Impact Assessment of Policy-Based Cache Management on Storage System Sustainability in Smart City Applications
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Abstract: The rapid growth of data in smart cities driven by interconnected systems like traffic monitoring, surveillance, and environmental sensing demands efficient and sustainable data management solutions. The aim of this study is to evaluate the effect of policy-based cache control on the performance and sustainability of the storage systems within smart city infrastructures. Synthetic workloads simulating urban data pattern were generated to evaluate system behavior under realistic conditions. Important performance metrics including cache-hit rate, latency, throughput, and energy consumption were analyzed across three different scenarios; namely, no cache, traditional LRU and proposed policy-based control. The findings show that the policy-based approach had an 85% cache hit rate, reduced latency to 70 ms, and improved throughput of 220 MB/s, while decreasing daily energy consumption of 95 kWh. These results shows clear benefits in both performances and energy efficiency. The study concludes that policy-based caching has a strong potential to enhance responsiveness of urban data infrastructure and sustainability. Its relevance to SDG 11 (Sustainable Cities and Communities) lies in its ability to support intelligent and low-impact technological systems, which promote resilient and smart urban growth.
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Alubady, R., Salman, M., & Mohamed, A. (2023). A Review of Modern Caching Strategies in Named Data Network: Overview, Classification, and Research Directions. Telecommunication Systems, 84(3), 1–46. Retrieved from https://doi.org/10.1007/s11235-023-01015-3.
Bello, H., Ige, A., & Ameyaw, M. (2024). Adaptive Machine Learning Models: Concepts for Real-Time Financial Fraud Prevention in Dynamic Environments. World Journal of Advanced Engineering, Technology and Sciences, 12(2), 021–034. Retrieved from https://doi.org/10.30574/wjaets.2024.12.2.0266.
Bilal, M., & Kang, S.-G. (2017). A Cache Management Scheme for Efficient Content Eviction and Replication in Cache Networks. IEEE Access, 5(16), 32720–32730. Retrieved from https://doi.org/10.1109/ACCESS.2017.2669344.
Chao, Y., & Han, R. (2025). A Hierarchical Cache Architecture-Oriented Cache Management Scheme for Information-Centric Networking. Future Internet, 17(1), 17. Retrieved from https://doi.org/10.3390/fi17010017.
Chidolue, O., Ohenhen, P., Umoh, ·, Ngozichukwu, ·, Fafure, A., & Ibekwe, P. (2024). Green Data Centers: Sustainable Practices For Energy-Efficient It Infrastructure. Engineering Science & Technology Journal, 5(1), 99–114. Retrieved from https://doi.org/10.51594/estj/v5i1.730.
Jangid, J. (2020). Efficient Training Data Caching for Deep Learning in Edge Computing Networks. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 7(8), 337–362. Retrieved from https://doi.org/10.32628/CSEIT20631113.
Khan, A. A., & Zakarya, M. (2021). Energy, Performance, and Cost Efficient Cloud Datacenters: A Survey. Computer Science Review, 40(100390), 1–27. Retrieved from https://doi.org/10.1016/j.cosrev.2021.100390.
Khedkar, V. (2024). The Carbon Conundrum: A Systematic Analysis of Environmental Impacts in Large-Scale Cloud Computing Infrastructure. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 10(6), 713–723. Retrieved from https://doi.org/10.32628/CSEIT241061115.
Krishna, K. (2025). Advancements in Cache Management: A Review of Machine Learning Innovations for Enhanced Performance and Security. Frontiers in Artificial Intelligence, 8, 1441250. Retrieved from https://doi.org/10.3389/frai.2025.1441250.
Levin, S. (2024). Unleashing Real-Time Analytics: A Comparative Study of In-Memory Computing vs. Traditional Disk-Based Systems. Brazilian Journal of Science, 3(5), 30–39. Retrieved from https://doi.org/10.14295/bjs.v3i5.553.
Mahomed, A. S., & Saha, A. K. (2025). Unleashing the Potential of 5G for Smart Cities: A Focus on Real-Time Digital Twin Integration. Smart Cities, 8(2), 70. Retrieved from https://doi.org/10.3390/smartcities8020070.
McKee, M. (2021). Policy-Based Access Controls. IDPro Body of Knowledge, 1, 1–3. Retrieved from https://doi.org/10.55621/idpro.61.
