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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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Název: An efficient task offloading and resource allocation using dynamic arithmetic optimized double deep Q-network in cloud edge platform.
Autoři: Kumaran, K., Sasikala, E.
Zdroj: Peer-to-Peer Networking & Applications; Mar2023, Vol. 16 Issue 2, p958-979, 22p
Témata: EDGE computing, CLOUD computing, OPTIMIZATION algorithms, RESOURCE allocation, ARITHMETIC, VIRTUAL machine systems, MENTAL arithmetic
Abstrakt: 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. [ABSTRACT FROM AUTHOR]
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Abstrakt: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. [ABSTRACT FROM AUTHOR]
ISSN:19366442
DOI:10.1007/s12083-022-01440-2