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• W4295308654 abstract "Due to increase in real-time mobile applications and Industrial Internet-of-Things (IIoT) devices, the edge computing paradigm provides a systematic and eccentric platform for real-time Internet-of-Things applications. Though the paradigm provides an effective infrastructure, however the resource requirements of IIoT devices change radically with time, which is described as a resource-agnostic property. Therefore, the estimation of resource requirements of IIoT devices is a critical and resilient assignment. In addition, it requires an extensive amount of resources to process the data traffic flows and microservice offloading. Hence, we present RAISE, a novel resource-agnostic microservice offloading scheme for mobile IIoT devices. RAISE efficiently estimates the resource-agnostic nature of IIoT devices to maximize their resource utilization in the network. Based on the estimated resource requirement, we propose a resource-agnostic microservice offloading scheme to maximize the success rate. Extensive experiments show that RAISE provides better performance in terms of network throughput and Quality-of-Service (QoS) than the other existing methods, SDTO and DTOS, in terms of cost and reliability." @default.
• W4295308654 created "2022-09-12" @default.
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• W4295308654 date "2023-01-01" @default.
• W4295308654 modified "2023-10-11" @default.
• W4295308654 title "Distributed Resource Distribution and Offloading for Resource-Agnostic Microservices in Industrial IoT" @default.
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• W4295308654 doi "https://doi.org/10.1109/tvt.2022.3206137" @default.
• W4295308654 hasPublicationYear "2023" @default.
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