Deep reinforcement learning for dynamic computation offloading and resource allocation in cache-assisted mobile edge computing systems

Nath, Samrat; Wu, Jingxian

doi:10.23919/icn.2020.0014

Cited by 164 publications

(71 citation statements)

References 18 publications

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“…In Reinforcement Learning [13], the agent does not need to know part of the information of the network in advance and achieves the optimization goal of maximizing the long-term benefits of energy consumption and delay by adjusting strategies. Researchers have recently begun to use deep reinforcement learning algorithms to solve this problem [14].…”

Section: Offloading Algorithm Based On Reinforcement Learningmentioning

confidence: 99%

Offloading in Mobile Edge Computing Based on Federated Reinforcement Learning

Xue

Gao

et al. 2022

Wireless Communications and Mobile Computing

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Mobile edge computing (MEC) has become a more and more popular technology, which plays a very important role in various fields. In view of the task of offloading of multiple users, most of the existing studies do not take into account data sharing and cooperation among users, which can easily lead to less generalization of the model trained by a single user, and some data sharing may also cause privacy leakage. Then, this paper uses the method of federated reinforcement learning to solve this problem in order to insure privacy. Besides, considering the poor quality of local models, which leads to the poor versatility of the overall parameters, this paper proposes a federated reinforcement learning method based on Attention mechanism to aggregate the parameter weights, which makes the new model more generalized. The experimental results show that the federated reinforcement learning task offloading model with Attention mechanism can reduce the processing delay of the task.

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Section: Offloading Algorithm Based On Reinforcement Learningmentioning

confidence: 99%

Offloading in Mobile Edge Computing Based on Federated Reinforcement Learning

Xue

Gao

et al. 2022

Wireless Communications and Mobile Computing

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“…This allows for different offloading scenarios [3]. A collaboration between IoT devices and an edge server is implemented in [34], [23], [16], [9], [25], [32], [2], [19], [31], [7], [8], [20], [15] and [1]. This is a vertical collaboration.…”

Section: Related Workmentioning

confidence: 99%

Task Offloading in Autonomous IoT Systems using Deep Reinforcement Learning and ns3-gym

Boni¹,

Hassan²,

Drira³

2021

Proceedings of the 11th International Conference on the Internet of Things

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IoT systems grow quickly and are massively present in urban areas. Their successful deployment requires autonomy that can be built on automated learning technologies such as Deep Learning. The IoT applications require important computational resources, rarely available on devices. Autonomous IoT systems require the computation power available on the edge and cloud servers in order to offload some tasks related to the supported applications and the underlying platforms. Task offloading constitutes a big challenge in autonomous IoT systems due to the huge number of IoT devices for scenarios of the family of smart cities. Managing task offloading in such contexts requires adaptive strategies capable of taking into consideration the rapid evolution of available resources and proposing efficient offloading solutions to all received requests. In this paper we use a Deep Reinforcement Learning (DRL) approach capable of handling large state spaces, and resolve the optimization problem in this context, where other techniques can not scale efficiently. Our solution is based on a DRL agent that was developed in the ns3-gym framework and was tested on IoT system scenario implemented in the NS3 simulator. The results obtained show that the DRL agent can adapt quickly to resource evolution in the IoT system and can handle big number of demands fulfilling scalabilty requirements of autonomous IoT systems.

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“…Many existing works have applied DRL to caching problems for different scenarios, including Content Delivery Network (CDNs) [5], [11], mobile networks [12], [13], and IoT networks [14]- [22]. Nevertheless, most of them did not take the limited data lifetime and constrained device energy into account.…”

Section: Introductionmentioning

confidence: 99%

A Deep Reinforcement Learning-Based Caching Strategy for IoT Networks with Transient Data

Wu¹,

Nasehzadeh²,

Wang³

2022

Preprint

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The Internet of Things (IoT) has been continuously rising in the past few years, and its potentials are now more apparent. However, transient data generation and limited energy resources are the major bottlenecks of these networks. Besides, minimum delay and other conventional quality of service measurements are still valid requirements to meet. An efficient caching policy can help meet the standard quality of service requirements while bypassing IoT networks' specific limitations. Adopting deep reinforcement learning (DRL) algorithms enables us to develop an effective caching scheme without the need for any prior knowledge or contextual information. In this work, we propose a DRL-based caching scheme that improves the cache hit rate and reduces energy consumption of the IoT networks, in the meanwhile, taking data freshness and limited lifetime of IoT data into account. To better capture the regional-different popularity distribution, we propose a hierarchical architecture to deploy edge caching nodes in IoT networks. The results of comprehensive experiments show that our proposed method outperforms the well-known conventional caching policies and an existing DRLbased solution in terms of cache hit rate and energy consumption of the IoT networks by considerable margins.

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Deep reinforcement learning for dynamic computation offloading and resource allocation in cache-assisted mobile edge computing systems

Cited by 164 publications

References 18 publications

Offloading in Mobile Edge Computing Based on Federated Reinforcement Learning

Offloading in Mobile Edge Computing Based on Federated Reinforcement Learning

Task Offloading in Autonomous IoT Systems using Deep Reinforcement Learning and ns3-gym

A Deep Reinforcement Learning-Based Caching Strategy for IoT Networks with Transient Data

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