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

By Lin Gu, Deze Zeng, Song Guo, Ahmed Barnawi, Yong Xiang. IEEE Transactions on Emerging Topics in Computing, 2017

332 citations recorded by the source corpus.

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.

Subfields: Computer Networks and Communications, Computer Vision and Pattern Recognition, Information Systems

View Paper: https://www.semanticscholar.org/paper/a580a30f9e9509cebbc34a637702b97f7b0a6565

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Citations in the corpus

On Multi-Access Edge Computing: A Survey of the Emerging 5G Network Edge Cloud Architecture and Orchestration (2017, unknown): “cal cyber-physical systems, investigating task distribution and VM placement in relation with the medical device and edgecloud association, is analyzed in [61], and that is based on”
A Comprehensive Survey on Fog Computing: State-of-the-Art and Research Challenges (2017, background): “[109] aim at optimizing resource utilization in the fog stratum for medical cyber-physical systems in order to minimize the communication cost as well as virtual machines deployment cost.”
Incentive Mechanism Design for Computation Offloading in Heterogeneous Fog Computing: A Contract-Based Approach (2018, background): “It deploys cloud computing resources closely to users and have advantages of reducing transmission latency [5][7], minimizing energy consumption [6]-[8], alleviating computation burden [6][10] and improve social welfare [9].”
Resource Management in Fog/Edge Computing (2018, methodology): “[59] proposed a fog computing architecture using VMs for a medical cyber-physical system (MCPS) [96].”
All One Needs to Know about Fog Computing and Related Edge Computing Paradigms: A Complete Survey (2018, methodology): “developed a linear programming-based two-phase heuristic algorithm to compute an optimal solution for service placement in fog computing medical cyber-physical systems [255].”
A Survey on the Edge Computing for the Internet of Things (2018, background): “in [73] proposed the concept of fog computing-supported medical cyber-physical systems to host virtual medical device applications.”
An Overview of Service Placement Problem in Fog and Edge Computing (2020, methodology): “[14, 26, 45, 50, 62] Metaheuristic [101] Minimizes the total energy consumption of mobile applications.”
On Reducing IoT Service Delay via Fog Offloading (2018, background): “[10] studied base station association, task distribution, and virtual machine placement in fog-computing-supported medical cyber-physical systems, while minimizing the overall cost and satisfying QoS requirements.”