Chaotic and Transfer Function-Assisted Grey Wolf Optimizer for IoT Task Scheduling in Cloud-of Things

The amalgamation of Cloud Computing and Internet-of-Things (IoT), i.e., Cloud-of-Things (CoT), has emerged as one of the indispensable technologies in the IT and business world. The success of CoT depends on the efficient services provided by cloud computing to the requests generated by IoT devices. The efficient management of IoT services on cloud virtualized resources, an NP-hard problem, is the major component in developing trust and reliability among IoT users. This research proposes a novel chaotic and transfer functionassisted modified grey wolf optimizer (MGWO) algorithm to schedule IoT requests on virtual machines in cloud computing. The proposed MGWO algorithm utilizes a transfer function to convert the wolf into the solution and a chaotic circle map to improve the initial population's diversity, which leads to faster convergence and better exploitation and exploration. The MGWO algorithm's performance is superior to the generic version of the GWO algorithm and its variants.