Adaptive Inertia Weight Based Particle Swarm Optimization for Resource Scheduling in Medical Cloud System

摘要

It is a hot issue to build the cloud computing based regional medical system. In a medical cloud system, the doctor often requires the medical data of patients in different medical institutions, and this kind of request can be done by using the FEP (Front-End Processor) in each medical institution to implement the corresponding allocated query task. For a medical institution, how to implement the allocated task efficiently is then a challenge as the cost and efficiency of each internal FEP is different and need to be taken into consideration. An adaptive inertia weight based Particle Swarm Optimization (PSO) is proposed in this paper to solve the aforementioned resource scheduling problem. By tuning the inertia weight of velocity updating for particle adaptively, the searching process for optimal solution is accelerated, and a reasonable resource scheduling is achieved when the cost and efficiency of the query task are both taken as the fitness function. The efficiency of query for medical data is improved, and the experiments validate the effectiveness of the proposed algorithm.