Dynamic Bandwidth Slicing for Time-Critical IoT Data Streams in the Edge-Cloud Continuum

Habeeb, Fawzy; Alwasel, Khaled; Noor, Ayman; Jha, Devki Nandan; Alqattan, Duaa; Li, Yinhao; Aujla, Gagangeet Singh; Szydlo, Tomasz; Ranjan, Rajiv

Abstract

Edge computing has gained momentum in recent years, as complementary to cloud computing, for supporting applications (e.g. industrial control systems) that require Time-Critical communication guarantees. While edge computing can provide immediate analysis of streaming data from Internet of Things (IoT) devices, those devices lack computing capabilities to guarantee reasonable performance for Time-Critical applications. To alleviate this critical problem, the prevalent trend is to offload these data analytics tasks from the edge devices to the cloud. However, existing offloading approaches are static in nature as they are unable to adapt varying workload and network conditions. To handle these issues, we present a novel distributed and QoS-based multi-level queue traffic scheduling system that can undertake semi-automatic bandwidth slicing to process Time-Critical incoming traffic in the edge-cloud environments. Our developed system shows a great enhancement in latency and throughput as well as reduction in energy consumption for edge-cloud environments.

Citation

Habeeb, F., Alwasel, K., Noor, A., Jha, D. N., Alqattan, D., Li, Y., Aujla, G. S., Szydlo, T., & Ranjan, R. (2022). Dynamic Bandwidth Slicing for Time-Critical IoT Data Streams in the Edge-Cloud Continuum. IEEE Transactions on Industrial Informatics, 18(11), 8017-8026. https://doi.org/10.1109/tii.2022.3169971

• Journal Article Type: Article
• Online Publication Date: Apr 25, 2022
• Publication Date: 2022-11
• Deposit Date: May 6, 2022
• Publicly Available Date: May 6, 2022
• Journal: IEEE Transactions on Industrial Informatics
• Print ISSN: 1551-3203
• Electronic ISSN: 1941-0050
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 18
• Issue: 11
• Pages: 8017-8026
• DOI: https://doi.org/10.1109/tii.2022.3169971
• Public URL: https://durham-repository.worktribe.com/output/1209063