WECOA: coati-walrus optimisation-based resource allocation in fog-cloud environment

Managing resource allocation in a fog-cloud environment is complex due to the dynamic nature of user requirements and the limited resources available in fog nodes. This work proposes a novel approach to optimisation-based resource allocation in fog-cloud (ORAFC) environments, utilising advanced optimisation techniques to improve system performance and efficiency. A core element of the model is a load prediction mechanism powered by a dual spatial pyramid pooling assisted SqueezeNet (DSPPAS) architecture, which determines whether tasks should be processed locally or offloaded to the cloud, considering factors like distance, delay, and performance metrics. To optimise resource allocation, this work employs the walrus enhanced with coati optimisation algorithm (WECOA), balancing key factors such as makespan, migration cost, energy consumption, and execution time. The proposed approach is validated through extensive simulations and comparisons with existing methods.