An Optimized Framework for Energy-Resource Allocation in a Cloud Environment based on the Whale Optimization Algorithm

Goyal, Shanky; Bhushan, S.; Kumar, Yogesh; Rana, Abu ul Hassan S.; Bhutta, M. Raheel; Ijaz, Muhammad Fazal; Son, Youngdoo

doi:10.3390/s21051583

Cited by 69 publications

(31 citation statements)

References 55 publications

(50 reference statements)

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“…The results show that the developed method has the highest performance in the analysis compared to existing methods. Goyal et al [31] applied various optimization methods such as the whale Alarif and Tolba [26] proposed a reinforcement method of adaptive Q-learning (AQL) to reduce the energy and overhead tradeoff in the IoT network. The AQL method selects the cluster head and perform a forward selection in the network.…”

Section: Energy Optimization In the Cloudmentioning

confidence: 99%

“…The results show that the developed method has the highest performance in the analysis compared to existing methods. Goyal et al [31] applied various optimization methods such as the whale optimization algorithm (WOA), cuckoo search algorithm (CSA), BAT, cat swarm optimization (CSO), and particle swarm optimization (PSO) for the energy efficiency, load balancing, and better resource allocation in the cloud environment. The developed WOA method has the highest efficiency compared to the other optimization method.…”

Section: Energy Optimization In the Cloudmentioning

confidence: 99%

“…This shows that the developed method had higher performance in the analysis. Azizi et al [31] applied MinPR heuristic method to reduce the power consumption based on the active PM and prioritizing the power efficient one. The resource usage factor is used to manage the VM placement on physical machines using penalty mechanisms and rewards.…”

Section: Virtual Machine (Vm) Migration For Energy Consumptionmentioning

confidence: 99%

“…The optimization methods [26][27][28][29][30][31][32][33][34] are the most commonly applied techniques in the cloud data center to reduce the energy consumption and SLA violation. The optimization methods analyze the cloud parameters of mathematical models and provide an effective solution for the energy consumption.…”

Section: Introductionmentioning

confidence: 99%

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A Systematic Review of Energy Management Strategies for Resource Allocation in the Cloud: Clustering, Optimization and Machine Learning

Jayaprakash¹,

Nagarajan

Prado

et al. 2021

Energies

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Nowadays, many organizations and individual users are employing cloud services extensively due to their efficiency, reliability and low cost. A key aspect for cloud data centers is to achieve management methods to reduce energy consumption, increasing the profit and reducing the environmental impact, which is critical in the deployment of leading-edge technologies today such as blockchain and digital finances, IoT, online gaming and video streaming. In this review, various clustering, optimization, and machine learning methods used in cloud resource allocation to increase the energy efficiency and performance are analyzed, compared and classified. Specifically, on the one hand, we discuss how clustering methods and optimization techniques are widely applied in energy management due to their capacity to provide solutions for energy consumption reduction. On the other hand, we study how multi-objective optimization methods focus on reducing energy consumption as well as service level agreement (SLA) violation, and improving quality of services (QoS) simultaneously. Also, we discuss how optimization methods such as the firefly algorithm, whale optimization algorithm (WOA), particle swarm optimization (PSO) and genetic algorithm (GA) provide the highest performance in the field. Moreover, we analyze how machine learning methods such as deep neural network (DNN), random forest, and support vector machine (SVM) are applied to the prediction of energy consumption in the cloud, showing an accurate performance in this prediction. Nevertheless, we study how the existing methods still have limitations of low convergence, trap into local optima and overfitting.

