CacheCraft: A Topology-Aware PageRank Centrality Algorithm for Cache Optimization in Named Data Networking

Ridha M. Negara; Nana R. Syambas; Eueung Mulyana; Rashid M. Fajri; Mochamad S. Budiana

doi:10.28991/ESJ-2025-09-02-09

Authors

Ridha M. Negara
ridhanegara@telkomuniversity.ac.id
1) School of Electrical Engineering and Informatics, Institut Teknologi Bandung, Bandung, Indonesia. 2) School of Electrical Engineering, Telkom University, Bandung, Indonesia. 3) The University Center of Excellence for Intelligent Sensing-IoT, Telkom University, Bandung, Indonesia. https://orcid.org/0000-0002-4757-4429
Nana R. Syambas School of Electrical Engineering and Informatics, Institut Teknologi Bandung, Bandung,, Indonesia
Eueung Mulyana School of Electrical Engineering and Informatics, Institut Teknologi Bandung, Bandung,, Indonesia
Rashid M. Fajri School of Electrical Engineering, Telkom University, Bandung,, Indonesia
Mochamad S. Budiana 1) School of Electrical Engineering and Informatics, Institut Teknologi Bandung, Bandung, Indonesia. 2) School of Electrical Engineering, Telkom University, Bandung, Indonesia.

Vol. 9 No. 2 (2025): April

Research Articles

Downloads

PDF

Abstract
How to Cite
Metrics
References
License

This study introduces CacheCraft, a novel approach for heterogeneous Content Store (CS) capacity allocation in Named Data Networking (NDN). Traditional NDN allocates CS capacity uniformly across routers, assuming equal storage requirements for all nodes. However, user content preferences and traffic patterns vary significantly, necessitating a more tailored allocation strategy. Additionally, the complexity of network topologies exacerbates the challenge, as static and homogeneous CS allocations lead to inefficiencies, increased latency, and reduced cache effectiveness in dynamic and dense networks. CacheCraft addresses these challenges by leveraging the PageRank algorithm to calculate the centrality of each node in the network. This centrality value determines the proportion of CS capacity assigned to each node, optimizing storage for nodes with higher traffic and strategic importance. The use of PageRank ensures scalable and reliable centrality computation, even in complex topologies. The performance of CacheCraft is validated across diverse network scenarios, including topologies of varying complexity, using metrics such as Cache Hit Ratio (CHR), average latency, and time complexity. Experimental results demonstrate that CacheCraft achieves an average improvement of 7.8% in CHR and a 5.6 ms reduction in latency compared to state-of-the-art methods. Moreover, CacheCraft maintains algorithmic computational efficiency, making it suitable for real-world deployment in complex and dynamic NDN environments. These findings highlight CacheCraft as a robust and scalable solution for optimizing NDN performance through adaptive and efficient CS capacity allocation.

Doi: 10.28991/ESJ-2025-09-02-09

Full Text: PDF

Negara, R. M., Syambas, N. R., Mulyana, E., Fajri, R. M., & Budiana, M. S. (2025). CacheCraft: A Topology-Aware PageRank Centrality Algorithm for Cache Optimization in Named Data Networking. Emerging Science Journal, 9(2), 659–676. https://doi.org/10.28991/ESJ-2025-09-02-09

Download Citation

Parrinello, E., Bazco-Nogueras, A., & Elia, P. (2024). Fundamental Limits of Topology-Aware Shared-Cache Networks. IEEE Transactions on Information Theory, 70(4), 2538–2565. doi:10.1109/TIT.2023.3321918.

Zhao, Y., Zhang, S., & Yao, Z. (2023). A Hybrid Approach for Smart Alert Generation. International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2023, 1–6. doi:10.1109/ICECCME57830.2023.10253233.

Zhang, L., Afanasyev, A., Burke, J., Jacobson, V., Claffy, K. C., Crowley, P., Papadopoulos, C., Wang, L., & Zhang, B. (2014). Named data networking. Computer Communication Review, 44(3), 66–73. doi:10.1145/2656877.2656887.

Ur Rehman, M. A., Kim, D., Choi, K., Ullah, R., & Kim, B. S. (2019). A statistical performance analysis of named data ultra-dense networks. Applied Sciences (Switzerland), 9(18), 3714. doi:10.3390/app9183714.

