Distributed Server Allocation for Internet-of-Things Monitoring Services With Preventive Start-Time Optimization Against Server Failure

Abstract

Internet-of-Things (IoT) services require high performance regarding low delay and fault tolerance. Distributed server allocation is well-suited for meeting these requirements in IoT monitoring services. Previous work focused on reducing delay but overlooked the need for fault tolerance in distributed server allocation. This paper proposes a distributed server allocation model based on preventive start-time optimization (PSO) for IoT monitoring services against server failure. The proposed model preventively determines the server allocation to minimize the largest maximum delay between IoT devices and application servers and between database and application servers among all failure patterns. We formulate the proposed model as an integer linear programming (ILP) problem. We introduce a server allocation algorithm based on PSO to accelerate the computation to obtain an optimal server allocation, compared to the ILP approach. We prove that the introduced algorithm obtains a PSO-based optimal allocation in polynomial time. Numerical results show that the introduced algorithm outputs an optimal server allocation faster than the ILP approach. We compare the PSO-based server allocation with allocations based on the start-time and run-time optimization. We observe that the PSO-based allocation reduces the largest maximum delay by 5.5% for a network model with eleven servers compared to the start-time optimization and avoids unnecessary network disconnections while increasing the maximum delay by 5.1% compared to the run-time optimization