An offloading and pricing mechanism based on virtualization in edge-cloud computing

Abstract

Integrate users, edge servers and cloud into a system can improve resource utilization and quality of service (QoS). The users can send the same request to multiple Edge Servers. However, edge servers have limited scope, which may result in repeated offloading tasks for the same user. In this paper, we consider the cloud resource migration in the form of virtual machines (VMs) to achieve high computing efficiency and low latency requirements. Obviously, the migration of VMs causes changes in the number and price of edge resources, which further affects the task offloading strategy. Therefore, it is necessary to optimize the VMs migration strategy and resource pricing strategy successively to achieve the optimal offloading strategy suitable for users. First, based on the competition between edge servers, a static VMs migration algorithm (SVMMA) is designed to formulate the strategy and price of VMs migration. Then, according to the users’ resource needs and the resource possession of different edge servers, a resource partitioning approach (RPA) have been proposed. Finally, two different QoS functions are evaluated, representing the QoS matching degree between tasks and multiple resource combination blocks (MRCBs). Wherein, a dynamic matching and pricing of tasks algorithm (DMPT) is conducted to obtain the optimal offloading strategy and price of the task. Experiments show that our algorithms can improve the offloading rate of tasks and achieve the relative balance among user, cloud server, and edge server benefits.