EMCS: An Energy-Efficient Makespan Cost-Aware Scheduling Algorithm Using Evolutionary Learning Approach for Cloud-Fog-Based IoT Applications

The tremendous expansion of the Internet Things (IoTs) has generated an enormous volume near and remote sensing data, which is increasing with emergence new solutions for sustainable environments. Cloud computing typically used to help resource-constrained IoT devices. However, cloud servers are placed deep within core network, a long way from IoT, introducing immense data transactions. These transactions require heavy electricity consumption release harmful CO2 environment. A distributed environment located at edge network named fog been promoted reduce limitation applications. Fog potentially processes real-time delay-sensitive it reduces traffic, minimizes energy consumption. additional can be reduced by implementing energy-aware task scheduling, decides on execution tasks or nodes basis minimum completion time, cost, In this paper, algorithm called energy-efficient makespan cost-aware scheduling (EMCS) proposed using evolutionary strategy optimize performance work evaluated extensive simulations. Results show that EMCS 67.1% better than cost makespan-aware (CMaS), 58.79% Heterogeneous Earliest Finish Time (HEFT), 54.68% Bees Life Algorithm (BLA) 47.81% Evolutionary Task Scheduling (ETS) in terms makespan. Comparing model, uses 62.4% less CMaS, 26.41% BLA, 6.7% ETS. When comparing consumption, consumes 11.55% 4.75% BLA 3.19% also increase number nodes, balance between gives makespan,