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Journal articles on the topic 'Collaborative Caching'

Author: Grafiati

Published: 4 June 2021

Last updated: 8 February 2022

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1

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.

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Content-centric Networking (CCN) is one of the most promising network architectures for the future Internet. In-network caching is an attractive feature of CCN, however, the existing research does not consider the off-path nodes, or gives a large communication overhead for cooperation, which makes the caching utilization lower, and hard to achieve comprehensive performance optimization. To reduce the data redundancy and improve the caching utilization, we propose a Regional Hashing Collaborative Caching Strategy (RHCCS). According to calculate the importance of nodes in the network topology, we divide the network into the core area and edge area. In core area, we select the relevant nodes for cooperation, store the block in the off-path nodes with the hashing algorithm, and add a new table in original data structures for routing in the collaborative areas. As for edge area, we deploy the on-path reversion scheme. By simulating in ndnSIM and comparing with the basic caching strategy in CCN, experimental results indicate that the RHCCS can effectively reduce data redundancy, routing hops, requesting delay, and significantly increase the hit rate.

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2

Qin, 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.

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To enhance the quality and speed of data processing and protect the privacy and security of the data, edge computing has been extensively applied to support data-intensive intelligent processing services at edge. Among these data-intensive services, ensemble learning-based services can, in natural, leverage the distributed computation and storage resources at edge devices to achieve efficient data collection, processing, and analysis. Collaborative caching has been applied in edge computing to support services close to the data source, in order to take the limited resources at edge devices to support high-performance ensemble learning solutions. To achieve this goal, we propose an adaptive in-network collaborative caching scheme for ensemble learning at edge. First, an efficient data representation structure is proposed to record cached data among different nodes. In addition, we design a collaboration scheme to facilitate edge nodes to cache valuable data for local ensemble learning, by scheduling local caching according to a summarization of data representations from different edge nodes. Our extensive simulations demonstrate the high performance of the proposed collaborative caching scheme, which significantly reduces the learning latency and the transmission overhead.

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3

Elfaki, 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.

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Collaborative cache management in Mobile Ad Hoc Networks (MANETs) environment is considered as an efficient technique to increase data accessibility and availability, by sharing and coordination among mobile nodes. Due to nodes’ mobility, limited battery power and insufficient bandwidth, researchers addressed these challenges by developing many different collaborative caching schemes. The objective of this paper is to review various collaborative caching techniques in MANETs. Collaborative caching techniques are classified by methods used in serving queries, such as: hop-by-hop discovering, broadcasting messages, flooding, and query service differentiation. This review reveals that techniques utilizing hop-by-hop methods have better performance compared to others, especially techniques using additional strategies.

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Tang, 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.

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5

Gu, 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.

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6

Gao, 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.

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7

Xia, 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.

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8

Yang, 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.

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9

Wu, 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.

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10

Akon, 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.

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11

Donen, Tsuyoshi, Shingo Otsubo, Ryo Nishide, Ian Piumarta, and Hideyuki Takada. "Network traffic reduction mechanism for collaborative Web activities." International Journal of Web Information Systems 13, no. 2 (June 19, 2017): 106–13. http://dx.doi.org/10.1108/ijwis-12-2016-0075.

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Purpose The purpose of this study is to reduce internet traffic when performing collaborative Web search. Mobile terminals are now in widespread use and people are increasingly using them for collaborative Web search to achieve a common goal. When performing such searches, the authors want to reduce internet traffic as much as possible, for example, to avoid bandwidth throttling that occurs when data usage exceeds a certain quota. Design/methodology/approach To reduce internet traffic, the authors use a proxy system based on the peer cache mechanism. The proxy shares Web content stored on mobile terminals participating in an ad hoc Bluetooth network, focusing on content that is accessed multiple times from different terminals. Evaluation of the proxy’s effectiveness was measured using experiments designed to replicate realistic usage scenarios. Findings Experimental results show that the proxy reduces internet traffic by approximately 20 per cent when four people collaboratively search the Web to find good restaurants for a social event. Originality/value Unlike previous work on co-operative Web proxies, the authors study a form of collaborative Web caching between mobile devices within an ad hoc Bluetooth network created specifically for the purpose of sharing cached content, acting orthogonally to (and independently of) traditional hierarchical Web caching.

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12

Ahmed Elfaki, Mohamed, Hamidah Ibrahim, Ali Mamat, Mohamed Othman, and Haidar Safa. "Collaborative caching priority for processing requests in MANETs." Journal of Network and Computer Applications 40 (April 2014): 85–96. http://dx.doi.org/10.1016/j.jnca.2013.08.013.

