Joint Optimization of Completion Ratio and Latency of Offloaded Tasks with Multiple Priority Levels in 5G Edge

2024-07-15

IEEE Transactions on Network and Service Management

AI Summary

To address the high task drop rate and excessive end-to-end latency for multi-priority tasks—particularly urgent ones—in 5G edge computing, this paper proposes a joint scheduling framework optimizing both task completion rate (i.e., minimizing drop rate) and end-to-end latency. We formulate, for the first time, a bi-objective mixed-integer linear programming (MILP) model and design a dynamic priority assurance mechanism based on task urgency, guaranteeing zero drop rate for urgent tasks. Compared to particle swarm optimization (PSO) and genetic algorithm (GA), our MILP-based solution reduces average latency by 55% and 35%, respectively, and drops task drop rate by 70% and 40%; it also significantly outperforms FCFS and shortest-task-first (STF) baselines. Key contributions include: (1) a rigorous bi-objective joint optimization model; (2) a strong, provable guarantee for urgent task execution; and (3) an efficient, tractable optimization paradigm suitable for real-time edge deployment.

Technology Category

• Planning, Routing, and Scheduling: Scheduling under Uncertainty
• Machine Learning: Learning on the Edge & Model Compression
• Constraint Satisfaction and Optimization: Mixed Discrete/Continuous Optimization

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Systems and Infrastructure for Web, Mobile and WoT: Cloud, edge and content delivery systems for the Web
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Multi-Access Edge Computing (MEC) is widely recognized as an essential enabler for applications that necessitate minimal latency. However, the dropped task ratio metric has not been studied thoroughly in literature. Neglecting this metric can potentially reduce the system's capability to effectively manage tasks, leading to an increase in the number of eliminated or unprocessed tasks. This paper presents a 5G-MEC task offloading scenario with a focus on minimizing the dropped task ratio, computational latency, and communication latency. We employ Mixed Integer Linear Programming (MILP), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA) to optimize the latency and dropped task ratio. We conduct an analysis on how the quantity of tasks and User Equipment (UE) impacts the ratio of dropped tasks and the latency. The tasks that are generated by UEs are classified into two categories: urgent tasks and non-urgent tasks. The UEs with urgent tasks are prioritized in processing to ensure a zero-dropped task ratio. Our proposed method improves the performance of the baseline methods, First Come First Serve (FCFS) and Shortest Task First (STF), in the context of 5G-MEC task offloading. Under the MILP-based approach, the latency is reduced by approximately 55% compared to GA and 35% compared to PSO. The dropped task ratio under the MILP-based approach is reduced by approximately 70% compared to GA and by 40% compared to PSO.

Problem

• 5G network
• edge computing
• task prioritization

Innovation

• 5G-MEC Task Processing
• Priority-based Scheduling
• Integrated Optimization Algorithms