{"title":"绿色云环境下资源优化的阈值比较与负载均衡算法","authors":"Nagamani H. Shahapure, P. Rekha, N. Poornima","doi":"10.47059/revistageintec.v11i4.2473","DOIUrl":null,"url":null,"abstract":"The reduction in the consumption of energy by the cloud data centers is called as green cloud. Green cloud also helps in restraining the waste disposed to the environment. With the increasing demand in cloud computing, there has been an increase in the energy consumption. Green cloud can be achieved by server consolidation and proper load balancing techniques using virtual machine (VM) migration. It is a feature provided by virtualization. The virtual machines are transferred from one host to another. The overhead associated with migration is performance degradation. This can be overcome by proper load balancing techniques. This helps to curtail the number of VM migrations as well as the energy consumption. In this paper, a technique called as threshold compare and load balance algorithm (TCLBA) is proposed for optimization of the resources at the cloud provider. Two types of threshold are defined for load balancing, namely the lower and upper threshold. This algorithm works on the principle of shifting the load from a server if the load is above the upper threshold or shifting to the server if its load is below the lower threshold. The load is balanced by migrating the VMs. The workload is consolidated to smaller number of hosts such that the remaining hosts are shut down. This method solves the purpose of effective utilization of available resources with lesser energy consumption.","PeriodicalId":428303,"journal":{"name":"Revista Gestão Inovação e Tecnologias","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Threshold Compare and Load Balancing Algorithm to for Resource Optimization in a Green Cloud\",\"authors\":\"Nagamani H. Shahapure, P. Rekha, N. Poornima\",\"doi\":\"10.47059/revistageintec.v11i4.2473\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The reduction in the consumption of energy by the cloud data centers is called as green cloud. Green cloud also helps in restraining the waste disposed to the environment. With the increasing demand in cloud computing, there has been an increase in the energy consumption. Green cloud can be achieved by server consolidation and proper load balancing techniques using virtual machine (VM) migration. It is a feature provided by virtualization. The virtual machines are transferred from one host to another. The overhead associated with migration is performance degradation. This can be overcome by proper load balancing techniques. This helps to curtail the number of VM migrations as well as the energy consumption. In this paper, a technique called as threshold compare and load balance algorithm (TCLBA) is proposed for optimization of the resources at the cloud provider. Two types of threshold are defined for load balancing, namely the lower and upper threshold. This algorithm works on the principle of shifting the load from a server if the load is above the upper threshold or shifting to the server if its load is below the lower threshold. The load is balanced by migrating the VMs. The workload is consolidated to smaller number of hosts such that the remaining hosts are shut down. This method solves the purpose of effective utilization of available resources with lesser energy consumption.\",\"PeriodicalId\":428303,\"journal\":{\"name\":\"Revista Gestão Inovação e Tecnologias\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-08-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Revista Gestão Inovação e Tecnologias\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.47059/revistageintec.v11i4.2473\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Revista Gestão Inovação e Tecnologias","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.47059/revistageintec.v11i4.2473","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Threshold Compare and Load Balancing Algorithm to for Resource Optimization in a Green Cloud
The reduction in the consumption of energy by the cloud data centers is called as green cloud. Green cloud also helps in restraining the waste disposed to the environment. With the increasing demand in cloud computing, there has been an increase in the energy consumption. Green cloud can be achieved by server consolidation and proper load balancing techniques using virtual machine (VM) migration. It is a feature provided by virtualization. The virtual machines are transferred from one host to another. The overhead associated with migration is performance degradation. This can be overcome by proper load balancing techniques. This helps to curtail the number of VM migrations as well as the energy consumption. In this paper, a technique called as threshold compare and load balance algorithm (TCLBA) is proposed for optimization of the resources at the cloud provider. Two types of threshold are defined for load balancing, namely the lower and upper threshold. This algorithm works on the principle of shifting the load from a server if the load is above the upper threshold or shifting to the server if its load is below the lower threshold. The load is balanced by migrating the VMs. The workload is consolidated to smaller number of hosts such that the remaining hosts are shut down. This method solves the purpose of effective utilization of available resources with lesser energy consumption.