Energy efficient temporal load aware resource allocation in cloud computing datacenters

Vakilinia, Shahin

doi:10.1186/s13677-017-0103-2

Cited by 41 publications

(19 citation statements)

References 66 publications

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“…Vakilinia, S. considered optimization problem in the cloud data centers in [26]. A workload is increased when a number of jobs arrive at the same time.…”

Section: Related Workmentioning

confidence: 99%

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An Enhanced Multi-Objective Gray Wolf Optimization for Virtual Machine Placement in Cloud Data Centers

et al. 2019

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Cloud computing offers various services. Numerous cloud data centers are used to provide these services to the users in the whole world. A cloud data center is a house of physical machines (PMs). Millions of virtual machines (VMs) are used to minimize the utilization rate of PMs. There is a chance of unbalanced network due to the rapid growth of Internet services. An intelligent mechanism is required to efficiently balance the network. Multiple techniques are used to solve the aforementioned issues optimally. VM placement is a great challenge for cloud service providers to fulfill the user requirements. In this paper, an enhanced levy based multi-objective gray wolf optimization (LMOGWO) algorithm is proposed to solve the VM placement problem efficiently. An archive is used to store and retrieve true Pareto front. A grid mechanism is used to improve the non-dominated VMs in the archive. A mechanism is also used for the maintenance of an archive. The proposed algorithm mimics the leadership and hunting behavior of gray wolves (GWs) in multi-objective search space. The proposed algorithm was tested on nine well-known bi-objective and tri-objective benchmark functions to verify the compatibility of the work done. LMOGWO was then compared with simple multi-objective gray wolf optimization (MOGWO) and multi-objective particle swarm optimization (MOPSO). Two scenarios were considered for simulations to check the adaptivity of the proposed algorithm. The proposed LMOGWO outperformed MOGWO and MOPSO for University of Florida 1 (UF1), UF5, UF7 and UF8 for Scenario 1. However, MOGWO and MOPSO performed better than LMOGWO for UF2. For Scenario 2, LMOGWO outperformed the other two algorithms for UF5, UF8 and UF9. However, MOGWO performed well for UF2 and UF4. The results of MOPSO were also better than the proposed algorithm for UF4. Moreover, the PM utilization rate (%) was minimized by 30% with LMOGWO, 11% with MOGWO and 10% with MOPSO.

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“…Vakilinia, S. considered optimization problem in the cloud data centers in [26]. A workload is increased when a number of jobs arrive at the same time.…”

Section: Related Workmentioning

confidence: 99%

“…The authors of [16,[18][19][20] focused on efficient task scheduling. Efficient VM placement, VM consolidation, VM migration and optimization problem are solved in [22,[24][25][26]. The authors of [30,31] proposed GreA and ACO to solve VM allocation and VM placement problem.…”

Section: Related Workmentioning

confidence: 99%

An Enhanced Multi-Objective Gray Wolf Optimization for Virtual Machine Placement in Cloud Data Centers

et al. 2019

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“…Shahin [27] presented a system model to place the VMs for scheduling the jobs for power consumption to be optimized by using column generation technique. Of time heterogeneity of the workload which could be increased because of the continuous arrival of the tasks.…”

Section: B Dynamic Resource Allocation Schemes In Cloud Computingmentioning

confidence: 99%

Task Scheduling Allocation based on Task Completion Time in Cloud Environment

Sambangi*,

Gondi

2019

IJRTE

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Cloud computing is a prominent computing model wherein shared resources can be given as per the customer request at a time. The available resources in the cloud are gathered to execute several tasks that are submitted by the customer. While implementing the tasks, there is a need to optimize performance in terms of execution time, response time and resource utilization of the cloud. The optimization of the mentioned factors in the Cloud Computing can be achieved by one of the major areas known as Load balancing which refers to dealing with client requests from diverse application servers that are functioning in the cloud. An efficient Load Balancing algorithm enables the cloud to be more proficient and enhances customer contentment. So, this survey paper highlights the latest studies regarding the application of Load Balancing techniques for task allocation such as resource allocation (RA) strategies, cloud task scheduling centered on Load Balancing, dynamic Resource Allocation schemes, and cloud resource provisioning scheduling heuristics. Finally, Load Balancing performance for task allocation methods is compared based on task completion time.

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“…Vakilinia et al proposed integer quadratic program (IQP) with linear and quadratic constraints in [15]. The main objective of the proposed work is an optimization of power in data center.…”

Section: Related Workmentioning

confidence: 99%

Virtual Machine Placement via Bin Packing in Cloud Data Centers

et al. 2018

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With the increasing size of cloud data centers, the number of users and virtual machines (VMs) increases rapidly. The requests of users are entertained by VMs residing on physical servers. The dramatic growth of internet services results in unbalanced network resources. Resource management is an important factor for the performance of a cloud. Various techniques are used to manage the resources of a cloud efficiently. VM-consolidation is an intelligent and efficient strategy to balance the load of cloud data centers. VM-placement is an important subproblem of the VM-consolidation problem that needs to be resolved. The basic objective of VM-placement is to minimize the utilization rate of physical machines (PMs). VM-placement is used to save energy and cost. An enhanced levy-based particle swarm optimization algorithm with variable sized bin packing (PSOLBP) is proposed for solving the VM-placement problem. Moreover, the best-fit strategy is also used with the variable sized bin packing problem (VSBPP). Simulations are done to authenticate the adaptivity of the proposed algorithm. Three algorithms are implemented in Matlab. The given algorithm is compared with simple particle swarm optimization (PSO) and a hybrid of levy flight and particle swarm optimization (LFPSO). The proposed algorithm efficiently minimized the number of running PMs. VM-consolidation is an NP-hard problem, however, the proposed algorithm outperformed the other two algorithms.

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Energy efficient temporal load aware resource allocation in cloud computing datacenters

Cited by 41 publications

References 66 publications

An Enhanced Multi-Objective Gray Wolf Optimization for Virtual Machine Placement in Cloud Data Centers

An Enhanced Multi-Objective Gray Wolf Optimization for Virtual Machine Placement in Cloud Data Centers

Task Scheduling Allocation based on Task Completion Time in Cloud Environment

Virtual Machine Placement via Bin Packing in Cloud Data Centers

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