International Journal of Computer Science and Technology

International Journal of Computer Science and Technology | Vol. 2, No. 4, April 2021 | pp. 25–32

Research Article

Title: Dynamic Task Offloading and Resource Scheduling in Mobile Edge Computing Using Deep Deterministic Policy Gradient

Names of Authors: Santiago Gomez¹, Valeria Rodriguez², and Mateo Lopez³

Authors’ Affiliations:
¹Department of Systems Engineering, National University of Colombia, Bogotá, Colombia
²Faculty of Engineering, University of the Andes, Bogotá, Colombia
³Department of Computer Science, University of Buenos Aires, Buenos Aires, Argentina

Abstract: Mobile Edge Computing (MEC) reduces computational strain on mobile devices by offloading resource-intensive tasks to local edge nodes. However, highly dynamic environments—characterized by unpredictable user mobility, varying task arrival rates, and fluctuating wireless channel qualities—complicate offloading decisions. Static or heuristic scheduling rules cannot adapt to continuous, multi-dimensional system states, resulting in excessive task drop rates and battery depletion. This paper proposes an autonomous, continuous-action task offloading and resource scheduling framework utilizing the Deep Deterministic Policy Gradient (DDPG) algorithm. The MEC ecosystem maps into a continuous state-action space where the actor network determines fractional offloading ratios and local CPU scaling frequencies, while the critic network evaluates the resulting system reward. The target multi-objective reward function minimizes both processing latency and device energy consumption under strict task completion deadline constraints. The model incorporates continuous changes in transmission bandwidth measured in megabits per second (Mbps) and local CPU workloads measured in cycles per second. Simulation experiments demonstrate that the proposed DDPG-based controller converges efficiently, reducing average task execution delay by 34.8% and device battery consumption by 29.1% relative to conventional genetic and Q-learning approaches. The framework ensures reliable execution for interactive edge applications under non-stationary traffic conditions.

Keywords: Mobile edge computing, Task offloading, Deep reinforcement learning, Continuous action space, Resource scheduling, Energy efficiency

Manuscript Timeline: Received: January 15, 2021; Revised: February 22, 2021; Accepted: March 19, 2021; Published: April 1, 2021

Citation: Gomez, S., Rodriguez, V., & Lopez, M. (2021). Dynamic task offloading and resource scheduling in mobile edge computing using deep deterministic policy gradient. International Journal of Computer Science and Technology, 2(4), 25–32. DOI: 10.46882/2021/IJCST/000016

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