Extensive review of cloud resource management techniques in industry 4.0: Issue and challenges

Dewangan, Bhupesh Kumar; Agarwal, Amit; Choudhury, Tanupriya; Pasricha, Ashutosh; Satapathy, Suresh Chandra

doi:10.1002/spe.2810

Cited by 24 publications

(7 citation statements)

References 113 publications

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“…The experimental test results show that the average utilization of physical machines and energy consumption of the algorithm is significantly better than those of the comparison algorithms. Dewangan et al 23 established a mathematical model of resource scheduling in the cloud data center aiming at the minimum task completion time. On the technology of the conventional genetic algorithm, an adaptive transformation operator is designed to improve the crossover and mutation of the genetic algorithm.…”

Section: Related Workmentioning

confidence: 99%

An energy‐efficient method of resource allocation based on request prediction in multiple cloud data centers

Chen

Wen

Wang

2023

Concurrency and Computation

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To meet the ever-increasing requirements of the applications, cloud service providers have further built and managed multiple cloud data centers in multiple regions across geographies with many physical machines (PMs). However, most of the existing resource allocation algorithms are developed for a single cloud data center, which normally cannot efficiently handle the load burst occasions where a single cloud data center may not be enough to satisfy the demands burst of applications. Therefore, it is necessary to consider how to efficiently manage multiple cloud data centers while meeting application requirements and reducing energy consumption. This paper first systematically analyzes multiple cloud data centers and energy consumption models.Then, an energy-efficient method of Resource Allocation based on Request Prediction in multiple cloud data centers (RARP) is proposed. The RARP method constructs a resource allocation framework based on request prediction in multiple cloud data centers, which anticipates the application request volume in advance. At the same time, the RARP method allocates VMs and PMs based on the principle of minimum remaining resources available to achieve minimum usage of PMs, thus minimizing energy consumption to complete application requests. Extensive experiments are conducted on the proposed RARP method through the simulation platform CloudSim. Finally, the experimental test results show that the accuracy of request detection and the energy consumption of cloud data centers are significantly better than those of the comparison algorithms.

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Section: Related Workmentioning

confidence: 99%

An energy‐efficient method of resource allocation based on request prediction in multiple cloud data centers

Chen

Wen

Wang

2023

Concurrency and Computation

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“…Rajkumar et al [43] designed a SMICloud (Service Measurement Index), and Dewangan et al proposed WARMS [44], where Ishizaka et al presented analytic hierarchy process [45] framework using (A.H.P.) [46] to rank services. The ranking process occurs in the following manner: gathered requirements, assigning weightage to attributes, and ranking services.…”

Section: Related Workmentioning

confidence: 99%

Energy-Efficient Cloud Service Selection and Recommendation Based on QoS for Sustainable Smart Cities

et al. 2021

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The growing demand for cloud technology brings several cloud service providers and their diverse list of services in the market, putting a challenge for the user to select the best service from the inventory of available services. Therefore, a system that understands the user requirements and finds a suitable service according to user-customized requirements is a challenge. In this paper, we propose a new cloud service selection and recommendation system (CS-SR) for finding the optimal service by considering the user’s customized requirements. In addition, the service selection and recommendation system will consider both quantitative and qualitative quality of service (QoS) attributes in service selection. The comparison is made between proposed CS-SR with three existing approaches analytical hierarchy process (A.H.P.), efficient non-dominated sorting-sequential search (ENS-SS), and best-worst method (B.W.M.) shows that CR-SR outperforms the above approaches in two ways (i) reduce the total execution time and (ii) energy consumption to find the best service for the user. The proposed cloud service selection mechanism facilitates reduced energy consumption at cloud servers, thereby reducing the overall heat emission from a cloud data center.

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“…Cloud centers consist of physical hosts in thousands that consumes a significant amount of energy. Therefore, companies keep on building more and more data centers while cloud resources of current data centers are still not fully utilized [4]. As the cloud data centers are conventional practices, the cloud resources are becoming affordable and can be cheaper.…”

Section: Introductionmentioning

confidence: 99%

An Energy Efficient VM Selection Using Updated Dragonfly Algorithm in Cloud Computing

Prashar

Thakur

2023

Preprint

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Cloud computing is popular among industries, academia, and government to supply reliable and scalable computational power. High speed networks in cloud data centers connect Virtual machines with Physical Machines. Virtualization assists the cloud service providers to manage resources effectively but unoptimized and inefficient services degrade the performance of the system. The scheduling architecture of cloud computing includes Physical Machines (PMs), Virtual Machines (VMs) and the allocation and migration policy of the VMs over the PMs. The overutilized PMs get a few migrations and this paper introduces a novel behaviour of VM selection from overutilized PM using Swarm intelligence. The evaluation of the proposed algorithm architecture is compared with other state of art optimization algorithm from the same series. The evaluation has been done on the base of Quality of Service (QoS) parameters such as SLA-Violation, energy consumption against various load variation scenario to support elasticity. The proposed work has outcasted other techniques with significant margin in terms of QoS and the illustrations are discussed in the result.

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Extensive review of cloud resource management techniques in industry 4.0: Issue and challenges

Cited by 24 publications

References 113 publications

An energy‐efficient method of resource allocation based on request prediction in multiple cloud data centers

An energy‐efficient method of resource allocation based on request prediction in multiple cloud data centers

Energy-Efficient Cloud Service Selection and Recommendation Based on QoS for Sustainable Smart Cities

An Energy Efficient VM Selection Using Updated Dragonfly Algorithm in Cloud Computing

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