Narain, S. (2024). Integrating Smart City Technologies for Enhanced Urban Sustainability. Journal of Sustainable Solutions, 1(3), 13–17. Retrieved from https://doi.org/10.36676/j.sust.sol.v1.i3.15.
Orikpete, O., Fawole, A., & Ewim, D. (2023). Impact of Data Centers on Climate Change: A Review of Energy Efficient Strategies. The Journal of Engineering and Exact Sciences, 9(6), 16397–16409. Retrieved from https://doi.org/10.18540/jcecvl9iss6pp16397-01e.
Qaiser, F., Harthy, K., Hussain, M., Frnda, J., Amin, R., Gantassi, R., & Zakaria, M. (2025). Classifications and Analysis of Caching Strategies in Information‐Centric Networking for Modern Communication Systems. Engineering Reports, 7(0), 1–33. Retrieved from https://doi.org/10.1002/eng2.70005.
Ullah, A., Syed, ·, Anwar, M., Li, J., Nadeem, L., Mahmood, T., Rehman, A., Syed, M., Anwar, ·, & Saba, T. (2023). Smart Cities: The Role of Internet of Things and Machine Learning in Realizing a Data-Centric Smart Environment. Complex & Intelligent Systems, 10(0), 1–31. Retrieved from https://doi.org/10.1007/s40747-023-01175-4.
Shahrabani, M. M. N., & Apanaviciene, R. (2025). Evaluation of Smart Building Integration into a Smart City by Applying Machine Learning Techniques. Buildings, 15(12), 2031. Retrieved from https://doi.org/10.3390/buildings15122031.
Talebkhah, M., Sali, A., Marjani, M., Gordan, M., Hashim, S., & Rokhani, F. Z. (2021). IoT and Big Data Applications in Smart Cities: Recent Advances, Challenges, and Critical Issues. IEEE Access, 9(35), 126383–126412. Retrieved from https://doi.org/10.1109/ACCESS.2021.3070905.
Thangam, D., Haritha, M., Ramesh, R., Ramakrishna, G. N., Muddasir, N., Khan, A., Booshan, S., Booshan, B., & Ganesh, R. (2024). Impact of Data Centers on Power Consumption, Climate Change, and Sustainability. https://doi.org/10.4018/979-8-3693-1552-1.ch004.
Trigka, M., & Dritsas, E. (2025). Edge and Cloud Computing in Smart Cities. Future Internet, 17(3), 118. Retrieved from https://doi.org/10.3390/fi17030118.
Weerasinghe, S., Zaslavsky, A., Loke, S., Hassani, A., Abken, A., & Medvedev, A. (2022). From Traditional Adaptive Data Caching to Adaptive Context Caching: A Survey. ArXiv preprint arXiv:2211.11259.
Yıldırım, F., Yalman, Y., Bayındır, K. Ç., & Terciyanlı, E. (2025). Comprehensive Review of Edge Computing for Power Systems: State of the Art, Architecture, and Applications. Applied Sciences, 15(8), 4592. Retrieved from https://doi.org/10.3390/app15084592.
Yu, M., Li, R., & Chen, Y.-W. (2020). A Cache Replacement Policy Based on Multi-factors for Named Data Networking. Computers, Materials & Continua, 65(4), 321–336. Retrieved from https://doi.org/10.32604/cmc.2020.010831.
Zaman, M., Abdelwahed, S., & Zohrabi, N. (2024). A Review of IoT-Based Smart City Development and Management. Smart Cities, 7(3), 1462–1501. Retrieved from https://doi.org/10.3390/smartcities7030061
Zeng, F., Zhang, K., Wu, L., & Wu, J. (2022). Efficient Caching in Vehicular Edge Computing Based on Edge-Cloud Collaboration. IEEE Transactions on Vehicular Technology (Early Access). https://doi.org/10.1109/TVT.2022.3213130.
Zhang, M., Tang, Q., Kim, J.-G., Burgstaller, B., & Kim, S.-D. (2023). Adaptive Regression Prefetching Algorithm by Using Big Data Application Characteristics. Applied Sciences, 13(7), 4436. Retrieved from https://doi.org/10.3390/app13074436.
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