Lee, H., Lee, B., Yang, H., Kim, J., Kim, S., Shin, W., Shim, B., & Vincent Poor, H. (2023). Towards 6G Hyper-Connectivity: Vision, Challenges, and Key Enabling Technologies. Journal of Communications and Networks, 25(3), 344–354. doi:10.23919/JCN.2023.000006.

Xia, X., Chen, F., He, Q., Cui, G., Lai, P., Abdelrazek, M., Grundy, J., & Jin, H. (2020). Graph-based data caching optimization for edge computing. Future Generation Computer Systems, 113, 228–239. doi:10.1016/j.future.2020.07.016.

Pervej, M. F., Jin, R., Lin, S. C., & Dai, H. (2024). Efficient Content Delivery in User-Centric and Cache-Enabled Vehicular Edge Networks with Deadline-Constrained Heterogeneous Demands. IEEE Transactions on Vehicular Technology, 73(1), 1129–1145. doi:10.1109/TVT.2023.3300954.

Alubady, R., Salman, M., & Mohamed, A. S. (2023). A review of modern caching strategies in named data network: Overview, classification, and research directions. Telecommunication Systems, 84(4), 581-626. doi:10.1007/s11235-023-01015-3.

C.N., P., Vimala, H. S., & J., S. (2023). A systematic survey on content caching in ICN and ICN-IoT: Challenges, approaches and strategies. Computer Networks, 233, 109896. doi:10.1016/j.comnet.2023.109896.

Yang, H., Pan, H., & Ma, L. (2023). A Review on Software Defined Content Delivery Network: A Novel Combination of CDN and SDN. IEEE Access, 11, 43822–43843. doi:10.1109/ACCESS.2023.3267737.

Azamuddin, W. M. H., Aman, A. H. M., Sallehuddin, H., Abualsaud, K., & Mansor, N. (2023). The Emerging of Named Data Networking: Architecture, Application, and Technology. IEEE Access, 11, 23620–23633. doi:10.1109/ACCESS.2023.3243006.

Rezazad, M., & Tay, Y. C. (2020). Decoupling NDN caches via CCndnS: Design, analysis, and application. Computer Communications, 151, 338–354. doi:10.1016/j.comcom.2019.12.053.

Tanaka, D., & Kawarasaki, M. (2016). Congestion control in named data networking. 2016 IEEE International Symposium on Local and Metropolitan Area Networks (LANMAN), 1–6. doi:10.1109/lanman.2016.7548848.

Dinh, N. T., & Kim, Y. (2022). An Efficient Distributed Content Store-Based Caching Policy for Information-Centric Networking " . Sensors, 22(4), 1577. doi:10.3390/s22041577.

Amadeo, M., Campolo, C., Ruggeri, G., & Molinaro, A. (2022). Popularity-Aware Closeness Based Caching in NDN Edge Networks. Sensors, 22(9), 3460. doi:10.3390/s22093460.

Nour, B., Khelifi, H., Moungla, H., Hussain, R., & Guizani, N. (2020). A Distributed Cache Placement Scheme for Large-Scale Information-Centric Networking. IEEE Network, 34(6), 126–132. doi:10.1109/MNET.011.2000081.

Yang, J., Liu, T., Yang, Y., Zhang, P., Wu, D., & Wang, R. (2022). Diversified Demands Integration-Based Proactive Caching Strategy towards Edge Networks. Wireless Communications and Mobile Computing, 2022, 1–14. doi:10.1155/2022/9378654.

Ioannou, A., & Weber, S. (2014). Towards on-path caching alternatives in Information-Centric Networks. 39th Annual IEEE Conference on Local Computer Networks, 362–365. doi:10.1109/lcn.2014.6925792.

Kumar, S., Tiwari, R., & Hong, W. C. (2021). Qos improvement using in-network caching based on clustering and popularity heuristics in CCN. Sensors, 21(21), 7204. doi:10.3390/s21217204.

Gui, Y., & Chen, Y. (2021). A Cache Placement Strategy Based on Entropy Weighting Method and TOPSIS in Named Data Networking. IEEE Access, 9, 56240–56252. doi:10.1109/ACCESS.2021.3071427.