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Section: Energy Optimization In the Cloudmentioning

confidence: 99%

“…The results show that the developed method has the highest performance in the analysis compared to existing methods. Goyal et al [31] applied various optimization methods such as the whale optimization algorithm (WOA), cuckoo search algorithm (CSA), BAT, cat swarm optimization (CSO), and particle swarm optimization (PSO) for the energy efficiency, load balancing, and better resource allocation in the cloud environment. The developed WOA method has the highest efficiency compared to the other optimization method.…”

Section: Energy Optimization In the Cloudmentioning

confidence: 99%

Section: Virtual Machine (Vm) Migration For Energy Consumptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Systematic Review of Energy Management Strategies for Resource Allocation in the Cloud: Clustering, Optimization and Machine Learning

Jayaprakash¹,

Nagarajan

Prado

et al. 2021

Energies

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“…Both the modes are described in detail in [14]. The pseudocode [26] of the Cat Swarm Optimization is given in Fig. 1.…”

Section: Introductionmentioning

confidence: 99%

Independent Task Scheduling in Cloud Computing using Meta-Heuristic HC-CSO Algorithm

Bhagwan¹,

Kumar²

2021

IJACSA

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Cloud computing is a vital paradigm of emerging technologies. It provides hardware, software, and development platforms to end-users as per their demand. Task scheduling is an exciting job in the cloud computing environment. Tasks can be divided into two categories dependent and independent. Independent tasks are not connected to any type of parent-child concept. Various meta-heuristic algorithms have come into force to schedule the independent tasks. In this, paper a hybrid HC-CSO algorithm has been simulated using independent tasks. This hybrid algorithm has been designed by using the HEFT algorithm, Self-Motivated Inertia Weight factor, and standard Cat Swarm Optimization algorithm. The Crow Search algorithm has been applied to overcome the problem of premature convergence and to avoid the H-CSO algorithm getting stuck in the local fragment. The simulation was carried out using 500-1300 random lengths independent tasks and it was found that the H-CSO algorithm has beaten PSO, ACO, and CSO algorithms whereas the hybrid algorithm HC-CSO is working fine despite Cat Swarm Optimization, Particle Swarm Optimization, and H-CSO algorithm in the name of processing cost and makespan. For all scenarios, the HC-CSO algorithm is found overall 4.15% and 7.18% efficient than the H-CSO and standard CSO respectively in comparison to the makespan and in case of computation cost minimization, 9.60% and 14.59% than the H-CSO and the CSO, respectively.

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SMOSA: Spider monkey optimization‐based scheduling algorithm for heterogeneous Hadoop

Zhang

Guan

et al. 2021

Concurrency and Computation

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Hadoop is a typical framework for processing big data. Task scheduling algorithms have a significant impact on the processing performance of Hadoop clusters. Existing scheduling algorithms of Hadoop fail to consider the performance differences between nodes in heterogeneous Hadoop clusters, causing problems such as uneven task allocation and low resource utilization. Aiming to solve this problem, we propose a spider monkey optimization-based scheduling algorithm (SMOSA) for heterogeneous Hadoop. First, the cluster heartbeat mechanism is used to obtain information such as memories and CPUs of nodes to comprehensively consider the actual load capacity of each node. Then, the spider monkey optimization algorithm is adopted to find the optimal mapping relationship between tasks and resources by taking the task completion time as the objective function and updating the position of the spider monkey.Finally, we calculate the remaining rate of node hardware resources, and according to the task type, the node with the higher remaining rate of resource will give priority to the task. Data are compressed for I/O type tasks to reduce disk operations and increase the speed of task execution. Experimental results show that, compared with existing scheduling algorithms, the SMOSA can effectively reduce task execution time and can significantly improve scheduling efficiency and task execution speed especially in heterogeneous Hadoop clusters. For different types of tasks, the execution time can be reduced by up to 19%.

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An Optimized Framework for Energy-Resource Allocation in a Cloud Environment based on the Whale Optimization Algorithm

Cited by 69 publications

References 55 publications

A Systematic Review of Energy Management Strategies for Resource Allocation in the Cloud: Clustering, Optimization and Machine Learning

A Systematic Review of Energy Management Strategies for Resource Allocation in the Cloud: Clustering, Optimization and Machine Learning

Independent Task Scheduling in Cloud Computing using Meta-Heuristic HC-CSO Algorithm

SMOSA: Spider monkey optimization‐based scheduling algorithm for heterogeneous Hadoop

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