Energy Efficient Resource Allocation in Cloud Environment Using Metaheuristic Algorithm
Utility-based computing popularly known as “cloud computing” offers several computing services to the users. Due to the proliferation in the users of cloud computing, there is an unprecedented increase in the demand for computation resources to execute cloud services. Thus, the...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10309856/ |
| _version_ | 1827386004905918464 |
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| author | Saurabh Singhal Nakul Gupta Parveen Berwal Quadri Noorulhasan Naveed Ayodele Lasisi Anteneh Wogasso Wodajo |
| author_facet | Saurabh Singhal Nakul Gupta Parveen Berwal Quadri Noorulhasan Naveed Ayodele Lasisi Anteneh Wogasso Wodajo |
| author_sort | Saurabh Singhal |
| collection | DOAJ |
| description | Utility-based computing popularly known as “cloud computing” offers several computing services to the users. Due to the proliferation in the users of cloud computing, there is an unprecedented increase in the demand for computation resources to execute cloud services. Thus, there is a requirement to investigate currently available resources like virtual machines, CPU, RAM, and storage to allocate cloud services. The allocation and QoS of cloud services are highly dependent on allocation schemes. The optimized solutions allocate resources to submitted jobs to reduce the overall cost to the end-users/service provider without degrading the performance of virtual machines. The allocation techniques also consider the harvesting of energy consumption required for running the cloud services. In this paper, we have utilized a Rock Hyrax-based optimization technique to allocate resources to the submitted jobs with reduced energy consumption. The proposed Rock Hyrax algorithm has been simulated on the CloudSim simulator for various scenarios. The performance of the proposed algorithm has been measured over various Quality of Service (QoS) parameters such as makespan, energy efficiency, response time, throughput, and cost. The gathered results validate the proposed algorithm that improves the QoS parameters by 3%-8% as compared to algorithms when both jobs and resources are considered to be dynamic in nature. |
| first_indexed | 2024-03-08T15:35:48Z |
| format | Article |
| id | doaj.art-9804e8cc8be54fc1a86b20f63d51b526 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-08T15:35:48Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-9804e8cc8be54fc1a86b20f63d51b5262024-01-10T00:03:13ZengIEEEIEEE Access2169-35362023-01-011112613512614610.1109/ACCESS.2023.333043410309856Energy Efficient Resource Allocation in Cloud Environment Using Metaheuristic AlgorithmSaurabh Singhal0https://orcid.org/0000-0002-3117-8085Nakul Gupta1Parveen Berwal2Quadri Noorulhasan Naveed3https://orcid.org/0000-0003-2757-4138Ayodele Lasisi4https://orcid.org/0000-0001-6975-9785Anteneh Wogasso Wodajo5https://orcid.org/0000-0002-1773-2588Department of Computer Engineering and Applications, GLA University, Mathura, Uttar Pradesh, IndiaDepartment of Civil Engineering, GLA University, Mathura, Uttar Pradesh, IndiaDepartment of Civil Engineering, Galgotias College of Engineering and Technology, Greater Noida, Uttar Pradesh, IndiaDepartment of Computer Science, College of Computer Science, King Khalid University, Abha, Saudi ArabiaDepartment of Computer Science, College of Computer Science, King Khalid University, Abha, Saudi ArabiaDepartment of Automotive Engineering, College of Engineering and Technology, Dilla University, Dilla, EthiopiaUtility-based computing popularly known as “cloud computing” offers several computing services to the users. Due to the proliferation in the users of cloud computing, there is an unprecedented increase in the demand for computation resources to execute cloud services. Thus, there is a requirement to investigate currently available resources like virtual machines, CPU, RAM, and storage to allocate cloud services. The allocation and QoS of cloud services are highly dependent on allocation schemes. The optimized solutions allocate resources to submitted jobs to reduce the overall cost to the end-users/service provider without degrading the performance of virtual machines. The allocation techniques also consider the harvesting of energy consumption required for running the cloud services. In this paper, we have utilized a Rock Hyrax-based optimization technique to allocate resources to the submitted jobs with reduced energy consumption. The proposed Rock Hyrax algorithm has been simulated on the CloudSim simulator for various scenarios. The performance of the proposed algorithm has been measured over various Quality of Service (QoS) parameters such as makespan, energy efficiency, response time, throughput, and cost. The gathered results validate the proposed algorithm that improves the QoS parameters by 3%-8% as compared to algorithms when both jobs and resources are considered to be dynamic in nature.https://ieeexplore.ieee.org/document/10309856/Cloud computingrock hyrax optimizationresource allocationcostenergy efficiency |
| spellingShingle | Saurabh Singhal Nakul Gupta Parveen Berwal Quadri Noorulhasan Naveed Ayodele Lasisi Anteneh Wogasso Wodajo Energy Efficient Resource Allocation in Cloud Environment Using Metaheuristic Algorithm IEEE Access Cloud computing rock hyrax optimization resource allocation cost energy efficiency |
| title | Energy Efficient Resource Allocation in Cloud Environment Using Metaheuristic Algorithm |
| title_full | Energy Efficient Resource Allocation in Cloud Environment Using Metaheuristic Algorithm |
| title_fullStr | Energy Efficient Resource Allocation in Cloud Environment Using Metaheuristic Algorithm |
| title_full_unstemmed | Energy Efficient Resource Allocation in Cloud Environment Using Metaheuristic Algorithm |
| title_short | Energy Efficient Resource Allocation in Cloud Environment Using Metaheuristic Algorithm |
| title_sort | energy efficient resource allocation in cloud environment using metaheuristic algorithm |
| topic | Cloud computing rock hyrax optimization resource allocation cost energy efficiency |
| url | https://ieeexplore.ieee.org/document/10309856/ |
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