{"title":"使用强化学习方法在虚拟机整合中构建以能源和性能为中心的资源分配框架","authors":"Madala Guru Brahmam, Vijay Anand R","doi":"10.2174/0126662558289911240206071447","DOIUrl":null,"url":null,"abstract":"\n\nVirtual machines are used to reduce cloud platform application performance, management\ncosts, and access irregularities. Virtual machines are frequently vulnerable to delays,\noverburdening workloads, and other obstacles while consolidating and migrating servers. To\nsignificantly disperse loads among virtual machines, dynamic consolidation techniques are implemented\nto control energy dissipation, monitor overloading, and address underloading problems.\n\n\n\nThe process of consolidation involves more calculations and resources in order\nto transfer services between virtual machines, provided that Service Level Agreements are observed.\n\n\n\nThe suggested approach promotes the use of cutting-edge architecture to combine\nvirtual machines, and, therefore, strike a balance between performance and energy requirements.\nThe main design considerations for the suggested Dynamic Weightage algorithm,\nwhich includes the clustering approach in relation to reinforcement learning approaches, are\noverall resource needs and Performance to Power Ratio (PPR). A cluster of ideal virtual machines\nis created, and resources are distributed according to performance and energy requirements.\nVirtual machine resource requests are converted into a matching relationship factor,\nwhich represents the individual hosts while taking PPR into account. The overall workload associated\nwith virtual machine consolidation is also provided by these estimations. It is noted\nthat there is little energy trade-off and that performance is maintained at a nominal level across\nthe cluster. The architecture is put into practice throughout offline platforms, which are dispersed\necosystems that allow for increased system performance and scaling.\n\n\n\nThe CloudSim simulator is used to validate the system using datasets that are obtained\nfrom PlanetLab. According to the data, energy saving has produced yields of up to 47% and\npromising quality of service attributes.\n\n\n\nThe validation of the system is performed using the CloudSim simulator with datasets\nfrom PlanetLab. The results indicate significant energy conservation, up to 47%, along\nwith promising quality of service parameters. The proposed architecture is compared with other\nstate-of-the-art algorithms for distributed architectures and heterogeneous environments,\nshowcasing its efficiency. The conclusion emphasizes the prioritization of VM consolidation\nand energy efficiency in the proposed architecture, which has been tested on a Proliant G7-\nbased data center using a variety of hosts. Notably, the CloudSim Toolkit is highlighted as outperforming\nOpenStack-based techniques in simulation results.\n","PeriodicalId":36514,"journal":{"name":"Recent Advances in Computer Science and Communications","volume":"12 6","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Energy and Performance Centric Resource Allocation Framework in\\nVirtual Machine Consolidation Using Reinforcement Learning Approach\",\"authors\":\"Madala Guru Brahmam, Vijay Anand R\",\"doi\":\"10.2174/0126662558289911240206071447\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n\\nVirtual machines are used to reduce cloud platform application performance, management\\ncosts, and access irregularities. Virtual machines are frequently vulnerable to delays,\\noverburdening workloads, and other obstacles while consolidating and migrating servers. To\\nsignificantly disperse loads among virtual machines, dynamic consolidation techniques are implemented\\nto control energy dissipation, monitor overloading, and address underloading problems.\\n\\n\\n\\nThe process of consolidation involves more calculations and resources in order\\nto transfer services between virtual machines, provided that Service Level Agreements are observed.\\n\\n\\n\\nThe suggested approach promotes the use of cutting-edge architecture to combine\\nvirtual machines, and, therefore, strike a balance between performance and energy requirements.\\nThe main design considerations for the suggested Dynamic Weightage algorithm,\\nwhich includes the clustering approach in relation to reinforcement learning approaches, are\\noverall resource needs and Performance to Power Ratio (PPR). A cluster of ideal virtual machines\\nis created, and resources are distributed according to performance and energy requirements.\\nVirtual machine resource requests are converted into a matching relationship factor,\\nwhich represents the individual hosts while taking PPR into account. The overall workload associated\\nwith virtual machine consolidation is also provided by these estimations. It is noted\\nthat there is little energy trade-off and that performance is maintained at a nominal level across\\nthe cluster. The architecture is put into practice throughout offline platforms, which are dispersed\\necosystems that allow for increased system performance and scaling.\\n\\n\\n\\nThe CloudSim simulator is used to validate the system using datasets that are obtained\\nfrom PlanetLab. According to the data, energy saving has produced yields of up to 47% and\\npromising quality of service attributes.\\n\\n\\n\\nThe validation of the system is performed using the CloudSim simulator with datasets\\nfrom PlanetLab. The results indicate significant energy conservation, up to 47%, along\\nwith promising quality of service parameters. The proposed architecture is compared with other\\nstate-of-the-art algorithms for distributed architectures and heterogeneous environments,\\nshowcasing its efficiency. The conclusion emphasizes the prioritization of VM consolidation\\nand energy efficiency in the proposed architecture, which has been tested on a Proliant G7-\\nbased data center using a variety of hosts. Notably, the CloudSim Toolkit is highlighted as outperforming\\nOpenStack-based techniques in simulation results.\\n\",\"PeriodicalId\":36514,\"journal\":{\"name\":\"Recent Advances in Computer Science and Communications\",\"volume\":\"12 6\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-02-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Recent Advances in Computer Science and Communications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2174/0126662558289911240206071447\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"Computer Science\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Recent Advances in Computer Science and Communications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2174/0126662558289911240206071447","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Computer Science","Score":null,"Total":0}
Energy and Performance Centric Resource Allocation Framework in
Virtual Machine Consolidation Using Reinforcement Learning Approach
Virtual machines are used to reduce cloud platform application performance, management
costs, and access irregularities. Virtual machines are frequently vulnerable to delays,
overburdening workloads, and other obstacles while consolidating and migrating servers. To
significantly disperse loads among virtual machines, dynamic consolidation techniques are implemented
to control energy dissipation, monitor overloading, and address underloading problems.
The process of consolidation involves more calculations and resources in order
to transfer services between virtual machines, provided that Service Level Agreements are observed.
The suggested approach promotes the use of cutting-edge architecture to combine
virtual machines, and, therefore, strike a balance between performance and energy requirements.
The main design considerations for the suggested Dynamic Weightage algorithm,
which includes the clustering approach in relation to reinforcement learning approaches, are
overall resource needs and Performance to Power Ratio (PPR). A cluster of ideal virtual machines
is created, and resources are distributed according to performance and energy requirements.
Virtual machine resource requests are converted into a matching relationship factor,
which represents the individual hosts while taking PPR into account. The overall workload associated
with virtual machine consolidation is also provided by these estimations. It is noted
that there is little energy trade-off and that performance is maintained at a nominal level across
the cluster. The architecture is put into practice throughout offline platforms, which are dispersed
ecosystems that allow for increased system performance and scaling.
The CloudSim simulator is used to validate the system using datasets that are obtained
from PlanetLab. According to the data, energy saving has produced yields of up to 47% and
promising quality of service attributes.
The validation of the system is performed using the CloudSim simulator with datasets
from PlanetLab. The results indicate significant energy conservation, up to 47%, along
with promising quality of service parameters. The proposed architecture is compared with other
state-of-the-art algorithms for distributed architectures and heterogeneous environments,
showcasing its efficiency. The conclusion emphasizes the prioritization of VM consolidation
and energy efficiency in the proposed architecture, which has been tested on a Proliant G7-
based data center using a variety of hosts. Notably, the CloudSim Toolkit is highlighted as outperforming
OpenStack-based techniques in simulation results.