Summary In the modern Internet of Things (IoT) era, several applications generate a vast amount of data and that needs to be handled appropriately. The conventional cloud computing system delivers us with enormous resources to manage such voluminous data. Despite that, the growing demands of IoT applications on minimal energy consumption, minimal latency, the privacy of data, data processing based on location, and maximum Quality of Service(QoS) impels the advent of fog computing. As the devices in the fog layer are heterogeneous, distributed, and resource‐constrained, how the fog resources are effectively utilized for executing latency and time‐sensitive applications is a primary challenge. The data generated by the IoT devices are voluminous and produce overhead in network bandwidth during transmission and slow down the response time. This article addresses the task scheduling problem in fog computing to minimize the makespan and energy consumption. The proposed work consists of two phases. In the first phase, the tasks are ordered based on the heuristic method, heterogeneous earliest finish time (HEFT). Then the ordered tasks are scheduled by applying the improved gaining sharing knowledge (IGSK) based algorithm. To reduce the energy consumption dynamic voltage frequency scaling (DVFS) is applied in the proposed scheme. To evaluate the proposed scheduling method the simulations are performed on different workflows with varying sizes. The performance evaluation results exhibit that the proposed work outperforms the task scheduling with similar methods in terms of makespan and energy consumption.
The tasks are offloaded to fog computing instead of cloud computing to enhance the quality of service (QoS) of internet of things (IoT) applications. Though it provides improved QoS the accessibility of unremitting computation properties of fog servers is one among the limitations of IoT applications as transmitting a huge quantity of data may induce more energy consumption, cost, and increased makespan. Much research has been made to build up an efficient technique to address the multi-objective task scheduling in fog computing. One such method used to solve multiple objective problems in different domains is multi-criteria decision-making methods (MCDM). The MCDM Method named complex proportional assessment (COPRAS) has risen in popularity due to simple and fast computation. In this paper, a task scheduling algorithm (ECOPRAS) based on the integration of the entropy weight method and COPRAS method is proposed. The proposed technique consists of two phases that is, Task prioritization, and Task assignment. In the first phase, the task is ordered based on the heuristic method, B-level and in the second phase, the task will be assigned based on the ranking provided by the ECOPRAS method. The proposed method aims at minimizing energy consumption, price, and makespan and maximizing the reliability and utilization rate. The simulation results are compared with similar algorithms through various workflows and the proposed method outperforms well.
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