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.

中文翻译：

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云边缘融合计算中的网络感知任务迁移

随着云计算的发展，已经提出了边缘计算以向用户提供实时且低延迟的服务。当前的研究通常将云计算和边缘计算集成为云边缘融合计算，以提供更多个性化服务。然而，云计算和边缘计算都遭受高网络消耗，这仍然是在云边缘融合计算环境中尚未解决的关键问题。网络消耗的成本可以分为两部分：迁移成本和通信成本。为了解决高网络消耗问题，可以通过虚拟化技术将某些虚拟机从过载的物理机迁移到其他物理机。当前的网络感知迁移策略通过优化通信拓扑来更多地关注通信成本。考虑到通信成本和迁移成本，本文从虚拟机的通信相关性和迁移过程的网络流量方面解决了网络消耗高的问题。它提出了三种启发式虚拟机迁移算法：LM，mCaM和mCaM2，以平衡通信成本和迁移成本。通过实验将这些算法的性能与现有虚拟机迁移算法的性能进行了比较。实验结果表明，我们的虚拟机迁移算法明显优化了通信成本和迁移成本。与现有算法AppAware相比，这三种算法的网络成本平均降低了20％。这意味着这三种算法可改善云边缘融合计算环境中的网络性能并减少网络消耗。在操作时间方面，它们也平均优于现有算法70％。