NDLI: Resource Prediction-Based Edge Collaboration Scheme for Improving QoE

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Resource Prediction-Based Edge Collaboration Scheme for Improving QoE

Content Provider	MDPI
Author	Park, Jinho Chung, Kwangsue
Copyright Year	2021
Description	Recent years have witnessed a growth in the Internet of Things (IoT) applications and devices; however, these devices are unable to meet the increased computational resource needs of the applications they host. Edge servers can provide sufficient computing resources. However, when the number of connected devices is large, the task processing efficiency decreases due to limited computing resources. Therefore, an edge collaboration scheme that utilizes other computing nodes to increase the efficiency of task processing and improve the quality of experience (QoE) was proposed. However, existing edge server collaboration schemes have low QoE because they do not consider other edge servers’ computing resources or communication time. In this paper, we propose a resource prediction-based edge collaboration scheme for improving QoE. We estimate computing resource usage based on the tasks received from the devices. According to the predicted computing resources, the edge server probabilistically collaborates with other edge servers. The proposed scheme is based on the delay model, and uses the greedy algorithm. It allocates computing resources to the task considering the computation and buffering time. Experimental results show that the proposed scheme achieves a high QoE compared with existing schemes because of the high success rate and low completion time.
Starting Page	8500
e-ISSN	14248220
DOI	10.3390/s21248500
Journal	Sensors
Issue Number	24
Volume Number	21
Language	English
Publisher	MDPI
Publisher Date	2021-12-20
Access Restriction	Open
Subject Keyword	Sensors Information and Library Science Internet of Things (iot) Edge Computing Mobile Edge Computing (mec) Computation Offloading
Content Type	Text
Resource Type	Article

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