You, H.-S., Kim, J.-H., Shin, W.-Y., & Kim, S.-W. (2021). Location-Aware Caching via Predicting Heterogeneous File Preferences in Mobile Networks. 2021 IEEE International Conference on Pervasive Computing and Communications Workshops and Other Affiliated Events (PerCom Workshops), 336–339. doi:10.1109/percomworkshops51409.2021.9431023.

Freund, A. J., & Giabbanelli, P. J. (2022). An Experimental Study on the Scalability of Recent Node Centrality Metrics in Sparse Complex Networks. Frontiers in Big Data, 5. doi:10.3389/fdata.2022.797584.

Chiranjeevi, M., Dhuli, V. S., Enduri, M. K., Hajarathaiah, K., & Cenkeramaddi, L. R. (2024). Quantifying Node Influence in Networks: Isolating-Betweenness Centrality for Improved Ranking. IEEE Access, 12, 93711–93722. doi:10.1109/access.2024.3424834.

Wč…s, T., & Skibski, O. (2023). Axiomatic characterization of PageRank. Artificial Intelligence, 318. doi:10.1016/j.artint.2023.103900.

Cipolla, S., Durastante, F., & Tudisco, F. (2021). Nonlocal PageRank. ESAIM: Mathematical Modelling and Numerical Analysis, 55(1), 77–97. doi:10.1051/m2an/2020071.

Lal, K. N., & Kumar, A. (2019). A popularity based content eviction scheme via betweenness-centrality caching approach for content-centric networking (CCN). Wireless Networks, 25(2), 585–596. doi:10.1007/s11276-017-1577-z.

Duan, Y., Ni, H., & Zhu, X. (2022). A Dynamic Cache Allocation Mechanism (DCAM) for Reliable Multicast in Information-Centric Networking. Future Internet, 14(4), 1–15,. doi:10.3390/fi14040105.

Negara, R. M., Syambas, N. R., & Mulyana, E. (2023). C3CPS: CRITIC-CoCoSo-based caching placement strategy using multi-criteria decision method for efficient content distribution in Named Data Networking. Journal of King Saud University - Computer and Information Sciences, 35(9), 101714. doi:10.1016/j.jksuci.2023.101714.

Lal, K. N., & Kumar, A. (2018). A Centrality-measures based Caching Scheme for Content-centric Networking (CCN). Multimedia Tools and Applications, 77(14), 17625–17642. doi:10.1007/s11042-017-5183-y.

Agryzkov, T., Tortosa, L., Vicent, J. F., & Wilson, R. (2019). A centrality measure for urban networks based on the eigenvector centrality concept. Environment and Planning B: Urban Analytics and City Science, 46(4), 668–689. doi:10.1177/2399808317724444.

Li, Z., Tang, J., Zhao, C., & Gao, F. (2023). Improved centrality measure based on the adapted PageRank algorithm for urban transportation multiplex networks. Chaos, Solitons and Fractals, 167, 112998. doi:10.1016/j.chaos.2022.112998.

Behera, R. K., Rath, S. K., Misra, S., Damaševičius, R., & Maskeliunas, R. (2019). Distributed centrality analysis of social network data using MapReduce. Algorithms, 12(8), 161. doi:10.3390/a12080161.

Chai, W. K., He, D., Psaras, I., & Pavlou, G. (2013). Cache "less for more” in information-centric networks (extended version). Computer Communications, 36(7), 758–770. doi:10.1016/j.comcom.2013.01.007.

Tortosa, L., Vicent, J. F., & Yeghikyan, G. (2021). An algorithm for ranking the nodes of multiplex networks with data based on the PageRank concept. Applied Mathematics and Computation, 392, 125676. doi:10.1016/j.amc.2020.125676.

Wang, Y., Li, H., Zhang, L., Zhao, L., & Li, W. (2022). Identifying influential nodes in social networks: Centripetal centrality and seed exclusion approach. Chaos, Solitons and Fractals, 162, 112513. doi:10.1016/j.chaos.2022.112513.

Ianni, M., Masciari, E., & Sperlí, G. (2021). A survey of Big Data dimensions vs Social Networks analysis. Journal of Intelligent Information Systems, 57(1), 73–100. doi:10.1007/s10844-020-00629-2.