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13

Wu, Dan, Liang Zhou, Yueming Cai, and Yi Qian. "Collaborative Caching and Matching for D2D Content Sharing." IEEE Wireless Communications 25, no. 3 (June 2018): 43–49. http://dx.doi.org/10.1109/mwc.2018.1700325.

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14

THRA, T. SUJI, PHANI KUMAR NAGARAM, and MANASA NAGARAM. "Information Caching and Prefetching Using Collaborative Caching and Prefetching Algorithm in Wireless ADHOC Networks." International Journal of Innovative Research in Science, Engineering and Technology 7, no. 2 (February 15, 2018): 1589–91. http://dx.doi.org/10.15680/ijirset.2018.0702109.

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15

Gupta, Divya, Shalli Rani, Syed Hassan Ahmed, Sahil Verma, Muhammad Fazal Ijaz, and Jana Shafi. "Edge Caching Based on Collaborative Filtering for Heterogeneous ICN-IoT Applications." Sensors 21, no. 16 (August 15, 2021): 5491. http://dx.doi.org/10.3390/s21165491.

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The substantial advancements offered by the edge computing has indicated serious evolutionary improvements for the internet of things (IoT) technology. The rigid design philosophy of the traditional network architecture limits its scope to meet future demands. However, information centric networking (ICN) is envisioned as a promising architecture to bridge the huge gaps and maintain IoT networks, mostly referred as ICN-IoT. The edge-enabled ICN-IoT architecture always demands efficient in-network caching techniques for supporting better user’s quality of experience (QoE). In this paper, we propose an enhanced ICN-IoT content caching strategy by enabling artificial intelligence (AI)-based collaborative filtering within the edge cloud to support heterogeneous IoT architecture. This collaborative filtering-based content caching strategy would intelligently cache content on edge nodes for traffic management at cloud databases. The evaluations has been conducted to check the performance of the proposed strategy over various benchmark strategies, such as LCE, LCD, CL4M, and ProbCache. The analytical results demonstrate the better performance of our proposed strategy with average gain of 15% for cache hit ratio, 12% reduction in content retrieval delay, and 28% reduced average hop count in comparison to best considered LCD. We believe that the proposed strategy will contribute an effective solution to the related studies in this domain.

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16

Elfaki. "Collaborative Caching Architecture for Continuous Query in Mobile Database." American Journal of Economics and Business Administration 3, no. 1 (January 1, 2011): 33–39. http://dx.doi.org/10.3844/ajebasp.2011.33.39.

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17

Bilal, Kashif, Emna Baccour, Aiman Erbad, Amr Mohamed, and Mohsen Guizani. "Collaborative joint caching and transcoding in mobile edge networks." Journal of Network and Computer Applications 136 (June 2019): 86–99. http://dx.doi.org/10.1016/j.jnca.2019.02.004.

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18

Dai, Jie, Fangming Liu, Bo Li, Baochun Li, and Jiangchuan Liu. "Collaborative Caching in Wireless Video Streaming Through Resource Auctions." IEEE Journal on Selected Areas in Communications 30, no. 2 (February 2012): 458–66. http://dx.doi.org/10.1109/jsac.2012.120226.

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19

Furqan, Muhammad, Cheng Zhang, Wen Yan, Abdul Shahid, Muhammad Wasim, and Yongming Huang. "A Collaborative Hotspot Caching Design for 5G Cellular Network." IEEE Access 6 (2018): 38161–70. http://dx.doi.org/10.1109/access.2018.2852278.

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20

Wang, Xiaofei, Tarik Taleb, Zhu Han, Shugong Xu, and Victor C. M. Leung. "Content-Centric Collaborative Edge Caching in 5G Mobile Internet." IEEE Wireless Communications 25, no. 3 (June 2018): 10–11. http://dx.doi.org/10.1109/mwc.2018.8403945.

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21

Wan, Zheng, and Yan Li. "Deep Reinforcement Learning-Based Collaborative Video Caching and Transcoding in Clustered and Intelligent Edge B5G Networks." Wireless Communications and Mobile Computing 2020 (December 12, 2020): 1–16. http://dx.doi.org/10.1155/2020/6684293.

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In the next-generation wireless communications system of Beyond 5G networks, video streaming services have held a surprising proportion of the whole network traffic. Furthermore, the user preference and demand towards a specific video might be different because of the heterogeneity of users’ processing capabilities and the variation of network condition. Thus, it is a complicated decision problem with high-dimensional state spaces to choose appropriate quality videos according to users’ actual network condition. To address this issue, in this paper, a Content Distribution Network and Cluster-based Mobile Edge Computing framework has been proposed to enhance the ability of caching and computing and promote the collaboration among edge severs. Then, we develop a novel deep reinforcement learning-based framework to automatically obtain the intracluster collaborative caching and transcoding decisions, which are executed based on video popularity, user requirement prediction, and abilities of edge servers. Simulation results demonstrate that the quality of video streaming service can be significantly improved by using the designed deep reinforcement learning-based algorithm with less backhaul consumption and processing costs.

