A Heuristic Transferring Strategy for Heterogeneous-Cached ICN
The in-network caching is a considerably significant feature of Information-Centric Networking (ICN), especially the heterogeneous-cached ICN has been widely investigated since it accords with the actual network deployment. For the heterogeneous-cached ICN, although there have been many proposals to...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2020-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9079839/ |
| _version_ | 1818854813476061184 |
|---|---|
| author | Yulan Zhao Jianhui Lv |
| author_facet | Yulan Zhao Jianhui Lv |
| author_sort | Yulan Zhao |
| collection | DOAJ |
| description | The in-network caching is a considerably significant feature of Information-Centric Networking (ICN), especially the heterogeneous-cached ICN has been widely investigated since it accords with the actual network deployment. For the heterogeneous-cached ICN, although there have been many proposals to improve network performance, it is very difficult for these approaches to reach the optimal network performance with multiple metrics consideration. Therefore, in this paper, we propose a heuristic transferring strategy which selects some Content Routers (CRs) and combines them to facilitate the optimal network performance under a constrained total cache budget. At first, we quantify energy consumption, CR load, cache hit ratio and throughput, because the optimal network performance depends on four objects, i.e., minimizing energy consumption and CRs load as well as maximizing cache hit ratio and throughput. Then, based on the given network constraints and objects, we convert the CR transferring problem into 0-1 Knapsack Problem (KP01). Finally, in order to effectively obtain the optimal solution, we propose a heuristic approach based on Ant Colony Optimization (ACO) and expectation efficiency to solve KP01. The simulation is driven by the real YouTube dataset from campus network measurement over GTS and Deltacom topologies, and the experimental results demonstrate that the proposed strategy is more efficient compared to three baselines. |
| first_indexed | 2024-12-19T07:58:40Z |
| format | Article |
| id | doaj.art-379c7ea0ed2a4d9297810eba0a999837 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-19T07:58:40Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-379c7ea0ed2a4d9297810eba0a9998372022-12-21T20:29:55ZengIEEEIEEE Access2169-35362020-01-018824218243110.1109/ACCESS.2020.29909009079839A Heuristic Transferring Strategy for Heterogeneous-Cached ICNYulan Zhao0Jianhui Lv1https://orcid.org/0000-0003-0884-6601School of Electrical and Information Engineering, Jilin Agricultural Science and Technology University, Jilin, ChinaInternational Graduate School at Shenzhen, Tsinghua University, Shenzhen, ChinaThe in-network caching is a considerably significant feature of Information-Centric Networking (ICN), especially the heterogeneous-cached ICN has been widely investigated since it accords with the actual network deployment. For the heterogeneous-cached ICN, although there have been many proposals to improve network performance, it is very difficult for these approaches to reach the optimal network performance with multiple metrics consideration. Therefore, in this paper, we propose a heuristic transferring strategy which selects some Content Routers (CRs) and combines them to facilitate the optimal network performance under a constrained total cache budget. At first, we quantify energy consumption, CR load, cache hit ratio and throughput, because the optimal network performance depends on four objects, i.e., minimizing energy consumption and CRs load as well as maximizing cache hit ratio and throughput. Then, based on the given network constraints and objects, we convert the CR transferring problem into 0-1 Knapsack Problem (KP01). Finally, in order to effectively obtain the optimal solution, we propose a heuristic approach based on Ant Colony Optimization (ACO) and expectation efficiency to solve KP01. The simulation is driven by the real YouTube dataset from campus network measurement over GTS and Deltacom topologies, and the experimental results demonstrate that the proposed strategy is more efficient compared to three baselines.https://ieeexplore.ieee.org/document/9079839/Heterogeneous ICNtransferring strategyKP01ACOexpectation efficiency |
| spellingShingle | Yulan Zhao Jianhui Lv A Heuristic Transferring Strategy for Heterogeneous-Cached ICN IEEE Access Heterogeneous ICN transferring strategy KP01 ACO expectation efficiency |
| title | A Heuristic Transferring Strategy for Heterogeneous-Cached ICN |
| title_full | A Heuristic Transferring Strategy for Heterogeneous-Cached ICN |
| title_fullStr | A Heuristic Transferring Strategy for Heterogeneous-Cached ICN |
| title_full_unstemmed | A Heuristic Transferring Strategy for Heterogeneous-Cached ICN |
| title_short | A Heuristic Transferring Strategy for Heterogeneous-Cached ICN |
| title_sort | heuristic transferring strategy for heterogeneous cached icn |
| topic | Heterogeneous ICN transferring strategy KP01 ACO expectation efficiency |
| url | https://ieeexplore.ieee.org/document/9079839/ |
| work_keys_str_mv | AT yulanzhao aheuristictransferringstrategyforheterogeneouscachedicn AT jianhuilv aheuristictransferringstrategyforheterogeneouscachedicn AT yulanzhao heuristictransferringstrategyforheterogeneouscachedicn AT jianhuilv heuristictransferringstrategyforheterogeneouscachedicn |