An improved list-based task scheduling algorithm for fog computing environment

A high-performance execution of programs predominately depends on the efficient scheduling of tasks. An application consists of a sequence of tasks that can be represented as a directed acyclic graph (DAG). The tasks in the DAG have precedence constraints between them and each task has a different timeline on different processors. In this paper, a new list-based scheduling algorithm is proposed which schedules the tasks which are represented as a DAG structure. The main focus of this algorithm is to schedule the tasks to the suitable processing node in fog environment as the fog nodes have limited processing capacity. The assignment of tasks on the fog node should consider both the computation cost of the node and the execution finishing time of the node. The proposed algorithm has three phases. (1) the level sorting phase, where the independent tasks are identified (2) in the Task prioritization phase the proposed algorithm assigns priority to the task which has more successors so that more tasks in the next level can start their execution and (3) in the task selection phase a balanced combination of local optimal and global optimal approach is considered to assign a task to a suitable processor which further enhances the processor selection phase results in minimizing both the makespan and overall computation cost of the processors. Extensive experiments are carried out using randomly generated graphs and graphs from the real-world to analyze the performance of the proposed algorithm. The results show that the proposed algorithm outperforms all other well-known algorithms like predict earliest finish time, heterogeneous earliest finish time algorithm, minimal optimistic processing time, and SDBBATS in terms of performance matrices like average scheduling length ratio, speedup, and makespan.