Enhancing environmental sustainability investigating resource allocation strategies and QOS in heterogeneous cloud and fog computing environments from an ecological perspective

Abstract

This thesis explores innovative approaches and algorithms aimed at newlineoptimizing resource allocation and maximizing Quality of Service (QoS) newlineparameters in heterogeneous cloud and fog computing environments. The newlineresearch investigates the challenges and opportunities presented by these newlinecomputing paradigms and emphasizes the significance of efficient resource newlineallocation for achieving optimal system performance and user satisfaction. newlineA hybrid optimization approach combines Sailfish Optimization newline(SO) and Ant Lion Optimizer (ALO) algorithms proposed to enhance newlineresource allocation efficiency in cloud infrastructures. By addressing the newlineunique challenges posed by dispersed computing architectures and variable newlineresource availability, this chapter emphasizes the importance of tailored newlinesolutions for optimizing resource allocation in dynamic environments. newlineThe proposed work focus shifts to a deeper exploration of resource newlineallocation optimization through the proposal of heuristic algorithms such as newlinethe Cloudlet-VM Assignment Optimization Algorithm (CVAOA) and the newlineEvolutionary VM Placement Algorithm (EVMPA). These algorithms aim to newlineoptimize task allocation and virtual machine (VM) placement, thereby newlinemaximizing resource utilization and system performance in both cloud and newlinefog computing environments. newline