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Toward an Intelligent Cache Management: In an Edge Computing Era for Delay Sensitive IoT Applications

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Abstract

The emergence of embedded technologies and Internet of Things (IoT), have perceived the proliferation of devices starting from tiny monitoring sensors, mobile devices, wearable devices, surveillance sensors etc. For the past few decades technological advancements in these devices leverages applications from home automation to health care industries. Due to this drastic growth in technology and newly emerging applications the number of IoT connected devices are expected to reach 42.62 billion with global mobile data traffic of 77.5 exabytes/month by 2022. So, offering required services with sufficient QoS parameters as per SLA is a challenging task. Also, the growing rate of wireless traffic exerts load to the core network and backhaul connections. Even the situations may get still worse with multimedia streaming applications. To mitigate the wireless traffic, Fog Caching (FC) is one of the promising solutions. In FC the popular contents in the mobile core network are cached in suitable places. However, identifying popular contents, locating cache, and replacing contents of cache are noticeable issues in FC. In this paper we propose an Intelligent framework (I-CADET) for efficient CAche management for Delay sensitive IoT applications in the Edge CompuTing era. To locate the cache in mobile core network Connected Dominating Set construction of graph theory is used. The content popularity is predicted by efficient ML technique. Then the identified popular contents are distributed over a constructed semigraph based connected edge dominating virtual backbone. The efficient distribution of popular content on the constructed semigraph based virtual backbone (hot spot places) increases the Quality of Experience (QoE). The numerical results reveal that the proposed framework improves the QoE in terms of content delivery and cache hit rate, minimized average downloading latency and backhaul load. The efficient usage of cache and bandwidth has been ensured while meeting high QoS.

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Data Availability

The datasets used during this research is publicly available (MovieLens Dataset). And it was preprocessed for the research and available from the corresponding author on reasonable request.

Code Availability

Available from the corresponding author on reasonable request.

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The authors declare that no funds received for this research.

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Authors and Affiliations

  1. School of Computing, SASTRA Deemed University, Thirumalaisamudram, Thanjavur, India

    V. Meena, Kannan Krithivasan, Punna Rahul & T. Suriya Praba

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  1. V. Meena
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  2. Kannan Krithivasan
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  3. Punna Rahul
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  4. T. Suriya Praba
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Correspondence to T. Suriya Praba.

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Meena, V., Krithivasan, K., Rahul, P. et al. Toward an Intelligent Cache Management: In an Edge Computing Era for Delay Sensitive IoT Applications. Wireless Pers Commun 131, 1075–1088 (2023). https://doi.org/10.1007/s11277-023-10469-2

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