Network perception task migration in cloud-edge fusion computing

Abstract

With the development of cloud computing, edge computing has been proposed to provide real-time and low-delay services to users. Current research usually integrates cloud computing and edge computing as cloud-edge fusion computing for more personalized services. However, both cloud computing and edge computing suffer from high network consumption, which remains a key problem yet to be solved in cloud-edge fusion computing environments. The cost of network consumption can be divided into two parts: migration costs and communication costs. To solve the high network consumption problem, some virtual machines can be migrated from overloaded physical machines to others with the help of virtualization technology. Current network perception migration strategies focus more on the communication cost by optimizing the communication topology. Considering both communication and migration costs, this paper addresses the high network consumption problem in terms of the communication correlations of virtual machines and the network traffic of the migration process. It proposes three heuristic virtual machine migration algorithms, LM, mCaM and mCaM2, to balance communication costs and migration costs. The performance of these algorithms is compared with those of existing virtual machine migration algorithms through experiments. The experimental results show that our virtual machine migration algorithms clearly optimize the communication cost and migration cost. These three algorithms have a lower network cost than AppAware, an existing algorithm, by 20% on average. This means that these three algorithms improve the network performance and reduce the network consumption in cloud-edge fusion computing environments. They also outperform existing algorithms in terms of operation time by 70% on average.