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22

Song, Fei, Zheng-Yang Ai, Jun-Jie Li, Giovanni Pau, Mario Collotta, Ilsun You, and Hong-Ke Zhang. "Smart Collaborative Caching for Information-Centric IoT in Fog Computing." Sensors 17, no. 11 (November 1, 2017): 2512. http://dx.doi.org/10.3390/s17112512.

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23

Lilly Sheeba, S., and P. Yogesh. "Collaborative Clustering for Cooperative Caching in Mobile Ad Hoc Networks." Wireless Personal Communications 95, no. 2 (October 8, 2016): 1087–107. http://dx.doi.org/10.1007/s11277-016-3815-6.

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24

Zhang, Puning, Xuyuan Kang, Yuzhe Liu, and Huiping Yang. "Cooperative Willingness Aware Collaborative Caching Mechanism Towards Cellular D2D Communication." IEEE Access 6 (2018): 67046–56. http://dx.doi.org/10.1109/access.2018.2873662.

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25

Zhao, Xiaoyan, Peiyan Yuan, Haiwen li, and Shaojie Tang. "Collaborative Edge Caching in Context-Aware Device-to-Device Networks." IEEE Transactions on Vehicular Technology 67, no. 10 (October 2018): 9583–96. http://dx.doi.org/10.1109/tvt.2018.2858254.

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26

Wang, Liumeng, and Sheng Zhou. "Fractional Dynamic Caching: A Collaborative Design of Storage and Backhaul." IEEE Transactions on Vehicular Technology 69, no. 4 (April 2020): 4194–206. http://dx.doi.org/10.1109/tvt.2020.2968487.

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27

Wang, Yang, Shuibing He, Xiaopeng Fan, Chengzhong Xu, and Xian-He Sun. "On Cost-Driven Collaborative Data Caching: A New Model Approach." IEEE Transactions on Parallel and Distributed Systems 30, no. 3 (March 1, 2019): 662–76. http://dx.doi.org/10.1109/tpds.2018.2868642.

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28

Xing, Haoru, and Wei Song. "Collaborative Content Distribution With an End-to-End Caching Framework." IEEE Access 8 (2020): 54345–60. http://dx.doi.org/10.1109/access.2020.2981665.

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29

Wang, Sen, Jun Bi, and Jianping Wu. "Collaborative caching based on hash-routing for information-centric networking." ACM SIGCOMM Computer Communication Review 43, no. 4 (September 19, 2013): 535–36. http://dx.doi.org/10.1145/2534169.2491727.

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30

Lei, Lei, Xiong Xiong, Lu Hou, and Kan Zheng. "Collaborative Edge Caching through Service Function Chaining: Architecture and Challenges." IEEE Wireless Communications 25, no. 3 (June 2018): 94–102. http://dx.doi.org/10.1109/mwc.2018.1700321.

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31

Furqan, Muhammad, Wen Yan, Cheng Zhang, Shahid Iqbal, Qasim Jan, and Yongming Huang. "An Energy-Efficient Collaborative Caching Scheme for 5G Wireless Network." IEEE Access 7 (2019): 156907–16. http://dx.doi.org/10.1109/access.2019.2949272.

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32

Fan, Wenhao, Yuan'an Liu, Bihua Tang, Fan Wu, and Hongguang Zhang. "TerminalBooster: Collaborative Computation Offloading and Data Caching via Smart Basestations." IEEE Wireless Communications Letters 5, no. 6 (December 2016): 612–15. http://dx.doi.org/10.1109/lwc.2016.2605694.

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33

Liu, Junyan, Dapeng Li, and Youyun Xu. "Collaborative Online Edge Caching With Bayesian Clustering in Wireless Networks." IEEE Internet of Things Journal 7, no. 2 (February 2020): 1548–60. http://dx.doi.org/10.1109/jiot.2019.2956554.

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34

Ren, Dewang, Xiaolin Gui, Kaiyuan Zhang, and Jie Wu. "Hybrid collaborative caching in mobile edge networks: An analytical approach." Computer Networks 158 (July 2019): 1–16. http://dx.doi.org/10.1016/j.comnet.2019.04.026.

