A New Task Offloading Algorithm in Edge Computing

Zhang, Zhenjiang; Li, Chen; Peng, Sheng‐Lung; Pei, Xintong

doi:10.21203/rs.3.rs-56632/v1

Cited by 5 publications

(7 citation statements)

References 3 publications

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“…[9] designed an adaptive computation offloading method with deep RL to balance the tradeoff between energy consumption and data transmission delay in vehicular networks. [29] used a learning‐based multi‐agent load balancing scheme to improve user experience in IoT environments. It considered both computing energy cost and latency, and obtained better results in time complexity and response delay of end users.…”

Section: Related Workmentioning

confidence: 99%

Intelligent task migration with deep Qlearning in multi‐access edge computing

Huang

Lin

2021

IET Communications

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Multi-access edge computing provides computation and network resources in proximity to user applications in mobile environments. Deploying edge servers in network boundary can not only offload the heavy task loading on the cloud, but also alleviate resource-limited capabilities of mobile devices. Rather than many stand-alone edge servers, the concept of multi-server edge computing is recently advocated to contend with the issues of system scalability and service quality against dynamic task workload. This study exploits collaborative computing resources and designs a task migration strategy for multiple edge servers in mobile networks. This study formulates a queueing optimization problem of minimizing the overall service time in a multi-server system. An intelligent task migration scheme is then developed using the deep reinforcement learning and Q-learning techniques. With a variety of numerical attributes derived from the queueing model, this intelligent scheme can arrange the task distribution among edge servers to enhance the task processing capability. Simulation-based results show that the proposed task migration scheme can sustain service efficiency and resource utilization, which is promising as compared with conventional designs without collaborative intelligence in mobile environments.

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Section: Related Workmentioning

confidence: 99%

Intelligent task migration with deep Qlearning in multi‐access edge computing

Huang

Lin

2021

IET Communications

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“…From an architectural point of view, the majority of the works ( [16], [22], [25], [6], [2], [19], [31], [7], [8], [33], [20], [15] and [1]) adopt a centralized architecture while some others [36], [10], [6], [30], [32], [26] have implemented a decentralized architecture. In the centralized architecture, offloading decisions are made at a central point, whereas in a decentralized architecture each IoT device implements the decision making algorithm.…”

Section: Related Workmentioning

confidence: 99%

“…However, the priority is no longer put on the response time. An intermediate collaboration between the horizontal and vertical approach is used in [36] where an IoT device can offload its task to an edge server which in turn can distribute the task between itself and other edge servers. Adding a central cloud server to this operation results in the collaboration employed in [10], [29], [22], [6], [5], [14] and [17].…”

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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“…In [66] an RL method is used to distribute the tasks, produced by different user devices, among a set of edge servers. To deal with the combinatorial action space (any task can be placed at any available edge server), authors introduce a multi-agent algorithm where each Deep Q-Network (DQN) [44] agent makes an action that corresponds to the location where the task will be offloaded.…”

Section: Reinforcement Learning In Edge Computingmentioning

confidence: 99%

Attention-Based Model and Deep Reinforcement Learning for Distribution of Event Processing Tasks

Mazayev¹,

Al-Tam²,

Correia³

2021

Preprint

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Event processing is the cornerstone of the dynamic and responsive Internet of Things (IoT). Recent approaches in this area are based on representational state transfer (REST) principles, which allow event processing tasks to be placed at any device that follows the same principles. However, the tasks should be properly distributed among edge devices to ensure fair resources utilization and guarantee seamless execution. This article investigates the use of deep learning to fairly distribute the tasks. An attention-based neural network model is proposed to generate efficient load balancing solutions under different scenarios. The proposed model is based on the Transformer and Pointer Network architectures, and is trained by an advantage actor-critic reinforcement learning algorithm. The model is designed to scale to the number of event processing tasks and the number of edge devices, with no need for hyperparameters re-tuning or even retraining. Extensive experimental results show that the proposed model outperforms conventional heuristics in many key performance indicators. The generic design and the obtained results show that the proposed model can potentially be applied to several other load balancing problem variations, which makes the proposal an attractive option to be used in real-world scenarios due to its scalability and efficiency.

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A New Task Offloading Algorithm in Edge Computing

Cited by 5 publications

References 3 publications

Intelligent task migration with deep Qlearning in multi‐access edge computing

Intelligent task migration with deep Qlearning in multi‐access edge computing

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

Attention-Based Model and Deep Reinforcement Learning for Distribution of Event Processing Tasks

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