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

机译：一种改进的基于清单的雾计算环境任务调度算法

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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.
机译：课程的高性能执行主要取决于任务的有效调度。应用程序由一系列任务组成，可以表示为定向的非循环图（DAG）。 DAG中的任务在它们之间具有优先约束，并且每个任务在不同的处理器上具有不同的时间轴。在本文中，提出了一种新的基于列表的调度算法，其调度表示为DAG结构的任务。此算法的主要焦点是将任务安排到雾环境中的合适处理节点，因为雾节点的处理能力有限。 FOG节点上的任务的分配应考虑节点的计算成本和节点的执行完成时间。所提出的算法有三个阶段。 （1）界面排序阶段，其中识别独立任务（2）在任务优先级阶段中，所提出的算法将优先级分配给具有更多继承者的任务，以便在下一级别中的更多任务可以启动他们的执行和（3）在任务选择阶段，认为局部最佳和全局最佳方法的平衡组合被认为将任务分配给合适的处理器，该处理器进一步增强处理器选择阶段导致最小化处理器的Mapspan和整体计算成本。广泛的实验是使用来自现实世界的随机生成的图形和图表进行了分析所提出的算法的性能。结果表明，所提出的算法优于所有其他众所周知的算法，如预测最早的结束时间，异构最早的结束时间算法，最小乐观处理时间和SDBBAT，如平均调度长度比，加速和MEPESPHAN等性能矩阵。

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