Academic literature on the topic 'Collaborative Caching'
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Journal articles on the topic "Collaborative Caching"
Hu, Qing, Chengming Li, Touhidul Hasan, Chengjun Li, and Qingshan Jiang. "A collaborative caching strategy in contentcentric networking." MATEC Web of Conferences 189 (2018): 03018. http://dx.doi.org/10.1051/matecconf/201818903018.
Full textQin, Yana, Danye Wu, Zhiwei Xu, Jie Tian, and Yujun Zhang. "Adaptive In-Network Collaborative Caching for Enhanced Ensemble Deep Learning at Edge." Mathematical Problems in Engineering 2021 (September 25, 2021): 1–14. http://dx.doi.org/10.1155/2021/9285802.
Full textElfaki, M. A., M. Abdellatief, A. A. Alwan, and A. Wahaballa. "A Literature Review on Collaborative Caching Techniques in MANETs: Issues and Methods used in Serving Queries." Engineering, Technology & Applied Science Research 9, no. 5 (October 9, 2019): 4729–34. http://dx.doi.org/10.48084/etasr.2962.
Full textTang, Qinqin, Renchao Xie, Tao Huang, and Yunjie Liu. "Hierarchical collaborative caching in 5G networks." IET Communications 12, no. 18 (November 20, 2018): 2357–65. http://dx.doi.org/10.1049/iet-com.2018.5553.
Full textGu, Xiaoming, and Chen Ding. "A generalized theory of collaborative caching." ACM SIGPLAN Notices 47, no. 11 (January 8, 2013): 109–20. http://dx.doi.org/10.1145/2426642.2259012.
Full textGao, Guoqiang, and Ruixuan Li. "Collaborative Caching in P2P Streaming Networks." Journal of Network and Systems Management 27, no. 3 (January 4, 2019): 815–36. http://dx.doi.org/10.1007/s10922-018-09485-6.
Full textXia, Xiaoyu, Feifei Chen, Qiang He, John Grundy, Mohamed Abdelrazek, and Hai Jin. "Online Collaborative Data Caching in Edge Computing." IEEE Transactions on Parallel and Distributed Systems 32, no. 2 (February 1, 2021): 281–94. http://dx.doi.org/10.1109/tpds.2020.3016344.
Full textYang, Jiong, Wei Wang, and Richard Muntz. "Collaborative Web caching based on proxy affinities." ACM SIGMETRICS Performance Evaluation Review 28, no. 1 (June 2000): 78–89. http://dx.doi.org/10.1145/345063.339360.
Full textWu, Kun-Lung, and Philip S. Yu. "Latency-sensitive hashing for collaborative Web caching." Computer Networks 33, no. 1-6 (June 2000): 633–44. http://dx.doi.org/10.1016/s1389-1286(00)00042-6.
Full textAkon, Mursalin, Towhidul Islam, Xuemin Shen, and Ajit Singh. "SPACE: A lightweight collaborative caching for clusters." Peer-to-Peer Networking and Applications 3, no. 2 (May 13, 2009): 83–99. http://dx.doi.org/10.1007/s12083-009-0047-5.
Full textDissertations / Theses on the topic "Collaborative Caching"
Liu, Wei. "Distributed Collaborative Caching for WWW." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape3/PQDD_0014/MQ53180.pdf.
Full textMahdavi, Mehregan Computer Science & Engineering Faculty of Engineering UNSW. "Caching dynamic data for web applications." Awarded by:University of New South Wales. Computer Science and Engineering, 2006. http://handle.unsw.edu.au/1959.4/32316.
Full textCardenas, Baron Yonni Brunie Lionel Pierson Jean-Marc. "Grid caching specification and implementation of collaborative cache services for grid computing /." Villeurbanne : Doc'INSA, 2008. http://docinsa.insa-lyon.fr/these/pont.php?id=cardenas_baron.
Full textCardenas, Baron Yonny. "Grid caching : specification and implementation of collaborative cache services for grid computing." Lyon, INSA, 2007. http://theses.insa-lyon.fr/publication/2007ISAL0107/these.pdf.
