Jun 2026· Journal of Advances in Developmental Research· 0 citations· 25 references
TL;DR
Experimental evaluation on a heterogeneous synthetic benchmark demonstrates that the proposed DDQN scheduler reduces SLA violations by approximately 85% relative to Round Robin and 72% relative to the greedy baseline, while achieving superior energy efficiency.
Abstract
In today’s world, with the emergence of IoT devices and critical latency applications, there has been an increased demand for intelligent resource management in diverse computing environments [5][10]. Collaborative computing is the integration of cloud and edge computing, while the issue of how to schedule each task for execution is still an open problem. Traditional heuristics like Round Robin and greedy latency reduction are incapable of adapting to the stochastic and non-stationary characteristics of practical workloads [4][14]. In this paper, an intelligent task scheduling mechanism based on Double Deep Q-Network (DDQN) reinforcement learning [2] has been proposed. The agent observes a four-dimensional state encoding task characteristics and selects binary offloading decisions, guided by a shaped reward signal encoding multiple performance objectives. Experimental evaluation on a heterogeneous synthetic benchmark demonstrates that the proposed DDQN scheduler reduces SLA violations by approximately 85% relative to Round Robin and 72% relative to the greedy baseline, while achieving superior energy efficiency. These results confirm that deep reinforcement learning [1][17][18] provides a principled foundation for adaptive resource management in next-generation edge-cloud systems.
An AI-enabled dynamic task scheduling framework based on Deep Reinforcement Learning (DRL) with a Deep Q-Network (DQN) model to dynamically assign tasks to virtual machines and learn the best scheduling policies by continuously interacting with the cloud environment based on system parameters such as resource availability, task queue length, and virtual machine load is introduced.
Karnam Sreenu, G. Prasadu, K. Premnadh et al.· VFAST Transactions on Softwa...· 0 citations
This paper proposes an innovative Deep Reinforcement Learning-based Intelligent Task Scheduling Framework (DRITS) designed to optimize task allocation and resource utilization in cloud distributed systems and establishes DRL-based intelligent scheduling as a promising solution for next-generation cloud computing infrastructure management.
Tileemat Ashour Aletiri· مجلة العلوم الشاملة· 0 citations
Battery life remains a major constraint on the continuous operation of mobile IoT devices represented by drones. Most existing computation offloading strategies rely on static heuristic rules, which fail to deliver stable performance in dynamically changing wireless mobile environments. Targeting this problem, this paper develops a multi-agent deep reinforcement learning approach for Spark job scheduling across local devices, edge servers and cloud resources. The scheduling task is modeled as a Markov decision process: six real-time system and network metrics, including CPU utilization, memory load, handover delay, signal strength, transmission latency and congestion level, form the state space, and the reward function is built directly on actual measured energy consumption data. Based on the multi-agent deep deterministic policy gradient (MADDPG) algorithm, the proposed method adopts a centralized training and decentralized execution paradigm to realize distributed collaborative decision-making. Validation on a public MEC dataset shows that the trained policy finally converges to a hybrid scheduling mode: 57% of tasks are processed locally, 39% are offloaded to edge nodes, and less than 4% are assigned to cloud resources for specific application scenarios. Comparative tests with PPO, FIFO, FAIR and HAS baselines confirm that multi-agent reinforcement learning can well capture the intrinsic scheduling patterns of complex mobile environments, providing an adaptive and energy-efficient scheduling solution for practical IoT deployments.
Haoyu Gu· Scientific Journal of Intell...· 0 citations
Edge computing has emerged as an effective paradigm for processing latency-sensitive applications by bringing computational resources closer to end users. However, efficient task scheduling remains a significant challenge due to limited computational capacity, dynamic workloads, and varying network conditions. This paper proposes an Energy-Efficient Deep Reinforcement Learning (EE-DRL) framework that optimizes task scheduling while minimizing energy consumption and execution delay. The proposed framework employs a Deep Q-Network (DQN) to dynamically allocate computational tasks among heterogeneous edge nodes. Experimental analysis demonstrates that the proposed approach reduces average task completion time by 23%, lowers energy consumption by 19%, and improves resource utilization compared with traditional scheduling algorithms. The proposed framework provides an intelligent, adaptive, and scalable solution for future edge computing infrastructures.
Sophia M. Carter, Rohan V. Iyer, Emilio J. Navarro· Journal of Computer Science· 0 citations
The rapid proliferation of Internet of Things (IoT) devices has placed unprecedented pressure on the network edge, where applications such as augmented reality, real-time analytics, and autonomous navigation demand low latency and tight energy budgets that traditional cloud-centric architectures cannot meet. Multi-access Edge Computing (MEC) addresses this gap by relocating computation closer to end users, but the core question of where and how each task should be executed remains open: rulebased and single-objective offloading strategies fail to simultaneously balance service latency, energy efficiency, and user experience under dynamic, large-scale conditions. In this paper we propose TARLOT (Two-Agent Reinforcement Learning Offloading Tasks), a cooperative framework for threetier IoT–MEC–Cloud environments. TARLOT decouples the offloading decision from the resourceallocation problem and assigns each to a dedicated Q-learning agent, so that the two subproblems are specialised independently while still being optimised jointly. The framework is evaluated on PureEdgeSim under heterogeneous IoT workloads, device densities ranging from 200 to 2,400, and diverse application profiles, and is compared against five widely-used baselines (Random, Round-Robin, Trade-Off, Pure-Edge, and Pure-Cloud). At 2,400 devices, TARLOT delivers an average service time of 1.1 s (against 4.3 s for Pure-Cloud), a Quality of Experience of 0.77 (against 0.22 for Pure-Cloud), a task-failure rate below 2 % (against nearly 14 % for Pure-Cloud), and a per-device energy consumption of only 3.6 W (against 11.2 W for Pure-Cloud) — roughly a 68 % reduction. Balanced CPU utilisation across the local, edge, and cloud tiers further confirms that TARLOT prevents resource bottlenecks, establishing it as a practical solution for next-generation large-scale IoT deployments.
Oussama Lagnfdi, Marouane Myyara, A. Darif· International journal of Com...· 0 citations
Sensitivity and ablation studies confirm stable learning and controllable latency-cost trade-offs, demonstrating that lightweight RL can effectively deliver cost-efficient, adaptive autoscaling in hybrid cloud environments.
Bekzat Kobei, N. Seilova, Zarina A. Kashaganova· AI@DTESI· 0 citations