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On the Interplay Between Network Metrics and Performance of Mobile Edge Offloading

Parisa Fard Moshiri, Murat Simsek, Burak Kantarci

TL;DR

This paper tackles the problem of offloading tasks in 5G-enabled MEC by explicitly incorporating the dropped task ratio alongside latency. It proposes two optimization approaches, MILP and Genetic Algorithms, to minimize a weighted sum of delay and task drops in a realistic NS-3-based 5G-MEC setup, comparing them against FCFS and STF. The results indicate that MILP achieves lower delays and dropped task ratios than GA (approximately 20 percent reduction in drops and about 2 ms in delay) and delivers faster runtimes, while STF helps reduce drops at the potential cost of longer tasks waiting. The work offers a more reliable edge offloading framework suitable for real-time applications such as YOLO5-based distracted driver recognition, and suggests avenues for scaling with additional MEC servers and scheduling strategies to contend with rising demand.

Abstract

Multi-Access Edge Computing (MEC) emerged as a viable computing allocation method that facilitates offloading tasks to edge servers for efficient processing. The integration of MEC with 5G, referred to as 5G-MEC, provides real-time processing and data-driven decision-making in close proximity to the user. The 5G-MEC has gained significant recognition in task offloading as an essential tool for applications that require low delay. Nevertheless, few studies consider the dropped task ratio metric. Disregarding this metric might possibly undermine system efficiency. In this paper, the dropped task ratio and delay has been minimized in a realistic 5G-MEC task offloading scenario implemented in NS3. We utilize Mixed Integer Linear Programming (MILP) and Genetic Algorithm (GA) to optimize delay and dropped task ratio. We examined the effect of the number of tasks and users on the dropped task ratio and delay. Compared to two traditional offloading schemes, First Come First Serve (FCFS) and Shortest Task First (STF), our proposed method effectively works in 5G-MEC task offloading scenario. For MILP, the dropped task ratio and delay has been minimized by 20% and 2ms compared to GA.

On the Interplay Between Network Metrics and Performance of Mobile Edge Offloading

TL;DR

This paper tackles the problem of offloading tasks in 5G-enabled MEC by explicitly incorporating the dropped task ratio alongside latency. It proposes two optimization approaches, MILP and Genetic Algorithms, to minimize a weighted sum of delay and task drops in a realistic NS-3-based 5G-MEC setup, comparing them against FCFS and STF. The results indicate that MILP achieves lower delays and dropped task ratios than GA (approximately 20 percent reduction in drops and about 2 ms in delay) and delivers faster runtimes, while STF helps reduce drops at the potential cost of longer tasks waiting. The work offers a more reliable edge offloading framework suitable for real-time applications such as YOLO5-based distracted driver recognition, and suggests avenues for scaling with additional MEC servers and scheduling strategies to contend with rising demand.

Abstract

Multi-Access Edge Computing (MEC) emerged as a viable computing allocation method that facilitates offloading tasks to edge servers for efficient processing. The integration of MEC with 5G, referred to as 5G-MEC, provides real-time processing and data-driven decision-making in close proximity to the user. The 5G-MEC has gained significant recognition in task offloading as an essential tool for applications that require low delay. Nevertheless, few studies consider the dropped task ratio metric. Disregarding this metric might possibly undermine system efficiency. In this paper, the dropped task ratio and delay has been minimized in a realistic 5G-MEC task offloading scenario implemented in NS3. We utilize Mixed Integer Linear Programming (MILP) and Genetic Algorithm (GA) to optimize delay and dropped task ratio. We examined the effect of the number of tasks and users on the dropped task ratio and delay. Compared to two traditional offloading schemes, First Come First Serve (FCFS) and Shortest Task First (STF), our proposed method effectively works in 5G-MEC task offloading scenario. For MILP, the dropped task ratio and delay has been minimized by 20% and 2ms compared to GA.
Paper Structure (7 sections, 9 equations, 3 figures, 4 tables)

This paper contains 7 sections, 9 equations, 3 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. System Model and Offloading Schemes
  4. Greedy Schemes
  5. The optimization problem
  6. Performance Analysis
  7. Conclusion

Figures (3)

  • Figure 1: Proposed methodology
  • Figure 2: Mean of delay for different number of tasks and confidence intervals
  • Figure 3: Mean of dropped task ratio for different number of tasks and confidence intervals