Discrete Black Widow Optimization Algorithm for Multi-Objective IoT Application Placement in Fog Computing Environments

The "Internet of Things" describes a network comprising variedly distributed and heterogeneous devices that communicate by exchanging data to realize various applications with minimal human intervention.However, processing the massive amounts of data in the cloud environment becomes challenging.To address this issue, fog computing has appeared as a new paradigm that extends the capabilities of cloud computing to the edge of networks.The deployment of applications on diverse and dispersed nodes is one of the key issues in fog computing This article presents an approach to optimize application placement in fog computing infrastructure by formulating it as a combinatorial problem that aims to minimize both execution times and costs.Here, we propose a Discrete Black Widow Optimization (DBWO) algorithm specifically designed to tackle the discrete nature of the application placement in fog environments.Experimental results show that our approach demonstrates an average improvement of 9% compared to several recent approaches in the literature.In fog-only topology, DBWO demonstrated an improvement range from 4.30% to 9.87%, while in fog-cloud topology, it showed notable performance improvement, with fitness value enhancement ranging from 8% to 15.16%.This innovation represents a significant stride towards efficient and cost-effective application placement in fog computing environments.