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.

程序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，这些任务可以表示为有向无环图（DAG）。DAG中的任务在它们之间具有优先级约束，并且每个任务在不同的处理器上具有不同的时间轴。本文提出了一种新的基于列表的调度算法，该算法可以调度以DAG结构表示的任务。该算法的主要焦点是将任务调度到雾环境中的合适处理节点，因为雾节点的处理能力有限。雾节点上的任务分配应同时考虑该节点的计算成本和该节点的执行完成时间。所提出的算法分为三个阶段。（1）级别排序阶段，在确定了独立任务的情况下（2）在任务优先级阶段，所提出的算法将优先级分配给具有更多后继任务的任务，以便下一级别的更多任务可以开始执行，并且（3）在任务选择阶段进行平衡的组合考虑将局部最优方法和全局最优方法的任务分配给合适的处理器，这进一步增强了处理器选择阶段的结果，从而使处理器的制造时间和总体计算成本最小化。使用随机生成的图和来自现实世界的图进行了广泛的实验，以分析所提出算法的性能。结果表明，所提出的算法优于所有其他知名算法，例如预测最早完成时间，异构最早完成时间算法，