Virtual Machine Live Migration Strategy in Big Data Information System

Abstract

In recent years, as an emerging technology, cloud computing has provided us with convenient services, and power consumption on issues have become increasingly prominent. Virtual machine live migration technology has become an important technology to reduce the power consumption of cloud computing centers. In the process of virtual machine migration, the performance of the virtual machine is inevitably degraded, which may violate service level agreement (SLA, Service Level Agreement). How to use virtual machine live migration technology to reduce power consumption as much as possible while ensuring a low SLA violation rate becomes a hot issue. This paper aims to optimize the light load detection and virtual machine redistribution in the virtual machine live migration model. Aiming at the problem that the existing virtual machine light load detection method is easy to cause “over-migration”, this paper proposes a threshold-based minimum CPU utilization method for light load detection, which effectively avoids excessive virtual machine migration. Aiming at the problem that the current process of virtual machine re allocation algorithm is relatively simple, and there is a certain power loss space, we present power aware simulation annealing algorithm (PASA). The algorithm combines the simulated annealing algorithm based on the power aware best fit decreasing algorithm (PABFD), which largely avoids the disadvantage that the PABFD easily falls into the local optimal solution trap. The paper uses the CloudSim simulator as simulation platform. The results show that compared with the best algorithm combination proposed by the previous researchers, the power consumption of the new algorithm combination proposed in the paper is reduced by 16.79%, and the SLA violation rate is reduced by 85.37%. Combining the two algorithms together can lead to better energy efficiency, performance and quality of service than using the two algorithms.