Task Offloading and Resource Allocation for Industrial Internet of Things: A Double-Dueling Deep Q-Network Approach

Cheng, Weijun; Liu, Xiaoshi; Wang, Xiaoting; Nie, Gaofeng

doi:10.1109/access.2022.3210248

Cited by 12 publications

(6 citation statements)

References 29 publications

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“…More deep RL approaches are proposed for resource allocation in edge applications dealing with industrial IoT and internet of medical things [16], [17]. Other related resource allocation problems in computationally constrained scenarios such as joint server selection, task offloading and handover in multi-access edge computing wireless networks have been tackled through DQNs as in [18], [19], [20], [21].…”

Section: Related Workmentioning

confidence: 99%

“…A fairly common solution technique is based on the use of deep-Q networks (DQN). Several resource allocation problems in computationally constrained environments [12], [13], [14], [15] and other related issues like joint server selection, task offloading, and handover [16], [17], [18], [19], [20], [21], [22], [23] in multi-access edge computing wireless networks have been tackled through DQNs. However, extending such approaches to a multi-service scenario falls into serious scalability issues.…”

Section: Introductionmentioning

confidence: 99%

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Fair and Scalable Orchestration of Network and Compute Resources for Virtual Edge Services

Tripathi

Puligheddu

Pramanik

et al. 2024

IEEE Trans. on Mobile Comput.

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The combination of service virtualization and edge computing allows for low latency services, while keeping data storage and processing local. However, given the limited resource availability at the edge, a conflict in resource usage arises when both virtualized user applications and network functions need to be supported. Further, the concurrent resource request by user applications and network functions is often entangled, since the data generated by the former has to be transferred by the latter, and vice versa. In this paper, we first show through experimental tests the correlation between a video-based application and a vRAN. Then, owing to the complex involved dynamics, we develop a scalable reinforcement learning-based framework for resource orchestration at the edge, which leverages a Pareto analysis for provable fair and efficient decisions. We validate our framework, named VERA, through a real-time proof-of-concept implementation, which we also use to obtain datasets reporting real-world operational conditions and performance. Using such experimental datasets, we numerically demonstrate that VERA meets the KPI targets for over 96% of the observation period and performs similarly when executed in our realtime implementation, with KPI differences below 12.4%. Further, its scaling cost is 54% lower than a centralized framework based on deep-Q networks.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fair and Scalable Orchestration of Network and Compute Resources for Virtual Edge Services

Tripathi

Puligheddu

Pramanik

et al. 2024

IEEE Trans. on Mobile Comput.

View full text Add to dashboard Cite

show abstract

“…There has been growing interest in applying DDQN algorithms for task offloading optimization and related fields, such as [39][40][41][42] . To solve the multi-objective optimization problem identified in the IoV architecture, MEC environment, and the blockchain network, we introduce a DDQN-based [38] algorithm.…”

Section: B Proposed Ddqn Algorithmmentioning

confidence: 99%

Multi-Objective Secure Task Offloading Strategy for Blockchain-Enabled IoV-MEC Systems: A Double Deep Q-Network Approach

Moghaddasi,

Rajabi,

Gharehchopogh

2024

IEEE Access

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The Internet of Vehicles (IoV) represents a paradigm shift in vehicular communication, aiming to enhance traffic efficiency, safety, and the driving experience by leveraging interconnected vehicles. Despite its promise, the IoV faces challenges such as efficient task offloading, energy management, and data security. Mobile Edge Computing (MEC) emerges as a solution to some of these challenges by bringing computational resources closer to the vehicular network's edge, yet it raises critical concerns regarding resource management, service continuity, and scalability in dynamic vehicular environments. Addressing both IoV and MEC challenges necessitates robust and dynamic optimization mechanisms. In response to these challenges, our study introduces a multi-objective approach using Double Deep Q-Networks (DDQN), a cutting-edge application of Deep Reinforcement Learning (DRL). This algorithm combines the strengths of Deep Neural Networks (DNNs) and Deep Learning (DL) techniques, enabling dynamic decision-making that can adapt to changing conditions. By considering multiple objectives, the DDQN algorithm allows for a sophisticated trade-off analysis, efficiently balancing between the different objectives to optimize overall system performance. Through the use of Blockchain technology, known for its secure, decentralized structure, our model enhances the integrity of data, providing a reliable and efficient solution for IoV-MEC systems. We conducted a comparative analysis of our model against the standard Deep Q-Network (DQN) and Deep Deterministic Policy Gradient (DDPG) algorithms, which are prevalent in this field. Our model demonstrated significant improvements over these traditional methods: energy consumption was reduced by 26.4%, latency decreased by 6.87%, and the cost was minimized by 7.41%.

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“…Meanwhile, other researchers have considered both energy and delay factors to improve energy efficiency or reduce costs [20][21][22][23]. One approach is an RL-based computation offloading and energy transfer algorithm that uses joint optimization methods to develop an allocation algorithm that obtains an approximately optimal solution for energy and computation resource allocation [20].…”

Section: Related Workmentioning

confidence: 99%

An Efficient Multi-Edge Server Coalition Computation Offloading Scheme of Sensor-Edge-Cloud

Yin,

Chen,

Yang

et al. 2024

IEEE Access

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The high latency and high energy consumption of wireless body areas networks (WBANs) for computing-intensive tasks is intolerable, especially for remote interventional surgery. In this paper, a multi-mobile edge server collaborative computation offloading scheme is proposed, which enables tasks to choose a server and offload a certain proportion of computation to efficiently handle computing-intensive services for massive users. More specifically, we formulate the problem of minimizing system latency and energy consumption, and then model the task offloading and resource allocation process as a Markov decision process (MDP). We have developed a scheme called m4m-PDQN to optimize offloading decisions, aiming to minimize the weighted sum of latency and energy consumption. Compared to existing singleserver offloading schemes, it is more effective in utilizing computing resources and reducing waiting time and energy consumption for computing tasks in the multiple-server collaborative computing scenarios. The experimental results show that it outperforms other algorithms in terms of performance and efficiency, significantly improving the quality of service (QoS) for wearable wireless body area networks for medical applications.INDEX TERMS multi-access edge computing (MEC), reinforcement learning, telemedicine, wireless body area networks

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Task Offloading and Resource Allocation for Industrial Internet of Things: A Double-Dueling Deep Q-Network Approach

Cited by 12 publications

References 29 publications

Fair and Scalable Orchestration of Network and Compute Resources for Virtual Edge Services

Fair and Scalable Orchestration of Network and Compute Resources for Virtual Edge Services

Multi-Objective Secure Task Offloading Strategy for Blockchain-Enabled IoV-MEC Systems: A Double Deep Q-Network Approach

An Efficient Multi-Edge Server Coalition Computation Offloading Scheme of Sensor-Edge-Cloud

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