Cost-aware automatic scaling and workload-aware replica management for edge-cloud environment

As the mobile edge computing develops continually, many emerging and diversified edge applications, such as Internet of vehicles, virtual reality games and lightweight deep learning tasks, are emerged. These applications are latency sensitive and require a large number of network connections. As the data size and business requests processed per unit of time continue to increase, the pressure of edge cloud load increases so much that the edge cloud cannot provide timely and effective services for users. In order to meet the high requirement of delay sensitive application service, on the one hand, the energy consumption of edge devices should be reduced, and the modules with high computational load should be scheduled to the remote server for execution. On the other hand, the automatic scaling model is proposed to improve the total cost of the tenanted instances. In the dynamic replica management model, the response time and the energy consumption are reduced, moreover, the workload in the hosts are balanced. In the experiment of automatic scaling, the cumulative total cost and the power consumption of our proposed algorithm are lower than that of CAAS and MLC algorithm. Two hours after the experiment, the CPU utility of our proposed algorithm is higher than the CPU utilizations of CAAS and MLC. In the dynamic replica placement experiment, the average response time and load balancing value of the data migration algorithm in our paper are lower than that of BA and LA. The available storage space of our replica placement method is more balanced than both the DDD and WA.