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An efficient task offloading and resource allocation using dynamic arithmetic optimized double deep Q-network in cloud edge platform

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Abstract

The dramatic increase in the number of the Internet of Things (IoT) devices resulted in massive data being generated. This complexity mainly increases the need to offload the IoT tasks to minimize the higher latency, computation, and storage complexities of resourceful architectures such as cloud and edge computing. Even though edge computing minimizes latency-related issues, the model deployment adds new challenges when different offloading schemes or service architectures are utilized. The main aim of this paper is to minimize the latency of high-priority healthcare applications that needs immediate service using different steps. The improved Variational mode decomposition (VMD)-Random Forest (RF) architecture is used to classify the edge device application tasks into computationally intensive, time-sensitive, and priority-sensitive workloads. The tasks are mainly classified by taking different parameters as input such as the task length, network demand, delay sensitivity, and Virtual Machine (VM) utilization parameters. This step reduces the processing time of edge-based applications. For task offloading, a novel Dynamic arithmetic optimized double deep Q-network (DAO-DDQN) architecture is developed, which determines task offloading decisions based on the classification results from the VMD-optimized RF design. A Computational Access Point (CAP) has been formed using interconnected wireless access points and the CAP is used for executing the application requests sent from mobile edge devices. To improve the task processing and computational capabilities of edge devices, the Dynamic arithmetic optimization algorithm (DAOA) is employed to choose the optimal CAP for task offloading. These steps help to minimize the edge latency by simultaneously improving the edge network performance. The results show that the proposed methodology is efficient in improving the service parameters when terms of different parameters such as average delivery time, schedulability, computing delay, bandwidth consumption, communication delay, and latency. The proposed model offers a 32% improvement in scheduling rate, an 18% improvement in bandwidth consumption, and a 25% improvement in the average delivery time when compared to the existing techniques as per the simulation outcomes.

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Data sharing is not applicable to this article as no new data were created or analyzed in this study.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, SRM Institute of Science and Technology, Chennai, India

    K. Kumaran

  2. Department of Information Technology, Easwari Engineering College, Chennai, India

    K. Kumaran

  3. Department of Data Science and Business System, SRM Institute of Science and Technology, Chennai, India

    E. Sasikala

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  1. K. Kumaran
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Contributions

KK & ES agreed on the content of the study. KK & ES collected all the data for analysis. KK & ES agreed on the methodology. KK & ES completed the analysis based on agreed steps. Results and conclusions are discussed and written together. The authors read and approved the final manuscript.

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Correspondence to K. Kumaran.

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Kumaran, K., Sasikala, E. An efficient task offloading and resource allocation using dynamic arithmetic optimized double deep Q-network in cloud edge platform. Peer-to-Peer Netw. Appl. 16, 958–979 (2023). https://doi.org/10.1007/s12083-022-01440-2

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