A Heuristic Clustering-Based Task Deployment Approach for Load Balancing Using Bayes Theorem in Cloud Environment

Zhao, Jia; Yang, Kun; Wei, Xiaohui; Ding, Yan; Hu, Liang; Xu, Gaochao

doi:10.1109/tpds.2015.2402655

Cited by 149 publications

(52 citation statements)

References 19 publications

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“…[14]- [18]. For example in [18], the authors presented a new approach for horizontally scaling cloud resources, and in [19] for load balancing, and resources scheduling [20] in cloud. In [21] the authors presented the cloud concept and its emerged services that deal with the IoT trends, and they notice also that the applications with complex data-intensive computations are the best candidate to take advantages of cloud computing.…”

Section: B Resultsmentioning

confidence: 99%

Toward a New Massively Distributed Virtual Machine based Cloud Micro-Services Team Model for HPC: SPMD Applications

Benchara¹,

Youssfi²,

Bouattane³

et al. 2017

ijacsa

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Abstract-This paper aims to propose a new massively distributed virtual machine with scalable and efficient parallel computing models for High Performance Computing (HPC). The message passing paradigm of the Processing Units has a significant impact on HPC with high communication cost that penalizes the performance of these models. Accordingly, the proposed micro-services model allows the HPC applications to enhance the processing power with low communication cost. Thus, the model based Micro-services Virtual Processing Units (MsVPUs) cooperate using asynchronous communication mechanism through the Advanced Message Queuing Protocol (AMQP) protocol in order to maintain the scalability of the Single Program Multiple Data (SPMD) applications. Additionally, this mechanism enhances also the efficiency of the model based load balancing service with time optimized load balancing strategy. The proposed virtual machine is tested and validated through an application of fine grained parallel programs for big data classification. Experimental results present reduced execution time compared to the virtual machine based mobile agent's model.

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Section: B Resultsmentioning

confidence: 99%

Toward a New Massively Distributed Virtual Machine based Cloud Micro-Services Team Model for HPC: SPMD Applications

Benchara¹,

Youssfi²,

Bouattane³

et al. 2017

ijacsa

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show abstract

“…In [39], the remaining resource of physical host L i is defined to represent the performance power of physical host L i :…”

Section: Residual Available Capacity Modelmentioning

confidence: 99%

Energy Efficient Resource Selection and Allocation Strategy for Virtual Machine Consolidation in Cloud Datacenters

Chang

Luo

et al. 2018

IEICE Trans. Inf. & Syst.

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SUMMARYVirtual Machine Placement (VMP) plays an important role in ensuring efficient resource provisioning of physical machines (PMs) and energy efficiency in Infrastructure as a Service (IaaS) data centers. Efficient server consolidation assisted by virtual machine (VM) migration can promote the utilization level of the servers and switch the idle PMs to sleep mode to save energy. The trade-off between energy and performance is difficult, because consolidation may cause performance degradation, even service level agreement (SLA) violations. A novel residual available capacity (RAC) resource model is proposed to resolve the VM selection and allocation problem from the cloud service provider (CSP) perspective. Furthermore, a novel heuristic VM selection policy for server consolidation, named Minimized Square Root available Resource (MISR) is proposed. Meanwhile, an efficient VM allocation policy, named Balanced Selection (BS) based on RAC is proposed. The effectiveness validation of the BS-MISR combination is conducted on CloudSim with real workloads from the CoMon project. Evaluation results of experiments show that the proposed combination BS-MISR can significantly reduce the energy consumption, with an average of 36.35% compared to the Local Regression and Minimum Migration Time (LR-MMT) combination policy. Moreover, the BS-MISR ensures a reasonable level of SLAs compared to the benchmarks.

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“…In this paper [12], here author proposed LB-BC method that is based on heuristic job deployment method, which is utilized to organize job requests accepted by the cloud data center into optimal objective physical hosts in the IaaS cloud computing data center. Its algorithm is what joins Bayes theorem with clustering.…”

Section: Literature Surveymentioning

confidence: 99%

An Effective Method for Load Balancing using Modified Active Monitoring based Ant Clustering

Rajput¹,

Mishra²,

Jain³

2017

IJCA

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Load balancing is a technique of balancing the load on virtual machines for the number of requests coming from the Users to access resources over Data Centers. Here in this paper a new and effectual tactic for Load Balancing over computing is proposed using Modified Active Load Balancing by Ant based Clustering Algorithm. The Existing technique implemented for Load balancing on Cloud Simulators fails to provide efficient load balancing, hence a new approach is proposed where Clustering is done ant based gathering on the basis of Utilization of Virtual Machines. The Proposed methodology provides efficient Throughput and MakeSpan time.

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A Heuristic Clustering-Based Task Deployment Approach for Load Balancing Using Bayes Theorem in Cloud Environment

Cited by 149 publications

References 19 publications

Toward a New Massively Distributed Virtual Machine based Cloud Micro-Services Team Model for HPC: SPMD Applications

Toward a New Massively Distributed Virtual Machine based Cloud Micro-Services Team Model for HPC: SPMD Applications

Energy Efficient Resource Selection and Allocation Strategy for Virtual Machine Consolidation in Cloud Datacenters

An Effective Method for Load Balancing using Modified Active Monitoring based Ant Clustering

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