Jul 2026· 2026 5th International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE)· pp. 1-7· 0 citations· 16 references
Abstract
Dynamic edge networks suffer from fluctuating bandwidth, latency, and edge-node load, which weaken conventional task offloading strategies under deadline and energy constraints. This study proposes an adaptive task offloading strategy based on dynamic network states. An online broad learning system predicts short-term bandwidth, transmission delay, and load from sliding-window observations. The predictions are embedded into a model predictive control framework for rolling optimization of the local-edge task allocation ratio, while confidence-interval-guided compensation adjusts decisions under prediction uncertainty. ADMM is used for distributed solution. NS-3/Python simulations show that the proposed method achieves a $93\%$ task success rate, 1.22 W average terminal energy consumption, 3.2 switches/s, and 45 ms average completion time, outperforming local execution, reactive offloading, and DQN-based offloading. The proposed strategy improves real-time reliability, energy efficiency, and robustness in highly dynamic edge computing environments.
A Mixed Integer Nonlinear Programming (MINLP) model with the objective of a weighted sum of long-term average task completion rate, total latency and energy consumption is established, which improves the task completion rate by 4% in high load scenarios and achieves a better balance between latency and energy consumption.
An adaptive Beta-policy and delayed-update multi-agent soft actor-critic method, abbreviated as ABDMASAC, which uses a Beta policy to model bounded actions and achieves a better overall trade-off than the selected MASAC-backbone and on-policy MARL baselines under the considered simulation settings.
Zheng Yao, Jie Liu, Changjun Deng et al.· Computers, Materials & C...· 0 citations
A new paradigm for satisfying the ever-growing demands of real-time Sixth Generation (6G) applications is Mobile Edge Computing (MEC). Additionally, base stations and Internet of Things devices that incorporate renewable energy harvesting capabilities have the potential to lower grid energy use. To maximize system potential and lower carbon emissions, it is crucial to make effective decisions about job offloading and resource allocation. A carbon-aware MEC architecture that uses both grid and renewable energy sources is proposed in this paper. Our goal is to jointly manage resource allocation and task offloading while monitoring carbon emissions and task queue delays to optimize system behavior under uncertainty, specifically for stochastic workloads and variable renewable generation. To balance these two cost components (emissions and queue length), we create a combined optimization problem. We develop a deep deterministic policy gradient (DDPG)-based joint optimization technique to address this issue in a constantly changing environment. In the optimization, we consider greedy policy (GP) and full offloading (FO), as well as time-average carbon emission (TACE) and time-average queue length (TAQL) as performance metrics, and time-average queue length (TAQL) and full execution (FE) as baseline strategies; we also evaluate normalized time-average cumulative reward (NTACR). This method uses continuous-action reinforcement learning to generate efficient, real-time control policies. For the proposed MEC network, numerical statistics show that our approach can lead to effective offloading and lower carbon emissions.
M. Saeed, Rashid A. Saeed, M. A. Ahmed et al.· 2026 6th International Confe...· 0 citations
A distributed Multi-stage Adaptive Deferred Acceptance (MA-DA) algorithm is proposed that enables a stable and Pareto-optimal assignment of tasks to edge computing nodes (ECNs) and determines a reasonable task execution sequence and ensures the prioritized completion of delay-sensitive tasks.
Integrated Terrestrial–Non-Terrestrial Networks (ITNTN), which combine terrestrial base stations (BSs), High-Altitude Platform Stations (HAPS), and Low-Earth Orbit (LEO) satellites, are key enablers of 6G communication and edge computing (EC) services. However, energy-limited BSs, particularly HAPS and satellites, pose significant sustainability challenges under continuous operation. To address this issue, we propose an on-demand EC server activation framework integrated with intelligent task offloading across ITNTN. A joint optimization problem is formulated to maximize task offloading success while satisfying energy and quality-of-service requirements. To solve it, we propose an online Q-learning policy that adaptively manages task offloading and EC server activation without prior knowledge of traffic dynamics. Simulation results show that the proposed method achieves superior task offloading success and energy efficiency compared to online heuristic and offline metaheuristic baselines. These findings highlight the importance of energyaware On-Off EC control for sustainable ITNTN systems.
Insaf Rzig, W. Jaafar, Safwan Alfattani· International Conference on...· 0 citations
Traditional resource scheduling strategies fail to fully utilize the computing and storage resources of edge nodes, leading to resource waste and overload of some nodes. This paper collects network topology and edge node resource information data for preprocessing. Then, a state space and action space are defined to record all possible system states and scheduling decisions in the edge computing environment. A two-layer deep Q-network model is constructed for action selection and Q-value calculation. Using 10-fold cross-validation and averaging, the optimized resource utilization reaches 85.18%, communication latency is $\mathbf{7 9. 1 m s}$, load balancing is improved to 0.841, and energy consumption and rejection rate are reduced to varying degrees, fully demonstrating the advantages of this algorithm in optimizing communication network resource scheduling in edge computing environments.