论文标题
基于VM显着性排名和云应用程序资源估算的高可用性管理模型
A High Availability Management Model based on VM Significance Ranking and Resource Estimation for Cloud Applications
论文作者
论文摘要
云资源使用情况的大规模上升效果避免了服务可用性，导致中断，资源争夺和过度消耗。现有方法通过提供多云，VM迁移并运行每个VM的多个复制品来解决这一挑战，这些复制品是云数据中心（CDC）的高费用。在这种情况下，提出了一种新颖的VM显着性排名和基于资源估计的高可用性管理（SRE-HM）模型，以增强CDC优化成本的用户的服务可用性。该模型估算了基于资源争议的服务器故障，并事先组织了需要资源来维持所需的服务可用性水平。引入和计算每个VM的显着性排名参数，执行关键或非关键任务，然后选择可允许的高可用性（HA）策略，该策略均针对其重要性和用户指定的约束。它通过仅针对重要VM而不是所有VM的失败耐受性策略来实现CDC的成本优化。通过使用Google群集数据集执行实验，对所提出的模型进行了评估并与最新模型进行了比较。 SRE-HM将服务的可用性提高了19.56％，并缩小了主动服务器的数量和功率消耗的数量，分别高达26.67％和19.1％，分别超过没有SRE-HM的HA。
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.
