DMRO: A Deep Meta Reinforcement Learning-Based Task Offloading Framework for Edge-Cloud Computing

Qu, Guanjin; Wu, Huaming; Li, Ruidong; Jiao, Pengfei

doi:10.1109/tnsm.2021.3087258

Cited by 148 publications

(36 citation statements)

References 38 publications

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“…Recently, an efficient algorithm based on deep meta reinforcement learning was proposed in [25] for IoT-edge-cloud computing systems with the goal of reducing the burden of computing and improving the processing of tasks. Specifically, a different number of DNNs are combined with Q-learning to derive an efficient offloading decision and increase the learning ability of the system.…”

Section: Single Edge Servermentioning

confidence: 99%

Optimizing Task Offloading Energy in Multi-User Multi-UAV-Enabled Mobile Edge-Cloud Computing Systems

2022

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With the emergence of various new Internet of Things (IoT) devices and the rapid increase in the number of users, enormous services and complex applications are growing rapidly. However, these services and applications are resource-intensive and data-hungry, requiring satisfactory quality-of-service (QoS) and network coverage density guarantees in sparsely populated areas, whereas the limited battery life and computing resources of IoT devices will inevitably become insufficient. Unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) is one of the most promising solutions that ensures the stability and expansion of the network coverage area for these applications and provides them with computational capabilities. In this paper, computation offloading and resource allocation are jointly considered for multi-user multi-UAV-enabled mobile edge-cloud computing systems. First, we propose an efficient resource allocation and computation offloading model for a multi-user multi-UAV-enabled mobile edge-cloud computing system. Our proposed system is scalable and can support increases in network traffic without performance degradation. In addition, the network deploys multi-level mobile edge computing (MEC) technology to provide the computational capabilities at the edge of the radio access network (RAN). The core network is based on software-defined networking (SDN) technology to manage network traffic. Experimental results demonstrate that the proposed model can dramatically boost the system performance of the system in terms of time and energy.

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Section: Single Edge Servermentioning

confidence: 99%

Optimizing Task Offloading Energy in Multi-User Multi-UAV-Enabled Mobile Edge-Cloud Computing Systems

2022

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“…There are also substantial studies which use RL as an aside system in cooperation with mobile-cloud system, to deal with optimization issues including online resource allocation [337], task scheduling [338], workload scheduling [339], computation offloading [340]- [346], and service mitigation [347]. Among these works, applications are implemented on the Internet of Thing (IOT) [337], [338], [340], [343], 5G network [342], telemonitoring [346], or vehicular terminals [345]. The basic motivation is that those optimization problems are generally NP-hard and easier to solve by DRL built on MDP [343], [344].…”

Section: Rl-assisted Optimizationmentioning

confidence: 99%

“…Among these works, applications are implemented on the Internet of Thing (IOT) [337], [338], [340], [343], 5G network [342], telemonitoring [346], or vehicular terminals [345]. The basic motivation is that those optimization problems are generally NP-hard and easier to solve by DRL built on MDP [343], [344]. The detailed RL methods applied in these attempts include Q-Learning [339], [346], DQN [337], [340], REINFORCE [338], DDPG [341], PPO [345], and meta-RL [343].…”

Section: Rl-assisted Optimizationmentioning

confidence: 99%

“…The basic motivation is that those optimization problems are generally NP-hard and easier to solve by DRL built on MDP [343], [344]. The detailed RL methods applied in these attempts include Q-Learning [339], [346], DQN [337], [340], REINFORCE [338], DDPG [341], PPO [345], and meta-RL [343]. Some key elements, such as storage space [344] or context impact [346], might also be took into account and is used to determined the computation stage of service .…”

Section: Rl-assisted Optimizationmentioning

confidence: 99%

See 1 more Smart Citation

Edge-Cloud Polarization and Collaboration: A Comprehensive Survey for AI

Yao¹,

Zhang²,

Yang³

et al. 2021

Preprint

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Influenced by the great success of deep learning via cloud computing and the rapid development of edge chips, research in artificial intelligence (AI) has shifted to both of the computing paradigms, i.e., cloud computing and edge computing. In recent years, we have witnessed significant progress in developing more advanced AI models on cloud servers that surpass traditional deep learning models owing to model innovations (e.g., Transformers, Pretrained families), explosion of training data and soaring computing capabilities. However, edge computing, especially edge and cloud collaborative computing, are still in its infancy to announce their success due to the resource-constrained IoT scenarios with very limited algorithms deployed. In this survey, we conduct a systematic review for both cloud and edge AI. Specifically, we are the first to set up the collaborative learning mechanism for cloud and edge modeling with a thorough review of the architectures that enable such mechanism. We also discuss potentials and practical experiences of some on-going advanced edge AI topics including pretraining models, graph neural networks and reinforcement learning. Finally, we discuss the promising directions and challenges in this field.

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“…However, due to the limited computing resources and battery capacity of IoT devices, these computation-intensive tasks cannot be fully handled [3][4][5]. To cope with this situation, mobile cloud computing (MCC) came into being [6][7][8]. However, offloading tasks to cloud computing centers for processing will introduce significant delays [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Task Offloading for NOMA-Enabled Mobile Edge Computing with Heterogeneous Networks

Xing

et al. 2022

Security and Communication Networks

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With the rapid development of the Internet of Things (IoT), more and more computation-intensive tasks are generated by IoT devices. Due to their own limitations, IoT devices cannot process all tasks locally, and some tasks need to be offloaded to edge servers for processing. In addition, nonorthogonal multiple access (NOMA) technology allows multiple IoT devices to share the same frequency resource. IoT devices can use NOMA technology to transmit data to increase the data transmission rate. In this article, we study the problem of NOMA-enabled dynamic task offloading in heterogeneous networks. We formulate a stochastic optimization problem to minimize system energy consumption. Using stochastic optimization techniques, we transform this problem into a deterministic optimization problem and decompose it into five sub-problems to solve. At the same time, we propose a NOMA-enabled dynamic task offloading (NDTO) algorithm. Then, we mathematically analyze the performance of the NDTO algorithm. We conduct a series of parameter analysis experiments and comparative experiments, and the results verify the performance of the NDTO algorithm.

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DMRO: A Deep Meta Reinforcement Learning-Based Task Offloading Framework for Edge-Cloud Computing

Cited by 148 publications

References 38 publications

Optimizing Task Offloading Energy in Multi-User Multi-UAV-Enabled Mobile Edge-Cloud Computing Systems

Optimizing Task Offloading Energy in Multi-User Multi-UAV-Enabled Mobile Edge-Cloud Computing Systems

Edge-Cloud Polarization and Collaboration: A Comprehensive Survey for AI

Dynamic Task Offloading for NOMA-Enabled Mobile Edge Computing with Heterogeneous Networks

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