Achieving concurrency in cloud‐orchestrated Internet of Things for resource sharing through multiple concurrent access

Abstract

AbstractCloud‐orchestrated Internet of Things (IoT) facilitates proper utilization of network resources and placating user demands in smart communications. Multiple concurrent access (MCA) techniques designed for cloud‐assisted communication helps to achieve better resource sharing features with fault tolerance ability. A multi‐objective resource allocation and sharing (RAS) for balancing MCA in cloud‐orchestrated IoT is presented in this article. The RAS constraints are modeled through linear programming (LP) as an optimization approach. The constraints are resolved using genetic representations (GR) for reducing the unserviced requests and failed resource allocations. Conventional genetic stages are inherited by the LP model to solve resource allocation and access issues reducing latency. The combined LP and GR jointly resolve resource allocation and MCA stagnation in cloud network. A fair outcome of LP‐GR is estimation using the metrics response latency, resource utilization, request handled, and average latency.