Efficient resource scaling based on load fluctuation in edge-cloud computing environment

With the rapid development of information technology, edge computing has grown rapidly by pushing large amounts of computing to the edge of the network. However, due to the rapid growth of edge access devices and limited edge storage space, the edge cloud faces many challenges in addressing the workloads. In this paper, a cost-optimized resource scaling strategy is proposed based on load fluctuation. Firstly, the load prediction model is built based on DBN with supervised learning to predict the workloads of edge cloud. Then, a cost-optimized resource scaling strategy is presented, which comprehensively considers reservation planning and on-demand planning. In the reservation phase, the long-term resource reservation problem is planned as a two-stage stochastic programming problem, which is transformed into a deterministic integer programming problem. In the on-demand phase, the on-demand resource scaling problem planning is solved as an integer programming problem. Finally, extensive experiments are conducted to evaluate the performance of the proposed cost-optimized resource scaling strategy based on load fluctuation.