🤖 AI Summary
This paper addresses the QoS-aware scheduling problem for periodic Directed Acyclic Graph (DAG) tasks under strict end-to-end latency constraints in heterogeneous, partially occupied Mobile Edge Computing (MEC) environments. We propose a hyperperiod-feasible scheduling method tailored for pre-emptive MEC systems. Our approach innovatively extends the HEFT algorithm to dynamically identify processor idle intervals, while jointly modeling task periodicity, precedence constraints, and resource limitations. We further introduce VM-level task binding and residual computing capacity awareness to achieve fine-grained task-resource matching without compromising the stability of existing workloads. Experimental results demonstrate that the proposed method significantly improves schedulability, load balancing, and resource utilization. It effectively supports high-density periodic DAG workloads while guaranteeing hard real-time QoS requirements.
📝 Abstract
In latency-sensitive applications, efficient task scheduling is crucial for maintaining Quality of Service (QoS) while meeting strict timing constraints. This paper addresses the challenge of scheduling periodic tasks structured as directed acyclic graphs (DAGs) within heterogeneous, pre-occupied Mobile Edge Computing (MEC) networks. We propose a modified version of the Heterogeneous Earliest Finish Time (HEFT) algorithm designed to exploit residual processing capacity in preoccupied MEC environments. Our approach dynamically identifies idle intervals on processors to create a feasible hyperperiodic schedule that specifies an allocated virtual machine (VM), task version, and start time for each task. This scheduling strategy maximizes the aggregate QoS by optimizing task execution without disrupting the existing periodic workload, while also adhering to periodicity, precedence, and resource constraints.Experimental results demonstrate that our method achieves enhanced load balancing and resource utilization, highlighting its potential to improve performance in heterogeneous MEC infrastructures supporting real-time, periodic applications.