Full textCette thèse propose une approche de la conception et de l'implémentation de systèmes de cache collaboratif dans les grilles de données. Notre approche permet la composition et l'évaluation des fonctions d‘un système de cache collaboratif de haut niveau de façon flexible. Notre proposition est basée sur un modèle multicouche qui définit les fonctions principales d'un système de cache collaboratif pour les grilles. Ce modèle et la spécification fournie sont utilisés pour construire une infrastructure logicielle flexible et générique pour l'opération et le contrôle du cache collaboratif. Cette infrastructure est composée d'un groupe d’éléments autonomes de cache appelés "Grid Cache Services" (GCS). Le GCS est un administrateur local de moyens de stockage et de données temporaires. Nous étudions une possible configuration d’un groupe de GCS qui constitue un système basique d'administration de données temporaires appelé "Temporal Storage Service" (TSS)
Zhou, Yifan. "Clustering Nature of Base Stations and Traffic Demands in Cellular Networks and the Corresponding Caching and Multicast Strategies." Thesis, CentraleSupélec, 2018. http://www.theses.fr/2018CSUP0008.
Full textTraditional cellular networks have evolved from the first generation of analog communications to the current fourth generation of digital communications where iteratively enhanced physical layer technologies have greatly increased the network capacity. According to Shannon's theory, the technical gains brought by physical layer has gradually become saturated, which cannot match the rapid increase of user traffic demand in current mobile internet era, thus calls for another path of evolution, i.e., digging into the traffic demand of mobile users. In recent years, the academic communities have begun to use the real data to analyze the infrastructure deployment of wireless networks and the traffic demand of mobile users, in order to make benefits from the underlying statistical patterns. At the same time, along with the recent rise of machine learning technics, data-driven service is considered as the next economic growth point. Thus the industry is putting more and more attention on data accumulation and knowledge mining related services and telecommunication operators are coming to realize the increasing importance of the recorded data from their own networks. Therefore, the real-data-driven technology advancement is considered as a promising direction for the next evolution of cellular networks.In this thesis, we firstly gave a comprehensive review of the state-of-the-art real data measurements in Chapter 2 which not only sheds light on the importance of real data analysis, but also paves way for its reasonable usage to improve the service performance of cellular networks. From the survey, we concluded that there exhibits a periodic pattern for the temporal traffic assumption of large coverage area in cellular networks, while for single cell, a heavy-tailed distribution is widespread across the temporal and spatial characterization. Furthermore, this imbalance phenomenon emerges more significantly in the call duration, request arrivals and content preference of mobile users.Then, based on a large amount of real data collected from on-operating cellular networks, we conducted a large-scale identification on spatial modeling of base stations (BSs) in Chapter 3. According to the fitting results, we verified the inaccuracy of Poisson distribution for BS locations, and uncovered the clustering nature of BS deployment in cellular networks. However, although typical clustering models have improved the modeling accuracy but are still not qualified to accurately reproduce the practical BSs deployment, which leads to the spatial density characterization of BS.In Chapter 4, we tried to characterize the density of BS deployment and traffic demand, in both spatial domain and temporal dimensions. In accordance with the heavy-tailed phenomenons in Chapter 2, we found that the α-Stable distribution is the most accurate model for the spatial densities of BSs and traffic consumption, between which a linear dependence is revealed through real data examination. Moreover, the accuracies of power-law and lognormal distributions for the packet length and inter-arrival time of user requests are verified, respectively, which convincingly leads to the α-Stable distribution of temporally aggregated traffic volume on BS level.[...]
Lorrillere, Maxime. "Caches collaboratifs noyau adaptés aux environnements virtualisés." Thesis, Paris 6, 2016. http://www.theses.fr/2016PA066036/document.
Full textWith the advent of cloud architectures, virtualization has become a key mechanism for ensuring isolation and flexibility. However, a drawback of using virtual machines (VMs) is the fragmentation of physical resources. As operating systems leverage free memory for I/O caching, memory fragmentation is particularly problematic for I/O-intensive applications, which suffer a significant performance drop. In this context, providing the ability to dynamically adjust the resources allocated among the VMs is a primary concern.To address this issue, this thesis proposes a distributed cache mechanism called Puma. Puma pools together the free memory left unused by VMs: it enables a VM to entrust clean page-cache pages to other VMs. Puma extends the Linux kernel page cache, and thus remains transparent, to both applications and the rest of the operating system. Puma adjusts itself dynamically to the caching activity of a VM, which Puma evaluates by means of metrics derived from existing Linux kernel memory management mechanisms. Our experiments show that Puma significantly improves the performance of I/O-intensive applications and that it adapts well to dynamically changing conditions
Wang, Yu-Hsiang, and 王煜祥. "Collaborative Computation Offloading in Mobile Edge Computing with Caching." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/f35asd.
