Title:

Task Offloading and Scheduling Based on Mobile Edge Computing and Software-defined Networking, Journal of Telecommunications and Information Technology, 2025, nr 1

Group publication title:

2025, nr 1, JTIT-artykuły

Creator:

Rawdhan, Fatimah Azeez

Subject and Keywords:

energy efficiency ; MEC ; PSO ; Q-learning ; scalability ; scheduling ; SDN

Description:

kwartalnik

Abstrakt:

When integrated with mobile edge computing (MEC), software-defined networking (SDN) allows for efficient network management and resource allocation in modern computing environments. The primary challenge addressed in this paper is the optimization of task offloading and scheduling in SDN-MEC environments. The goal is to minimize the total cost of the system, which is a function of task completion lead time and energy consumption, while adhering to task deadline constraints. This multi-objective optimization problem requires balancing the trade-offs between local execution on mobile devices and offloading tasks to edge servers, considering factors such as computation requirements, data size, network conditions, and server capacities. This research focuses on evaluating the performance of particle swarm optimization (PSO) and Q-learning algorithms under full and partial offloading scenarios. Simulation-based comparisons of PSO and Q-learning show that for large data quantities, PSO is more cost efficient than the other algorithms, with the cost increase equaling approximately 0.001% per kilobyte, as opposed to 0.002% in the case of Q-learning. As far as energy consumption is concerned, PSO performs 84% and 23% better than Q-learning in the case of full and partial offloading, respectively. The cost of PSO is also less sensitive to network latency conditions than GA. Furthermore, the results demonstrate that Q-learning offers better scalability in terms of execution time as the number of tasks increases, and exceeds the outcomes achieved by PSO for task loads of more than 40. Such observations prove that PSO is better suited for large data transfers and energy-critical applications, whereas Q-learning is better suited for highly scalable environments and large numbers of tasks.

Number:

1

Publisher:

National Institute of Telecommunications

Date:

2025, nr 1

Resource Type:

artykuł

DOI:

10.26636/jtit.2025.1.1941

eISSN:

on-line: ISSN 1899-8852

Source:

Journal of Telecommunications and Information Technology

Language:

ang

Rights Management:

Biblioteka Naukowa Instytutu Łączności

License:

CC BY 4.0