Energy-Efficient Composition of Configurable Operators in Big Data Environment

With the rapid development of edge computing, heterogeneous smart devices, serving as operators, require to be deployed closely to data sources, and cooperate to accomplish big data applications considering factors including energy consumption and fast response. Specifically, an operators composition can be encapsulated and represented as feasible solution, and an operator in a certain solution prefers to be encoded with abstract matrix defined by evaluation indicators including spatial and temporal constraint, priority between consecutive operators, the load balancing factor, and energy consumption. Discovering an optimal composition strategy in the constructed edge cluster can boil down to a multi-objective and multi-constrained optimization problem, which can be solved through adopting heuristic algorithms. Experimental evaluation demonstrates that the Grey Wolf Optimization outperforms Particle Swarm Optimization in discovering an approximately optimal operators composition in the corresponding edge cluster, and can minimize the energy consumption of the network. (C) 2019 The Authors. Published by Elsevier B.V.