Optimized task scheduling and preemption for distributed resource management in fog-assisted IoT environment

The fog-assisted cloud computing gives better quality of service (QoS) to Internet of things (IoT) applications. However, the large quantity of data transmitted by the IoT devices results in the overhead of bandwidth and increased delay. Moreover, large amounts of data transmission generate resource management issues and decrease the system's throughput. This paper proposes the optimized task scheduling and preemption (OSCAR) model to overcome the limitations and improve the QoS. The dataset used for the study is a real-time crowd-based dataset which provides task information. The processes involved in this paper are as follows: (i) Initially, the tasks from the IoT devices are clustered based on the priority and deadline by implementing expectation-maximization (EM) clustering to decrease the computational complexity and bandwidth overhead. (ii) The clustered tasks are then scheduled by implementing a modified heap-based optimizer based on the QoS and service level agreement (SLA) constraints. (iii) Distributed resource management is performed by allocating resources to the tasks based on multiple constraints. The categorical deep Q network is the deep reinforcement learning model is implemented for this purpose. The dynamic nature of tasks from the IoT devices is addressed by performing preemption of tasks using the ranking method, where the tasks with higher priority, with a short deadline replaces less priority task by moving it into the waiting queue. The proposed model is experimented with in the iFogsim simulation tool and evaluated in terms of average response time, loss ratio, resource utilization, average makespan time, queuing waiting time, percentage of tasks satisfying the deadline and throughput. The proposed OSCAR model outperforms the existing model in achieving the QoS and SLA with maximal throughput and reduced response time.