Stochastic resource scheduling via bilayer dynamic Markov decision process in mobile cloud networks

摘要

In mobile cloud networks, the efficiency and the quality of service are mainly affected by the service delay, the drop rate of service requests and CPU utilization. For these reasons, we propose a real-time stochastic resource scheduling framework (R-SRSF) to realize pre-allocation of resources by bilayer dynamic markov decision processes (DMDPs). In R-SRSF, we divide the resource scheduling into two parts, the virtual machine (VM) basic configuration resources management and VM switching management. To this end, we firstly design a strategy transformation management algorithm (STMA) to control the VM basic configuration resources. Secondly, we design a scheme-changed drift-plus-penalty algorithm (SDPPA) to execute VM switching management. We also prove the convergence of SDPPA based on Lyapunov optimization theory. For better VM scheduling, we propose a stochastic dynamic policy adjustment algorithm to cooperate with SDPPA. The experimental results show that R-SRSF can reduce the service delay, expenditure and the drop rate of service requests. Moreover, R-SFISF also has a high CPU utilization and stability.