Distributed resource management in dew based edge to cloud computing ecosystem: A hybrid adaptive evolutionary approach

Abstract

SummaryTo extend the reach of cloud computing, the concept of edge computing and dew computing is introduced to execute various Internet of things (IoT) application with minimized delay in real time. The requested tasks are allocated computing resources that best suits their purpose. In this work, a novel hybrid hierarchical dew based edge to cloud architecture is developed. The objective of the study is to provide a detailed analysis and validation of real‐time scheduling of IoT application in this hybrid hierarchical ecosystem. The problem of optimally mapping requested tasks to the computing layers is mathematically formulated based on several quality of service factors and solved using the proposed hybrid adaptive metaheuristic algorithm. This is a combination of learning‐based adaptive particle swarm optimization and genetic algorithm. The exploitative and exploratory feature of the proposed algorithm helps in achieving better global optima compared with other existing metaheuristic algorithms.

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