Virtual machine scheduling strategy based on machine learning algorithms for load balancing

Xin, Shihe; Liú, Dan; Li, Li; Wang, Huan; Yang, Hongwei

doi:10.1186/s13638-019-1454-9

Cited by 33 publications

(11 citation statements)

References 12 publications

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“…These parameters are again divided into primary and secondary parameters. Primary parameters include but are not limited to number of execution units available, capacity of each unit to execute the task, execution requirements for the resources, and others (Sui et al, 2019). A typical task scheduler can be observed from figure 1, wherein the tasks coming from users are given to the data center broker, the broker sends these tasks to the cloud controller for processing.…”

Section: T F T T T Tmentioning

confidence: 99%

Improvement in Task Scheduling Capabilities for SaaS Cloud Deployments Using Intelligent Schedulers

Sawwashere

2021

International Journal of Big Data and Analytics in Healthcare

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Task scheduling on the cloud involves processing a large set of variables from both the task side and the scheduling machine side. This processing often results in a computational model that produces efficient task to machine maps. The efficiency of such models is decided based on various parameters like computational complexity, mean waiting time for the task, effectiveness to utilize the machines, etc. In this paper, a novel Q-Dynamic and Integrated Resource Scheduling (DAIRS-Q) algorithm is proposed which combines the effectiveness of DAIRS with Q-Learning in order to reduce the task waiting time, and improve the machine utilization efficiency. The DAIRS algorithm produces an initial task to machine mapping, which is optimized with the help of a reward & penalty model using Q-Learning, and a final task-machine map is obtained. The performance of the proposed algorithm showcases a 15% reduction in task waiting time, and a 20% improvement in machine utilization when compared to DAIRS and other standard task scheduling algorithms.

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Section: T F T T T Tmentioning

confidence: 99%

Improvement in Task Scheduling Capabilities for SaaS Cloud Deployments Using Intelligent Schedulers

Sawwashere

2021

International Journal of Big Data and Analytics in Healthcare

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“…This technique diminishes the PM's frenzied energy and increases the breach of SLA. VM scheduling strategies based on machine learning algorithms for load balancing is suggested in [4 ]. The proposed method measures PM performance interference by clustering operation using the K‐means clustering algorithm.…”

Section: Related Workmentioning

confidence: 99%

Rough‐set and machine learning‐based approach for optimised virtual machine utilisation in cloud computing

Shylaja

Bhaskar

2020

IET Networks

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To meet rising demands for computing resources, information technology service providers need to select cloudbased services for their vitality and elasticity. Enormous numbers of data centres are designed to meet customer needs. Burning up energy by data centre is very high with the large-scale deployment of cloud data centres. Virtual machine consolidation strategy implementation reduces the data centre energy consumption and guarantees service level agreements. This study proposes a machine learning-based method in cloud computing for the automated use of virtual machines. Machine learningbased virtual machine selection approach integrates the migration control mechanism that enhances selection strategy efficiency. The experiment is performed with various real machine workload circumstances to provide proof and effectiveness of the proposed method. The exploratory outcome shows that the proposed approach streamlines the utilisation of the virtual machine and diminishes the consumption of energy and improves infringement of service level agreements to accomplish better performance.

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“…Recently, machine learning models have made a huge impact in achieving performance efficiency in dynamic load balancing in the cloud [18]. These machine learning models are being integrated with load balancing algorithms resulting in hybrid techniques [19,20]. Classification is one of the important concepts of supervised machine learning which further divides into SVM [21], Discriminant Analysis [22], Naïve Bayes (NB) [23], K-Nearest Neighbor (K-NN) [24], and Neural Networks (NN) [25].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Cloud Performance Using File Format Classifications

Junaid¹,

Sohail²,

Alkinani³

et al. 2022

Computers, Materials &Amp; Continua

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Metaheuristic approaches in cloud computing have shown significant results due to their multi-objective advantages. These approaches are now considering hybrid metaheuristics combining the relative optimized benefits of two or more algorithms resulting in the least tradeoffs among several factors. The critical factors such as execution time, throughput time, response time, energy consumption, SLA violations, communication overhead, makespan, and migration time need careful attention while designing such dynamic algorithms. To improve such factors, an optimized multi-objective hybrid algorithm is being proposed that combines the relative advantages of Cat Swarm Optimization (CSO) with machine learning classifiers such as Support Vector Machine (SVM). The adopted approach is based on SVM one to many classification models of machine learning that performs the classifications of various data format types in the cloud with best accuracy. In CSO, grouping phase is used to divide the data files as audio, video, image, and text which is further extended by polynomial Kernel function based on various input features and used for optimized load balancing. Overall, proposed approach works well and achieved performance efficiency in evaluated QoS metrics such as average energy consumption by 12%, migration time by 9%, and optimization time by 10%, in the presence of competitor baselines.

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Virtual machine scheduling strategy based on machine learning algorithms for load balancing

Cited by 33 publications

References 12 publications

Improvement in Task Scheduling Capabilities for SaaS Cloud Deployments Using Intelligent Schedulers

Improvement in Task Scheduling Capabilities for SaaS Cloud Deployments Using Intelligent Schedulers

Rough‐set and machine learning‐based approach for optimised virtual machine utilisation in cloud computing

Enhancing Cloud Performance Using File Format Classifications

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