Performance and Energy Aware Task Scheduling in Fog Computing

with the increase of the IoT devices, the data is exponentially increases which has to be analyzed and store in cloud data centres. Due to high data traffic and for minimizing the latency of the data a new paradigm was came in picture i.e. fog computing. Fog computing makes the cloud services available through networks and more accessible or closer to the users, fog computing also lowers the traffic and delays. Fog devices, in contrast to cloud computing, have relatively constrained power supply, processing capabilities, and communication resources, which makes it more difficult to design fog system that can fulfil real-time application needs. As the fog system is having limited power supply, thus energy saving becomes a primary aim. Energy-aware task scheduling is the one of the strategies to decrease the usage of energy in the fog system. Determining an efficient scheduling strategy that doesn’t compromise system performance and energy consumption while satisfying the client’s needs in terms of Quality of Service aspects is still difficult. Hence, in this article, we proposed a Particle Swarm optimization (PSO) based approach for energy aware and performance task scheduling in fog computing. Our proposed approach addresses the problem of mapping a collection of tasks with various computing needs to a number of fog nodes with dissimilar processing capabilities and energy consumption amounts. The proposed approach finds the optimal schedule that balances the trade-offs between energy consumption and makespan. The performance of the presented approach is evaluated through simulation and results demonstrate that significantly minimizes the makespan and the usage of the energy in the fog computing environment.