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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Xiaoyan Hu Jian Gong Guang Cheng Chengyu Fan |
| Copyright Year | 2015 |
| Description | Author affiliation: Comput. Sci. Dept., Colorado State Univ., Fort Collins, CO, USA (Chengyu Fan) || Sch. of Comput. Sci. & Eng., Southeast Univ., Nanjing, China (Xiaoyan Hu; Jian Gong; Guang Cheng) |
| Abstract | As a distinctive feature of Information Centric Networking (ICN), in-network caching plays a fundamental role on system performance. The line-speed requirement of in-network caching invalidates the employ of complex collaborative caching schemes. The cache management of in-network caching includes three components - cache placement, cache replacement and cache location. Coupling the three pieces would make more efficient use of in-network caches, but existing in-network caching schemes consider only one or two of the three pieces. This work enhances in-network caching with a low complexity cache placement scheme that takes into account content popularity, hop reduction gains, cache space contention and replacement penalty and couples with cache replacement and location, here dubbed PRL (coupling cache Placement, Replacement and Location). PRL keeps data chunks that are more popular and farther away to fetch at an en-route router with less cache space contention. And PRL locates cached copies so as to serve a higher proportion of requests from in-network caches. Our preliminary simulation results suggest that PRL increases cache hit ratio and reduces the average hop count traversed by users' requests and the caching operations at routers as compared to existing representative innetwork caching schemes. |
| Starting Page | 5672 |
| Ending Page | 5678 |
| File Size | 1493756 |
| Page Count | 7 |
| File Format | |
| e-ISBN | 9781467364324 |
| DOI | 10.1109/ICC.2015.7249226 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2015-06-08 |
| Publisher Place | UK |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Artificial neural networks Benchmark testing |
| Content Type | Text |
| Resource Type | Article |
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