1 CCN CCN(Content-Centric-Network) [1] PUR- SUIT [2]DONA(Data-Oriented-Network Architecture) [3] 4WARD [4]PSIRP [5] Energy Efficient and Enhanced-type

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1 THE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGINEERS TECHNICAL REPORT OF IEICE miyazaki@yamanaka.ics.keio.ac.jp Content Centric Network(CCN) CCN CCN LRU(Least-Recently-Used)Prob-Cache(Probablistic In-Network Caching) LRU Prob-Cache 2 (CCN) A Cash Policy Taking into account User s Location into cache lifetimes in Content Centric Network Takahiro MIYAZAKI, Hidetoshi TAKESHITA, Satoru OKAMOTO, and Naoaki YAMANAKA School of Science for Open and Environmental Systems, Graduate School of Science and Technology, Keio University Hiyoshi, Kohoku, Yokohama, Japan miyazaki@yamanaka.ics.keio.ac.jp Abstract Against the problem that contents request occupies the majority of today s Internet traffic, content centric network(ccn) is proposed. In CCN, user request contents by content s name and relay node cache contents. Because of it s characteristic, content search time fluctuates greatly on whether the user s request content are cached at the relay node as soon as possible. So, cache methods that search the content as soon as possible is needed. In this paper, we introduced some cache methods and revealed its advantages and problems. Then, we proposed cache method that reflect the user location into survival time in order to solve the problems of the conventional method. Compared to the conventional method, simulation shows the proposed method improved cache hit ratio and the number of hops the content can be acquired. Key words Future Network, Content-Centric Network, Cache Policy 1. (Information Centric Network) 1

2 1 CCN CCN(Content-Centric-Network) [1] PUR- SUIT [2]DONA(Data-Oriented-Network Architecture) [3] 4WARD [4]PSIRP [5] Energy Efficient and Enhanced-type Data-centric Network (E 3 -DCN) [7] DCON Information Centric Network Information Centric Network Information Centric Network Content-Centric Network 2 Content Centric Network Content Centric Network(CCN) Content Centric Network CCN 2 ID CCN 1 1 CCN Interest Data 2 Interest Interest ID 1 CCN A Interest Interest CS(Content Store) CS CCN 1 CCN A Data Data A CCN A FIB(Fowarding Interest Base) Interest 1 CCN B CCN A PIT(Pending Interest Table) Interest PIT Interest CCN B Interest CCN A CCN B CS CCN B CCN A CCN A PIT Interest FIB Interest CCN Interest PIT CCN Interest Interest CCN CCN CCN 3. CCN CCN LRU(Least- Recently-Used) CCN LRU Prob-Cache(Probablistic In-Networking Caching) 2

3 2 LRU 3 Prob 3. 1 LRU(Least-Recently-Used) LRU CCN LRU 3. 2 LRU LRU.. LRU CCN AB B CCN B B 2 A CCN A CCN B CCN LRU 3. 3 Prob-Cache (Probablistic In-Networking Caching) Prob-Cache [6] 3 Prob 3 LRU. AD. CCN A A A. CCN D AD AD LRU CCN A CCN D. Prob-Cache.Prob-Cache. ctime Since Inception (TSI) xtime Since Birth (TSB) CacheWeight() CacheW eight() = x c (1) 3 A CCN A CacheWeight(A) c=4x=4 CacheWeight(A)=1. A D c=4x=1 CacheWeight(D)=0.25 CacheWeight. TimesIn(). N i i T tw [8] T imesin() = c (x 1) N i i=1 N x c (x 1) i=1 N i T tw N x (2) [8] [8] Prob- Cache(). P robcache() = T imesin() CacheW eight()(3) Prob-Cache Prob-Cache LRU LRU. 3

4 CCN.. Prob-Cache LRU. Prob-Cache LRU. 4. Prob-Cache... Prob-Cache (1). 1 CCN Data CCN 2 CCN Data CCN 1 4 CCN ABCD Interest. Interest. CCN. CacheWeight(). CCN. LRUProbCache CCN.. 5 CCN. 5 6 (1).

4 4 CCN.. Prob-Cache LRU. Prob-Cache LRU. 4. Prob-Cache... Prob-Cache (1). 1 CCN Data CCN 2 CCN Data CCN 1 4 CCN ABCD Interest. Interest. CCN. CacheWeight(). CCN. LRUProbCache CCN.. 5 CCN. 5 6 (1).CCN A 100.CCN C 75 (1 0.75) = 0.25x (4) 0.25x.CCN CCCN D. LRU CCN Data CCN 2. 1 Data 2 Interest (1)(2). 2 (1). 6 CCN C CCN C. LRU Prob-Cache (CCN ). (2) CCN Prob- Cache CCN 4

5 7 (2) 10 8 CCN 4 4 CCN CCN CCN Prob-Cache 8 A CCN A CCN B 50 CCN B 50 CCN B B B CCN B Prob-Cache 9 Prob-Cache 100 LRU 33 LRU 33 LRU LRU 23 LRU LRU33 LRU 33 LRU Omnet , /1 Zipf (=1) 5. 1 Prob-Cache 10 CCN 10 CCN Prob-Cache 30 LRUProb-Cache 5

6 Interest CCN = (5) CCN 11 CCN () CCN Prob-CacheLRU 15 CCN Prob-Cache 6, Interest CCN 11 CCN 11 CCN LRUProb-Cache 6. CCN LRUProb-Cache NICT [1] V.Jacobson, D.KSmetters, J.D.Thornton, M.F.Plass, N.H.Briggs, and R.L.Braynard, Networking Named Content in Proceeding of ACM CoNEXT 2009, pp.1-12,dec [2] N. Fotiou, P. Nikander, D. Trossen, and G. C. Polyzos,Developing information networking further: From PSIRP to PURSUIT,in Proceedings of BROADNETS 2010, pp. 113, Oct [3] T. Koponen et al., A Data-Oriented (and Beyond) Network Architecture, Proc. SIGCOMM 07, Kyoto, Japan, Aug. 2731, [4] N. Niebert, S. Baucke, I. El-Khayat, M. Johnsson, B. Ohlman, H. Abramowicz, K. Wuenstel, H. Woesner, J. Quittek, and L. M. Correia, The way 4WARD to the creation of a future internet, in Proceedings of PIMRC 2008, pp. 15, Sept [5] P. Jokela, A. Zahemszky, C. E. Rothenberg, S. Arianfar, and P. Nikander, LIPSIN: line speed publish/subscribe internetworking, in Proceedings of ACM SIGCOMM 2009, pp , Aug [6] Psaras, Ioannis, Wei Koong Chai, and George Pavlou. Probabilistic in-network caching for information-centric networks. Proceedings of the second edition of the ICN workshop on Information-centric networking. ACM, [7],,,,, E3-DCN,, No. B-6-136, March [8] A. Anand, C. Muthukrishnan, A. Akella, and R. Ramjee, Redundancy in network trafc: ndings and implications, in SIGMETRICS 09. 6

IPSJ SIG Technical Report Vol.2012-CG-148 No /8/29 3DCG 1,a) On rigid body animation taking into account the 3D computer graphics came

IPSJ SIG Technical Report Vol.2012-CG-148 No /8/29 3DCG 1,a) On rigid body animation taking into account the 3D computer graphics came 3DCG 1,a) 2 2 2 2 3 On rigid body animation taking into account the 3D computer graphics camera viewpoint Abstract: In using computer graphics for making games or motion pictures, physics simulation is