Cost Efficient Resource Management in Fog Computing Supported Medical Cyber-Physical System

TLDR: Fog computation and MCPS are integrated to build fog computing supported MCPS (FC-MCPS), and an LP-based two-phase heuristic algorithm is proposed that produces near optimal solution and significantly outperforms a greedy algorithm.

Abstract: With the recent development in information and communication technology, more and more smart devices penetrate into people’s daily life to promote the life quality. As a growing healthcare trend, medical cyber-physical systems (MCPSs) enable seamless and intelligent interaction between the computational elements and the medical devices. To support MCPSs, cloud resources are usually explored to process the sensing data from medical devices. However, the high quality-of-service of MCPS challenges the unstable and long-delay links between cloud data center and medical devices. To combat this issue, mobile edge cloud computing, or fog computing, which pushes the computation resources onto the network edge (e.g., cellular base stations), emerges as a promising solution. We are thus motivated to integrate fog computation and MCPS to build fog computing supported MCPS (FC-MCPS). In particular, we jointly investigate base station association, task distribution, and virtual machine placement toward cost-efficient FC-MCPS. We first formulate the problem into a mixed-integer non-linear linear program and then linearize it into a mixed integer linear programming (LP). To address the computation complexity, we further propose an LP-based two-phase heuristic algorithm. Extensive experiment results validate the high-cost efficiency of our algorithm by the fact that it produces near optimal solution and significantly outperforms a greedy algorithm.