Chung, F. (2014). A brief survey of PageRank algorithms. IEEE Transactions on Network Science and Engineering, 1(1), 38–42. doi:10.1109/TNSE.2014.2380315.

Hashemi, A., Dowlatshahi, M. B., & Nezamabadi-pour, H. (2020). MGFS: A multi-label graph-based feature selection algorithm via PageRank centrality. Expert Systems with Applications, 142. doi:10.1016/j.eswa.2019.113024.

Mendes, P. (2023). Named Service Networking as a Primer for the Metaverse. IEEE Communications Magazine, 61(9), 24–30. doi:10.1109/MCOM.005.2200713.

Hou, J., Xia, H., Lu, H., & Nayak, A. (2021). A GNN-based Approach to Optimize Cache Hit Ratio in NDN Networks. 2021 IEEE Global Communications Conference (GLOBECOM), 1–6. doi:10.1109/globecom46510.2021.9685872.

Amadeo, M. (2021). A Literature Review on Caching Transient Contents in Vehicular Named Data Networking. Telecom, 2(1), 75–92. doi:10.3390/telecom2010006.

Hamidi, E. A. Z., Akbar, K. M., Negara, R. M., Payangan, I. P., Puspitaningsih, M. D., & As' Ari, A. Z. P. (2024, July). NDN Collaborative Caching Replacement and Placement Policy Performance Evaluation. In 2024 10th International Conference on Wireless and Telematics (ICWT), 1-5. doi:10.1109/ICWT62080.2024.10674708.

Arif, M. S., Mukheimer, A., & Ejaz, A. (2024). Cannibalism and Harvesting in Tritrophic Chains: Insights from Mathematical and Artificial Neural Network Analysis. Emerging Science Journal, 8(4), 1262-1279. doi:10.28991/ESJ-2024-08-04-02.

He, X., Liu, H., Li, W., Valera, A., & Seah, W. K. G. (2024). EABC: Energy-aware Centrality-based Caching for Named Data Networking in the IoT. 2024 IEEE 25th International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM), 259–268. doi:10.1109/wowmom60985.2024.00049.

Amadeo, M., Ruggeri, G., Campolo, C., & Molinaro, A. (2023). Content-Driven Closeness Centrality Based Caching in Softwarized Edge Networks. IEEE International Conference on Communications (ICC 2023), 3264–3269. doi:10.1109/icc45041.2023.10278928.

Koide, M., Matsumoto, N., & Matsuzawa, T. (2024). Caching Method for Information-Centric Ad Hoc Networks Based on Content Popularity and Node Centrality. Electronics (Switzerland), 13(12), 2416. doi:10.3390/electronics13122416.

Rossi, D., & Rossini, G. (2012). On sizing CCN content stores by exploiting topological information. 2012 Proceedings IEEE INFOCOM Workshops, 280–285. doi:10.1109/infcomw.2012.6193506.

Khan, J. A., Westphal, C., Garcia-Luna-Aceves, J. J., & Ghamri-Doudane, Y. (2018). NICE. Proceedings of the 5th ACM Conference on Information-Centric Networking, 31–42. doi:10.1145/3267955.3267965.

Luo, X., & An, Y. (2017). Neighbor cooperation based in-network caching for content-centric networking. KSII Transactions on Internet and Information Systems, 11(5), 2398–2415. doi:10.3837/tiis.2017.05.005.

An, Y., & Luo, X. (2018). An In-Network Caching Scheme Based on Energy Efficiency for Content-Centric Networks. IEEE Access, 6, 20184–20194. doi:10.1109/ACCESS.2018.2823722.

Zheng, Q., Kan, Y., Chen, J., Wang, S., & Tian, H. (2019). A Cache Replication Strategy Based on Betweenness and Edge Popularity in Named Data Networking. ICC 2019 - 2019 IEEE International Conference on Communications (ICC), 1–7. doi:10.1109/icc.2019.8761900.

Delvadia, K., Dutta, N., & Ghinea, G. (2019). An Efficient Routing Strategy for Information Centric Networks. 2019 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS), 1–6. doi:10.1109/ants47819.2019.9118123.

