A Novel Two-Layered Reinforcement Learning for Task Offloading with Tradeoff between Physical Machine Utilization Rate and Delay

Li, Quan; Wang, Zhiliang; Ren, Fuji

doi:10.3390/fi10070060

Cited by 20 publications

(14 citation statements)

References 26 publications

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“…8(d). Each Offloading cellular traffic to WLAN [93], (b) Offloading to a single MEC-enabled BS [94], (c) Offloading to one shared MEC server via multiple BSs [95], [96], [100], (d) Offloading to multiple MECenabled BSs [104], [105] and mobile cloudlets [106], [107]. mobile user has a first-in-first-out queue with limited buffer size to store the arriving tasks arriving as a Poisson process.…”

Section: B Data and Computation Offloadingmentioning

confidence: 99%

Applications of Deep Reinforcement Learning in Communications and Networking: A Survey

Luong

Hoang

Gong

et al. 2019

IEEE Commun. Surv. Tutorials

1,476

481

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This paper presents a comprehensive literature review on applications of deep reinforcement learning in communications and networking. Modern networks, e.g., Internet of Things (IoT) and Unmanned Aerial Vehicle (UAV) networks, become more decentralized and autonomous. In such networks, network entities need to make decisions locally to maximize the network performance under uncertainty of network environment. Reinforcement learning has been efficiently used to enable the network entities to obtain the optimal policy including, e.g., decisions or actions, given their states when the state and action spaces are small. However, in complex and large-scale networks, the state and action spaces are usually large, and the reinforcement learning may not be able to find the optimal policy in reasonable time. Therefore, deep reinforcement learning, a combination of reinforcement learning with deep learning, has been developed to overcome the shortcomings. In this survey, we first give a tutorial of deep reinforcement learning from fundamental concepts to advanced models. Then, we review deep reinforcement learning approaches proposed to address emerging issues in communications and networking. The issues include dynamic network access, data rate control, wireless caching, data offloading, network security, and connectivity preservation which are all important to next generation networks such as 5G and beyond. Furthermore, we present applications of deep reinforcement learning for traffic routing, resource sharing, and data collection. Finally, we highlight important challenges, open issues, and future research directions of applying deep reinforcement learning.

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Section: B Data and Computation Offloadingmentioning

confidence: 99%

Applications of Deep Reinforcement Learning in Communications and Networking: A Survey

Luong

Hoang

Gong

et al. 2019

IEEE Commun. Surv. Tutorials

1,476

481

View full text Add to dashboard Cite

show abstract

“…In [128], a namely deep reinforcement learning based resource allocation (DRLRA) scheme is proposed to allocate computing and network resources adaptively, in order to reduce the delay and balance the use of resources under varying MEC environment. In [134], several RL methods, e.g., Q-learning, SARSA, Expected SARSA, and Monte Carlo, Utilization rate of computing resources [126] are applied to solve the Fog-RAN resource allocation issues respectively. The performance and applicability of the methods are verified.…”

Section: E Aiot Application Layer -Iot Edge/fog/cloud Computing Systemsmentioning

confidence: 99%

Deep Reinforcement Learning for Autonomous Internet of Things: Model, Applications and Challenges

Lei

Tan

Zheng

et al. 2020

IEEE Commun. Surv. Tutorials

225

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The Internet of Things (IoT) extends the Internet connectivity into billions of IoT devices around the world, which collect and share information to reflect the status of physical world. The Autonomous Control System (ACS), on the other hand, performs control functions on the physical systems without external intervention over an extended period of time. The integration of IoT and ACS results in a new concept -autonomous IoT (AIoT). The sensors collect information on the system status, based on which intelligent agents in IoT devices as well as Edge/Fog/Cloud servers make control decisions for the actuators to react. In order to achieve autonomy, a promising method is for the intelligent agents to leverage the techniques in the field of artificial intelligence, especially reinforcement learning (RL) and deep reinforcement learning (DRL) for decision making. In this paper, we first provide comprehensive survey of the state-of-art research, and then propose a general model for the applications of RL/DRL in AIoT. Finally, the challenges and open issues for future research are identified.

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“…3) LBS for Task Offloading: Quan et al [164] proposed a two-layered RL algorithm for task offloading with a tradeoff between physical machine utilization rate and delay in M-CC. The k-nearest neighbors algorithm divides the physical machines into many clusters.…”

Section: Lbs In CC and Ecmentioning

confidence: 99%

A Review on Computational Intelligence Techniques in Cloud and Edge Computing

Asim

Wang

et al. 2020

IEEE Trans. Emerg. Top. Comput. Intell.

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Cloud computing (CC) is a centralized computing paradigm that accumulates resources centrally and provides these resources to users through Internet. Although CC holds a large number of resources, it may not be acceptable by real-time mobile applications, as it is usually far away from users geographically. On the other hand, edge computing (EC), which distributes resources to the network edge, enjoys increasing popularity in the applications with low-latency and high-reliability requirements. EC provides resources in a decentralized manner, which can respond to users' requirements faster than the normal CC, but with limited computing capacities. As both CC and EC are resource-sensitive, several big issues arise, such as how to conduct job scheduling, resource allocation, and task offloading, which significantly influence the performance of the whole system. To tackle these issues, many optimization problems have been formulated. These optimization problems usually have complex properties, such as non-convexity and NP-hardness, which may not be addressed by the traditional convex optimization-based solutions. Computational intelligence (CI), consisting of a set of nature-inspired computational approaches, recently exhibits great potential in addressing these optimization problems in CC and EC. This paper provides an overview of research problems in CC and EC and recent progresses in addressing them with the help of CI techniques. Informative discussions and future research trends are also presented, with the aim of offering insights to the readers and motivating new research directions.

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A Novel Two-Layered Reinforcement Learning for Task Offloading with Tradeoff between Physical Machine Utilization Rate and Delay

Cited by 20 publications

References 26 publications

Applications of Deep Reinforcement Learning in Communications and Networking: A Survey

Applications of Deep Reinforcement Learning in Communications and Networking: A Survey

Deep Reinforcement Learning for Autonomous Internet of Things: Model, Applications and Challenges

A Review on Computational Intelligence Techniques in Cloud and Edge Computing

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