Full text國立臺灣科技大學
資訊管理系
107
Mobile Edge Computing (MEC) is one of the main functions of the next generation mobile network 5G. The 5G mobile base station will be equipped with edge server with computing power. Its main purpose is to provide application services with ultra-low latency. At that time, some tasks that require low latency will not be transmitted to the cloud server for computation but will be transmitted to the edge server that is closer. The existing paper is mainly based on two-tier, and when the task is offloading, only the gain of the individual is considered. However, the same task may happen repeatedly on different UEs. If UE only considers its own gain when offloading that may cause the same task to repeat the computation on different UEs, resulting in longer delays for the whole system. For the above reasons, we propose a Gain-based Collaborative Offloading (GCO) in cache-enabled collaborative MEC. When the task is offloaded to the edge server, the edge server can cache the task result. When the same task is accessed, the cache can be directly returned, reducing the delay. Our gain concept is to evaluate whether the offload to the edge server can reduce the number of repeated computations of the same task to reduce the average delay. During the offloading, the GCO will consider the gain of the whole system determines whether to offload or not. If UEs covered by the same edge server or the neighboring edge server with high access probability to access the same task, offload to the edge server can reduce the same computation, thus reducing overall delay. Simulation results show that GCO can reduce the delay by 35% compared to the traditional two-tier offloading decision.
Liu, Chao-Shiu, and 劉兆修. "A Collaborative Micro-Caching Mechanism for Supporting the Location-Aware Information Service in MANET." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/82139315691482637245.
Full text國立屏東科技大學
資訊管理系
92
Continuous progress in the technologies of wireless communications (ex: GSM, GPRS, PHS and 3G) and satellite position (ex: GPS), mobile device’s support are more widespread. We can use handheld device to get we need’s information via wireless communications. One of the promising emerging applications is the location-aware mobile information service. A user gets his/her present location through a global positioning system (GPS) and feeds it to the backend information server via GPRS or CDMA. Server uses that location information to search the information around that area and deliver it to the user. However, multimedia information (ex: Sound and Movie) use pervasive, wide area network’s bandwidth and quality at this time still can not satisfied with a large number of users connection’s request at the same time. Therefore, in this thesis, we propose a Collaborative Micro Caching Mechanism (CC) for the mobile entity who uses MANET to share files by myself without request the same data with server. The basic principle: users in the same area can use MANET to connect and share files by myself with each other. When user still need other data then enables to start to download data with database. This can increase chance to share information and reduce the wide area network’s traffic. We discuss with related work and suggest some solution, and development a simple system at the time. The experiments results confirm that the CC Mechanism can effectively avoid the information exceeding waiting time, and information’s transmission quantity also can to be promoted. Hence, it helps the information availability of location-aware mobile information service.
Book chapters on the topic "Collaborative Caching"
Gao, Guoqiang, and Ruixuan Li. "Collaborative Caching in P2P Streaming Systems." In Web Information Systems and Applications, 115–22. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-02934-0_11.
Full textLefèvre, Laurent, Jean-Marc Pierson, and SidAli Guebli. "Deployment of Collaborative Web Caching with Active Networks." In Active Networks, 80–91. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24715-9_8.
Full textO’Neill, John Paul, and Jonathan Dukes. "On-Demand Multicast Streaming Using Collaborative Prefix Caching." In Lecture Notes in Computer Science, 27–40. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04994-1_3.
Full textWang, XiaoYu, WeeSiong Ng, BengChin Ooi, Kian-Lee Tan, and AoYing Zhou. "BuddyWeb: A P2P-Based Collaborative Web Caching System." In Lecture Notes in Computer Science, 247–51. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45745-3_22.
