Service Characteristics-Oriented Joint Optimization of Radio and Computing Resource Allocation in Mobile Edge Computing

Mobile Edge Computing (MEC) is a promising technology, which allows reducing latency and energy consumption, thereby making the user experience better. Although MEC can support various types of services, differentiated quality of service (QoS) requirements bring difficulties and challenges to the allocation of radio resources and computing resources of the MEC system. In this paper, we jointly optimize subchannel allocation, as well as the local central processing unit (CPU) speed scaling, user association, subcarrier assignment, power allocation, and video quality decision for MEC systems to study the total cost saving problem. Considering the traffic variations, we develop an online algorithm by using the Lyapunov optimization technique to solve this problem, referred to as dynamic subchannel allocation and resource allocation (DSARA). Particularly, the proposed DSARA algorithm only needs to track the state of the current network without requiring any prior knowledge. Besides, we prove that our proposed algorithm can asymptotically achieve the minimum total cost value (such as minimizing the power consumption and maximizing quality satisfaction). Simulation results show that the DSARA can achieve a good tradeoff between the total cost and delay, and outperforms the existing schemes in terms of the total cost expenditure.