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An Adaptive, Energy-Efficient DRL-Based and MCMC-Based Caching Strategy for IoT Systems

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Algorithmic Aspects of Cloud Computing (ALGOCLOUD 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14053))

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Abstract

The Internet of Things (IoT) has seen remarkable growth in recent years, but the data volatility and limited energy resources in these networks pose significant challenges. In addition, traditional quality of service metrics like throughput, latency, packet delay variation, and error rate remain important benchmarks. In this work, we explore the application of Markov Chain Monte Carlo (MCMC) methods to address these issues by designing efficient caching policies. Without the necessity for prior knowledge or context, MCMC methods provide a promising alternative to traditional caching schemes and existing machine learning models. We propose an MCMC-based caching strategy that can improve both cache hit rates and energy efficiency in IoT networks. Additionally, we introduce a hierarchical caching structure that allows parent nodes to process requests from several edge nodes and make autonomous caching decisions. Our experimental results indicate that the MCMC-based approach outperforms both traditional and other ML-based caching policies significantly. For environments where file popularity changes over time, we propose an MCMC-based adaptive caching solution. This solution detects shifts in popularity distribution using clustering and cluster similarity metrics, leading to an MCMC adaptation process. This adaptability further enhances the efficiency and effectiveness of our caching scheme, reducing training time and improving overall performance.

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Acknowledgments

The financial support of the European Union and Greece (Partnership Agreement for the Development Framework 2014–2020) under the Regional Operational Programme Ionian Islands 2014–2020 for the project “Laertis” is gratefully acknowledged.

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Authors and Affiliations

  1. Computer Engineering and Informatics Department, University of Patras, 26504, Patras, Greece

    Aristeidis Karras, Christos Karras & Spyros Sioutas

  2. Department of Informatics, Ionian University, 49100, Corfu, Greece

    Ioannis Karydis & Markos Avlonitis

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  1. Aristeidis Karras
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Correspondence to Aristeidis Karras .

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Editors and Affiliations

  1. Sapienza University of Rome, Rome, Italy

    Ioannis Chatzigiannakis

  2. Ionian University, Corfu, Greece

    Ioannis Karydis

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Karras, A., Karras, C., Karydis, I., Avlonitis, M., Sioutas, S. (2024). An Adaptive, Energy-Efficient DRL-Based and MCMC-Based Caching Strategy for IoT Systems. In: Chatzigiannakis, I., Karydis, I. (eds) Algorithmic Aspects of Cloud Computing. ALGOCLOUD 2023. Lecture Notes in Computer Science, vol 14053. Springer, Cham. https://doi.org/10.1007/978-3-031-49361-4_4

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  • DOI: https://doi.org/10.1007/978-3-031-49361-4_4

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