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Nowadays, data caching is being used as a high-speed data storage layer inmobile edge computing networks employing flow control methodologies at anexponential rate. This study shows how to discover the best architecture forbackhaul networks with caching capability using a distributed offloadingtechnique. This article used a continuous power flow analysis to achieve theoptimum load constraints, wherein the power of macro base stations with variouscaching capacities is supplied by either an intelligent grid network orrenewable energy systems. This work proposes ubiquitous connectivity betweenusers at the cell edge and offloading the macro cells so as to provide featuresthe macro cell itself cannot cope with, such as extreme changes in the requireduser data rate and energy efficiency. The offloading framework is then reformedinto a neural weighted framework that considers convergence and Lyapunovinstability requirements of mobile-edge computing under Karush Kuhn Tuckeroptimization restrictions in order to get accurate solutions. The cell-layerperformance is analyzed in the boundary and in the center point of the cells.The analytical and simulation results show that the suggested methodoutperforms other energy-saving techniques. Also, compared to other solutionsstudied in the literature, the proposed approach shows a two to three timesincrease in both the throughput of the cell edge users and the aggregatethroughput per cluster.

An Optimized Multi-Layer Resource Management in Mobile Edge Computing Networks: A Joint Computation Offloading and Caching Solution