents an effective and time prioritization based smart resource management platform employing the Cuckoo Search based Optimized Resource Allocation (CSO-RA) methodology. The opensource JStorm platform is utilized for dynamic asset planning while using big data analytics and the outcomes of the experimentation are observed using various assessment parameters. The proposed (CSO-RA) system is compared with the current methodologies like particle swarm optimization (PSO), ant colony optimization (ACO) and genetic algorithm (GA) based optimization methodologies and the viability of the proposed framework is established. The percentage of optimality observed for CSO-RA algorithm is 97\% and overall resource deployment rate of 28\% is achieved using CSO-RA method which is comparatively much better than PSO, GA and ACO conventional algorithms. Feasible outcomes are obtained by using the CSO-RA methodology for cloud computing based large scale optimization-based data intensive industrial applications.

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Cost optimization in cloud provisioning using Particle Swarm Optimization

Cited by 15 publications

References 10 publications

A survey on cloud-based solutions for cloud manufacturing

A survey on cloud-based solutions for cloud manufacturing

Efficient Task Scheduling Multi-Objective Particle Swarm Optimization in Cloud Computing

Cloud based Resource Scheduling Methodology for Data-Intensive Smart Cities and Industrial Applications

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