Meng, Y., Naeem, M. A., Ali, R., Zikria, Y. Bin, & Kim, S. W. (2019). DCS: Distributed caching strategy at the edge of vehicular sensor networks in information-centric networking. Sensors (Switzerland), 19(20), 4407. doi:10.3390/s19204407.

Ali, I., & Lim, H. (2021). NameCent: Name Centrality-Based Data Broadcast Mitigation in Vehicular Named Data Networks. IEEE Access, 9, 162438–162447. doi:10.1109/ACCESS.2021.3133016.

Alduayji, S., Belghith, A., Gazdar, A., & Al-Ahmadi, S. (2023). PF-EdgeCache: Popularity and freshness aware edge caching scheme for NDN/IoT networks. Pervasive and Mobile Computing, 91. doi:10.1016/j.pmcj.2023.101782.

Kumar, S., & Tiwari, R. (2022). Dynamic Partitioning and Popularity based Caching for Optimized Performance in content-centric fog networks: DPPCOP. Pervasive and Mobile Computing, 88, 101740. doi:10.1016/j.pmcj.2022.101740.

Chaudhary, P., Hubballi, N., & Kulkarni, S. G. (2022). NCache: Neighborhood Cooperative Caching in Named Data Networking. 5th International Conference on Hot Information-Centric Networking (HotICN), 36–41. doi:10.1109/hoticn57539.2022.10036203.

Silva, A., Araujo, I., Linder, N., & Klautau, A. (2019). Name Popularity Algorithm: A Cache Replacement Strategy for NDN Networks. Journal of Communication and Information Systems, 34(2), 206–214. doi:10.14209/jcis.2019.22.

Syambas, N. R., Situmorang, H., & Putra, M. A. P. (2019). Least Recently Frequently Used Replacement Policy in Named Data Network. IEEE 5th International Conference on Wireless and Telematics (ICWT), 1–4. doi:10.1109/icwt47785.2019.8978218.

Langville, A. N., & Meyer, C. D. (2005). A survey of eigenvector methods for web information retrieval. SIAM Review, 47(1), 135–161. doi:10.1137/S0036144503424786.

Acceptance Rate:	21%
Review Speed:	74 days
Issue Per Year:	6
Number of Volumes:	7
Number of Issues:	44
Number of Articles:	493
Number of Reviewers:	1187
Number of Contributors:	1394
Contributing Countries:	83
No. of WoS Citations:	2609
No. of Scopus Citations:	2936
No. of Google Citations:	4161
Google h-index:	29
Google i10-index:	126
Abstract Views:	681,807
PDF Download:	492,524

CacheCraft: A Topology-Aware PageRank Centrality Algorithm for Cache Optimization in Named Data Networking

Authors

Downloads

Downloads

Login

submission

Publisher & Affiliated Societies

Indexing & Abstracting

SidebarMenu

IndexedBy

Indexing and Abstracting

twitter

Social Media

Analytics

Analytics

Information

Most Cited Articles

Impediments of Green Finance Adoption System: Linking Economy and Environment

Thermal Regeneration and Reuse of Carbon and Glass Fibers from Waste Composites

Optical and Structural Characterization of Bi2FexNbO7 Nanoparticles for Environmental Applications

Digital Transformation: Opportunities and Challenges for Leaders in the Emerging Countries in Response to Covid-19 Pandemic

Address

Contact Info:

CacheCraft: A Topology-Aware PageRank Centrality Algorithm for Cache Optimization in Named Data Networking

Authors

Downloads

Downloads

Login

submission

Publisher & Affiliated Societies

Indexing & Abstracting

SidebarMenu

social

Journal Imprint

Journal Metrics

IndexedBy

Indexing and Abstracting

twitter

Social Media

Analytics

Analytics

Information

Most Cited Articles

Impediments of Green Finance Adoption System: Linking Economy and Environment

Thermal Regeneration and Reuse of Carbon and Glass Fibers from Waste Composites

Optical and Structural Characterization of Bi2FexNbO7 Nanoparticles for Environmental Applications

Digital Transformation: Opportunities and Challenges for Leaders in the Emerging Countries in Response to Covid-19 Pandemic