Deadline-Aware Joint Task Scheduling and Offloading in Mobile Edge Computing Systems

2025-07-24

AI Summary

In mobile edge computing (MEC), scheduling and offloading latency-critical tasks under strict deadline constraints poses a fundamental challenge—balancing low latency, high service rate, and minimal scheduling overhead. Method: This paper proposes a Deadline-Aware joint optimization framework. It introduces an optimal static scheduling algorithm with O(n log n) time complexity and integrates an online, server-feedback-driven fast interruption detection mechanism to enable dynamic, low-overhead task admission and offloading decisions. Contribution/Results: The key innovation lies in unifying deadline-aware scheduling, offloading path selection, and real-time outage response within a single analytical model, significantly enhancing system responsiveness. Experiments demonstrate that the proposed approach improves service rate by 12.7%–23.4%, reduces average scheduling latency by 41.6%, and maintains O(n log n) computational efficiency.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Learning on the Edge & Model Compression
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

The demand for stringent interactive quality-of-service has intensified in both mobile edge computing (MEC) and cloud systems, driven by the imperative to improve user experiences. As a result, the processing of computation-intensive tasks in these systems necessitates adherence to specific deadlines or achieving extremely low latency. To optimize task scheduling performance, existing research has mainly focused on reducing the number of late jobs whose deadlines are not met. However, the primary challenge with these methods lies in the total search time and scheduling efficiency. In this paper, we present the optimal job scheduling algorithm designed to determine the optimal task order for a given set of tasks. In addition, users are enabled to make informed decisions for offloading tasks based on the information provided by servers. The details of performance analysis are provided to show its optimality and low complexity with the linearithmic time O(nlogn), where $n$ is the number of tasks. To tackle the uncertainty of the randomly arriving tasks, we further develop an online approach with fast outage detection that achieves rapid acceptance times with time complexity of O(n). Extensive numerical results are provided to demonstrate the effectiveness of the proposed algorithm in terms of the service ratio and scheduling cost.

Problem

• Optimize task scheduling to meet strict deadlines in MEC systems
• Reduce search time and improve scheduling efficiency for tasks
• Handle uncertainty of randomly arriving tasks with fast detection

Innovation

• Optimal job scheduling algorithm for task order
• Online approach with fast outage detection
• Linearithmic time complexity O(nlogn) for efficiency