The proliferation of various IoT devices has brought about diverse computing requests. Scheduling delay-sensitive tasks to edge nodes closer to data sources can help alleviate core network congestion and improve system quality of service (QoS). However, with the dynamic computing requirements of changing scenarios and the imbalanced performance of limited heterogeneous edge resources, resource competition among multiple tasks has become increasingly fierce. This resource competition leads to inefficient services and performance fluctuations in edge scheduling systems. The key lies in dynamically matching task requirements and limited heterogeneous resources to improve resource utilization efficiency. To overcome this challenge, we propose a resource-aware task grouping scheduling strategy (RATGS) based on our proposed group-based and sharedstate edge scheduling framework, aiming to improve the overall service quality of edge computing systems. We perform extensive evaluation on multiple metrics using realistic workloads and realworld traces. The experimental results demonstrate that RATGS improves the task completion rate by 7.56%∼50.1% before the deadline and improves the efficiency of resource utilization by 17.7%∼94.8% compared with existing baseline strategies. In addition, RATGS performed second best in terms of average completion time.

In recent years, more and more large-scale data processing and computing workflow applications run on heterogeneous clouds. Such cloud applications with precedence-constrained tasks are usually deadline-constrained and their scheduling is an essential problem faced by cloud providers. Moreover, minimizing the workflow execution cost based on cloud billing periods is also a complex and challenging problem for clouds. In realizing this, we first model the workflow applications as I/O Data-aware Directed Acyclic Graph (DDAG), according to clouds with global storage systems. Then, we mathematically state this deadline-constrained workflow scheduling problem with the goal of minimum execution financial cost. We also prove that the time complexity of this problem is NP-hard by deducing from a multidimensional multiple-choice knapsack problem. Third, we propose a heuristic cost-efficient task scheduling strategy called CETSS, which includes workflow DDAG model building, task subdeadline initialization, greedy workflow scheduling algorithm, and task adjusting method. The greedy workflow scheduling algorithm mainly consists of dynamical task renting billing period sharing method and unscheduled task subdeadline relax technique. We perform rigorous simulations on some synthetic randomly generated applications and real-world applications, such as Epigenomics, CyberShake, and LIGO. The experimental results clearly demonstrate that our proposed heuristic CETSS outperforms the existing algorithms and can effective save the total workflow execution cost. In particular, CETSS is very suitable for large workflow applications.