Massive upsurge in cloud resource usage stave off service availability resulting into outages, resource contention, and excessive power-consumption. The existing approaches have addressed this challenge by providing multi-cloud, VM migration, and running multiple replicas of each VM which accounts for high expenses of cloud data centre (CDC). In this context, a novel VM Significance Ranking and Resource Estimation based High Availability Management (SRE-HM) Model is proposed to enhance service availability for users with optimized cost for CDC. The model estimates resource contention based server failure and organises needed resources beforehand for maintaining desired level of service availability. A significance ranking parameter is introduced and computed for each VM, executing critical or non-critical tasks followed by the selection of an admissible High Availability (HA) strategy respective to its significance and user specified constraints. It enables cost optimization for CDC by rendering failure tolerance strategies for significant VMs only instead of all the VMs. The proposed model is evaluated and compared against state-of-the-arts by executing experiments using Google Cluster dataset. SRE-HM improved the services availability up to 19.56% and scales down the number of active servers and power-consumption up to 26.67% and 19.1%, respectively over HA without SRE-HM.
翻译:云层资源使用量大增,避免因断电、资源争议和过度电力消耗而出现大量使用云层资源的情况,使云层数据中心(CDC)费用高昂,云层资源使用量大增,从而避免因断流、资源争议和过度电力消耗而出现服务供应量。 现有办法通过提供多球、VM迁移和运行每个VM的多重复制件来应对这一挑战。 在这方面,提议了一个新的VM 重要评级和资源估算模型(SRE-HM),以提高CDC成本优化成本的用户的服务供应量。 模型估计基于资源争议的服务器故障,并预先安排维持所需服务水平所需的资源。 为每个VM引入和计算一个重要等级参数,随后执行关键或非关键的任务,并选择适合其重要性和用户具体限制的可接受的高可用性战略(HA),使CDC能够实现成本优化,即只为大型VMS(SR-HM)制定容忍失败战略,而不是所有VMS。 拟议的模型通过使用谷群集数据集进行实验,评估并比国家艺术。 SRE将服务的提供量提高到19.56%和19-HAM服务器的规模分别降至191%和比例。