A Heuristic Deep Q Learning for Offloading in Edge Devices in 5 g Networks

Dong, Yixin; Alwakeel, Ahmed M.; Alwakeel, Mohammed M.; Alharbi, Lubna A.; Althubiti, Sara A.

doi:10.1007/s10723-023-09667-w

Cited by 4 publications

(3 citation statements)

References 48 publications

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“…Next, [16] work's goal is to reduce the overall latency of the MEC system while ensuring that each task is completed within its deadline and resource constraints. It handles both partial and complete offloading tasks and uses energy consumption as a factor in the offloading decision.…”

Section: Ml-based Solutionsmentioning

confidence: 99%

Deep Reinforcement Learning techniques for dynamic task offloading in the 5G edge-cloud continuum

Nieto,

de la Iglesia,

Lopez-Novoa

et al. 2024

J Cloud Comp

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The integration of new Internet of Things (IoT) applications and services heavily relies on task offloading to external devices due to the constrained computing and battery resources of IoT devices. Up to now, Cloud Computing (CC) paradigm has been a good approach for tasks where latency is not critical, but it is not useful when latency matters, so Multi-access Edge Computing (MEC) can be of use. In this work, we propose a distributed Deep Reinforcement Learning (DRL) tool to optimize the binary task offloading decision, this is, the independent decision of where to execute each computing task, depending on many factors. The optimization goal in this work is to maximize the Quality-of-Experience (QoE) when performing tasks, which is defined as a metric related to the battery level of the UE, but subject to satisfying tasks’ latency requirements. This distributed DRL approach, specifically an Actor-Critic (AC) algorithm running on each User Equipment (UE), is evaluated through the simulation of two distinct scenarios and outperforms other analyzed baselines in terms of QoE values and/or energy consumption in dynamic environments, also demonstrating that decisions need to be adapted to the environment’s evolution.

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Section: Ml-based Solutionsmentioning

confidence: 99%

Deep Reinforcement Learning techniques for dynamic task offloading in the 5G edge-cloud continuum

Nieto,

de la Iglesia,

Lopez-Novoa

et al. 2024

J Cloud Comp

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show abstract

“…[39] has proposed a DRL-aware Blockchain-based task scheduling system for healthcare applications through a solution that provides makespan efficient scheduling to solve the issues of task scheduling, security, and the cost related to processing tasks in the IIoT healthcare paradigms. We have also been inspired by the work carried out in [40], where they have introduced a multi-agent collaborative DRL-based scheduling algorithm with DQN in Mobile Edge Computing (MEC) to solve the issues of low latency, offloading, and task scheduling in MEC using Karush-Kuhn-Tucker (KKT) to minimize the total latency and DQN to reduce the energy consumption.…”

Section: Literature Reviewmentioning

confidence: 99%

A Proposal for a Tokenized Intelligent System: A Prediction for an AI-Based Scheduling, Secured Using Blockchain

Younis,

Jambi,

Eassa

et al. 2024

Systems

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Intelligent systems are being proposed every day as advances in cloud systems are increasing. Mostly, the services offered by these cloud systems are dependent only on their providers, without the inclusion of services from other providers, specialized third parties, or individuals. This ‘vendor lock-in’ issue and the limitations related to offering tailored services could be resolved by allowing multiple providers or individuals to collaborate through intelligent task scheduling. To address such real-world systems’ limitations in provisioning and executing heterogeneous services, we employed Blockchain and Deep Reinforcement Learning here; the first is used for the token-based secured communication between parties, and the latter is to predict the appropriate task scheduling; hence, we guarantee the quality of not only the immediate decision but also the long-term. The empirical results show a high reward achieved, meaning that it accurately selected the candidates and adaptably assigned the tasks based on job nature and executors’ individual computing capabilities, with 95 s less than the baseline in job completion time to maintain the Quality of Service. The successful collaboration between parties in this tokenized system while securing transactions through Blockchain and predicting the right scheduling of tasks makes it a promising intelligent system for advanced use cases.

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“…The double DQN-based 5G network job scheduling algorithm provides higher convergence between wireless nodes in the 5G network and consumes less energy. And compared with the current standard deep learning methods DeMADRL and BiDRL, this algorithm has the best effect [19]. Kopacz et al modeled cooperative-competitive social group dynamics with multi-agent environments and used three methods to solve the multi-agent optimization problem.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Routing Policies for Multi-Skill Call Centers Using Deep Q Network

Zhang

2023

Mathematics

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When the call center queuing system becomes complex, it turns out that the static routing policy is not optimal. This paper considers the problem of the dynamic routing policy for call centers with multiple skill types and agent groups. A state-dependent routing policy based on the Deep Q Network (DQN) is proposed, and a reinforcement learning algorithm is applied to optimize the routing. A simulation algorithm is designed to help customers and agents interact with the external environment to learn the optimal strategy. The performance evaluation considered in this paper is the service level/abandon rate. Experiments show that the DQN-based dynamic routing policy performs better than the common static policy Global First Come First Serve (FCFS) and the dynamic policy Priorities with Idle Agent Thresholds and Weight-Based Routing in various examples. On the other hand, the training time of the routing policy model based on the DQN is much faster than routing optimization based on simulation and a genetic algorithm.

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A Heuristic Deep Q Learning for Offloading in Edge Devices in 5 g Networks

Cited by 4 publications

References 48 publications

Deep Reinforcement Learning techniques for dynamic task offloading in the 5G edge-cloud continuum

Deep Reinforcement Learning techniques for dynamic task offloading in the 5G edge-cloud continuum

A Proposal for a Tokenized Intelligent System: A Prediction for an AI-Based Scheduling, Secured Using Blockchain

Dynamic Routing Policies for Multi-Skill Call Centers Using Deep Q Network

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