Full textGuo, Shuo, Haiyong Xie, and Guangyu Shi. "Collaborative Forwarding and Caching in Content Centric Networks." In NETWORKING 2012, 41–55. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-30045-5_4.
Full textJiang, Hao, Hehe Huang, Ying Jiang, Yuan Wang, Yuanyuan Zeng, and Chen Zhou. "Collective Behavior Aware Collaborative Caching for Mobile Edge Computing." In Lecture Notes in Computer Science, 172–81. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-05755-8_18.
Full textJones, Andrew, and Robert Simon. "A Privacy-Preserving Collaborative Caching Approach in Information-Centric Networking." In Lecture Notes in Computer Science, 133–50. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-64348-5_11.
Full textQian, Weining, Linhao Xu, Shuigeng Zhou, and Aoying Zhou. "CoCache: Query Processing Based on Collaborative Caching in P2P Systems." In Database Systems for Advanced Applications, 498–510. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11408079_44.
Full textWang, Ruichao, Jizhao Lu, Yongjie Li, Xingyu Chen, and Shaoyong Guo. "Deep Q-Learning Based Collaborative Caching in Mobile Edge Network." In Advances in Intelligent Systems and Computing, 327–34. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-8462-6_38.
Full textRen, Jianji, Tingting Hou, and Shuai Zheng. "Collaborative Mobile Edge Caching Strategy Based on Deep Reinforcement Learning." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 3–15. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-63941-9_1.
Full textConference papers on the topic "Collaborative Caching"
Gu, Xiaoming, and Chen Ding. "A generalized theory of collaborative caching." In the 2012 international symposium. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2258996.2259012.
Full textKhreishah, Abdallah, and Jacob Chakareski. "Collaborative caching for multicell-coordinated systems." In IEEE INFOCOM 2015 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). IEEE, 2015. http://dx.doi.org/10.1109/infcomw.2015.7179394.
Full textZhong, Nan, Hongmei Zhang, and Xiangli Zhang. "Collaborative Partition Caching with Local Popularity." In 2020 IEEE 20th International Conference on Communication Technology (ICCT). IEEE, 2020. http://dx.doi.org/10.1109/icct50939.2020.9295921.
Full textGu, Xiaoming. "Collaborative Caching for Unknown Cache Sizes." In 2011 International Conference on Parallel Architectures and Compilation Techniques (PACT). IEEE, 2011. http://dx.doi.org/10.1109/pact.2011.50.
Full textYao, Chao, Changkun Jiang, Zun Liu, Jie Chen, and Jianqiang Li. "Optimal Capacity Allocation and Caching Strategy for Multi-UAV Collaborative Edge Caching." In 2021 6th IEEE International Conference on Advanced Robotics and Mechatronics (ICARM). IEEE, 2021. http://dx.doi.org/10.1109/icarm52023.2021.9536069.
Full textGu, Xiaoming. "Minor memory references matter in collaborative caching." In the 2011 ACM SIGPLAN Workshop. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/1988915.1988927.
Full textYang, Yi-Jen, Ming-Hsun Yang, Y. W. Peter Hong, and Jwo-Yuh Wu. "Collaborative Sensor Caching via Sequential Compressed Sensing." In ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2019. http://dx.doi.org/10.1109/icassp.2019.8683070.
Full textYang, Jiong, Wei Wang, and Richard Muntz. "Collaborative Web caching based on proxy affinities." In the 2000 ACM SIGMETRICS international conference. New York, New York, USA: ACM Press, 2000. http://dx.doi.org/10.1145/339331.339360.
Full textMiyoshi, Yuta, Takuya Wada, and Kouji Hirata. "Collaborative in-network caching for multi-path routing." In 2017 IEEE International Conference on Consumer Electronics - Taiwan (ICCE-TW). IEEE, 2017. http://dx.doi.org/10.1109/icce-china.2017.7991075.
Full textLi, Xiuhua, Peiran Wu, Xiaofei Wang, Keqiu Li, Zhu Han, and Victor C. M. Leung. "Collaborative hierarchical caching in cloud radio access networks." In 2017 IEEE Conference on Computer Communications: Workshops (INFOCOM WKSHPS). IEEE, 2017. http://dx.doi.org/10.1109/infcomw.2017.8116420.
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