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1 |
Attention-based model and deep reinforcement learning for distribution of event processing tasks
مدل مبتنی بر توجه و یادگیری تقویتی عمیق برای توزیع وظایف پردازش رویداد-2022 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 actorcritic 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.
keywords: Web of Things (WoT) | Representational state transfer (REST) | application programming interface (APIs) | Edge computing | Load balancing | Resource placement | Deep reinforcement leaning | Transformer model | Pointer networks | Actor critic |
مقاله انگلیسی |
2 |
A pointer network based deep learning algorithm for unconstrained binary quadratic programming problem
یک شبکه اشاره گر مبتنی بر الگوریتم یادگیری عمیق برای مسئله برنامه نویسی درجه دوم باینری نامحدود-2020 Combinatorial optimization problems have been widely used in various fields. And many types of com- binatorial optimization problems can be generalized into the model of unconstrained binary quadratic programming (UBQP). Therefore, designing an effective and efficient algorithm for UBQP problems will also contribute to solving other combinatorial optimization problems. Pointer network is an end-to-end sequential decision structure and combines with deep learning technology. With the utilization of the structural characteristics of combinatorial optimization problems and the ability to extract the rule be- hind the data by deep learning, pointer network has been successfully applied to solve several classical combinatorial optimization problems. In this paper, a pointer network based algorithm is designed to solve UBQP problems. The network model is trained by supervised learning (SL) and deep reinforcement learning (DRL) respectively. Trained pointer network models are evaluated by self-generated benchmark dataset and ORLIB dataset respectively. Experimental results show that pointer network model trained by SL has strong learning ability to specific distributed dataset. Pointer network model trained by DRL can learn more general distribution data characteristics. In other words, it can quickly solve problems with great generalization ability. As a result, the framework proposed in this paper for UBQP has great potential to solve large scale combinatorial optimization problems. Keywords: UBQP | Pointer network | Supervised learning | Deep reinforcement learning |
مقاله انگلیسی |