An adaptive approach to better load balancing in a consumer-centric cloud environment

Liu, Qi; Cai, Weidong; Shen, Jian; Liu, Xiaodong; Linge, Nigel

doi:10.1109/tce.2016.7613190

Cited by 21 publications

(8 citation statements)

References 22 publications

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“…Some of the relevant papers are discussed here to understand the view of technique. Q i L i u et al [7] suggested an adaptive approach for allocating tasks evenly to improve time-space efficiency through map reduce function along with Multiobjective algorithm. Here the algorithm optimizes the execution time by map phase and prediction of execution time is achieved by reducer and the load balance is maintained by using Multiobjective algorithm.…”

Section: Related Workmentioning

confidence: 99%

Pareto Based Virtual Machine Selection with Load Balancing in Cloud Data Centre

Naik

Gandhi

Patil

2018

Cybernetics and Information Technologies

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Cloud Data centers have adopted virtualization techniques for effective and efficient compilation of an application. The requirements of application from the execution perspective are fulfilled by scaling up and down the Virtual Machines (VMs). The appropriate selection of VMs to handle the unpredictable peak workload without load imbalance is a critical challenge for a cloud data center. In this article, we propose Pareto based Greedy-Non dominated Sorting Genetic Algorithm-II (G-NSGA2) for agile selection of a virtual machine. Our strategy generates Pareto optimal solutions for fair distribution of cloud workloads among the set of virtual machines. True Pareto fronts generate approximate optimal trade off solution for multiple conflicting objectives rather than aggregating all objectives to obtain single trade off solution. The objectives of our study are to minimize the response time, operational cost and energy consumption of the virtual machine. The simulation results evaluate that our hybrid NSGA-II outperforms as compared to the standard NSGA-II Multiobjective optimization problem.

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Section: Related Workmentioning

confidence: 99%

Pareto Based Virtual Machine Selection with Load Balancing in Cloud Data Centre

Naik

Gandhi

Patil

2018

Cybernetics and Information Technologies

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“…Liu et al proposed a new model by collecting real-time data, which has achieved higher accuracy [24]. A dynamic strategy has been proposed based on an exponential smoothing model for each phase [25].…”

Section: Related Workmentioning

confidence: 99%

“…While theoretically infinite computing resources can be provided in a cloud, the unreasonable increment of mappers/reducers cannot achieve process efficiency, and may waste more storage to complete. Many optimization schemes have been proposed [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

An Adaptively Speculative Execution Strategy Based on Real-Time Resource Awareness in a Multi-Job Heterogeneous Environment

2017

KSII TIIS

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MapReduce (MRV1), a popular programming model, proposed by Google, has been well used to process large datasets in Hadoop, an open source cloud platform. Its new version MapReduce 2.0 (MRV2) developed along with the emerging of Yarn has achieved obvious improvement over MRV1. However, MRV2 suffers from long finishing time on certain types of jobs. Speculative Execution (SE) has been presented as an approach to the problem above by backing up those delayed jobs from low-performance machines to higher ones. In this paper, an adaptive SE strategy (ASE) is presented in Hadoop-2.6.0. Experiment results have depicted that the ASE duplicates tasks according to real-time resources usage among work nodes in a cloud. In addition, the performance of MRV2 is largely improved using the ASE strategy on job execution time and resource consumption, whether in a multi-job environment.

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“…These preemption techniques can be costly and should be employed with care. In order to reduce the number of such events but still avoid overloading the host, it is important to find an effective initial VM allocation, which takes into consideration the behavior of each application. To achieve this, the scheduler must work in unison with the local host elasticity controllers to reduce interference.…”

Section: Introductionmentioning

confidence: 99%

Static job scheduling for environments with vertical elasticity

Kloh

Rebello

Boeres

et al. 2020

Concurrency and Computation

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Summary In virtualized environments, such as Clouds, allocating a fixed amount of resources to a job a priori, may result in underutilization of the shared host. Meanwhile, vertical elasticity can be adopted to reduce the impact by resizing virtual machines (VMs) dynamically, in conjunction with suspension and/or migration before the host had been overloaded. In order to reduce the number of such events, but still avoid overloading the host, it is important to find an effective initial VM allocation. To achieve this, the scheduler must work in unison with the local host elasticity controllers to reduce interference. This work proposes and evaluates a framework for job scheduling in elastic memory managed virtualized environments. The memory elasticity management in clouds (MEMiC) framework is a two‐tier VM scheduler for batch jobs which attempts to predict the impact caused by competition for the memory of a host in shared Cloud‐like environments for harnessing VM allocation, suspension, and migration. Evaluations show that the MEMiC achieves a reduction of approximately 19% in the total time for jobs execution in comparison to other approaches, by reducing the degree of interference.

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An adaptive approach to better load balancing in a consumer-centric cloud environment

Cited by 21 publications

References 22 publications

Pareto Based Virtual Machine Selection with Load Balancing in Cloud Data Centre

Pareto Based Virtual Machine Selection with Load Balancing in Cloud Data Centre

An Adaptively Speculative Execution Strategy Based on Real-Time Resource Awareness in a Multi-Job Heterogeneous Environment

Static job scheduling for environments with vertical elasticity

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