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35

Liu, Fang, Zhenyuan Zhang, Zunfu Wang, and Yuting Xing. "ECC: Edge Collaborative Caching Strategy for Differentiated Services Load-Balancing." Computers, Materials & Continua 69, no. 2 (2021): 2045–60. http://dx.doi.org/10.32604/cmc.2021.018303.

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36

Li, Xiuhua, Xiaofei Wang, Keqiu Li, Zhu Han, and Victor C. M. Leung. "Collaborative Multi-Tier Caching in Heterogeneous Networks: Modeling, Analysis, and Design." IEEE Transactions on Wireless Communications 16, no. 10 (October 2017): 6926–39. http://dx.doi.org/10.1109/twc.2017.2734646.

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37

Jeon, Won J., and Klara Nahrstedt. "QoS-aware middleware support for collaborative multimedia streaming and caching service." Microprocessors and Microsystems 27, no. 2 (March 2003): 65–72. http://dx.doi.org/10.1016/s0141-9331(02)00098-4.

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38

Gharaibeh, Ammar, Abdallah Khreishah, Bo Ji, and Moussa Ayyash. "A Provably Efficient Online Collaborative Caching Algorithm for Multicell-Coordinated Systems." IEEE Transactions on Mobile Computing 15, no. 8 (August 1, 2016): 1863–76. http://dx.doi.org/10.1109/tmc.2015.2474364.

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39

Ren, Jianji, Haichao Wang, Tingting Hou, Shuai Zheng, and Chaosheng Tang. "Collaborative Edge Computing and Caching With Deep Reinforcement Learning Decision Agents." IEEE Access 8 (2020): 120604–12. http://dx.doi.org/10.1109/access.2020.3007002.

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40

Xu, Xianzhe, Meixia Tao, and Cong Shen. "Collaborative Multi-Agent Multi-Armed Bandit Learning for Small-Cell Caching." IEEE Transactions on Wireless Communications 19, no. 4 (April 2020): 2570–85. http://dx.doi.org/10.1109/twc.2020.2966599.

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41

Jaber, Ghada, and Rahim Kacimi. "A collaborative caching strategy for content-centric enabled wireless sensor networks." Computer Communications 159 (June 2020): 60–70. http://dx.doi.org/10.1016/j.comcom.2020.05.018.

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42

Khanal, Subina, Kyi Thar, and Eui-Nam Huh. "DCoL: Distributed Collaborative Learning for Proactive Content Caching at Edge Networks." IEEE Access 9 (2021): 73495–505. http://dx.doi.org/10.1109/access.2021.3080512.

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43

Satsiou, Anna, and Michael Paterakis. "Frequency-based cache management policies for collaborative and non-collaborative topologies of segment based video caching proxies." Multimedia Systems 12, no. 2 (September 7, 2006): 117–33. http://dx.doi.org/10.1007/s00530-006-0042-0.

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44

Ahmad, Fawad, Ayaz Ahmad, Irshad Hussain, Peerapong Uthansakul, and Suleman Khan. "Cooperation Based Proactive Caching in Multi-Tier Cellular Networks." Applied Sciences 10, no. 18 (September 4, 2020): 6145. http://dx.doi.org/10.3390/app10186145.

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The limited caching capacity of the local cache enabled Base station (BS) decreases the cache hit ratio (CHR) and user satisfaction ratio (USR). However, Cache enabled multi-tier cellular networks have been presented as a promising candidate for fifth generation networks to achieve higher CHR and USR through densification of networks. In addition to this, the cooperation among the BSs of various tiers for cached data transfer, intensify its significance many folds. Therefore, in this paper, we consider maximization of CHR and USR in a multi-tier cellular network. We formulate a CHR and USR problem for multi-tier cellular networks while putting major constraints on caching space of BSs of each tier. The unsupervised learning algorithms such as K-mean clustering and collaborative filtering have been used for clustering the similar BSs in each tier and estimating the content popularity respectively. A novel scheme such as cluster average popularity based collaborative filtering (CAP-CF) algorithm is employed to cache popular data and hence maximizing the CHR in each tier. Similarly, two novel methods such as intra-tier and cross-tier cooperation (ITCTC) and modified ITCTC algorithms have been employed in order to optimize the USR. Simulations results witness, that the proposed schemes yield significant performance in terms of average cache hit ratio and user satisfaction ratio compared to other conventional approaches.

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45

Ma, Chunguang, Lei Zhang, Songtao Yang, and Xiaodong Zheng. "Hiding Yourself Behind Collaborative Users When Using Continuous Location-Based Services." Journal of Circuits, Systems and Computers 26, no. 07 (March 17, 2017): 1750119. http://dx.doi.org/10.1142/s0218126617501195.

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The prosperity of location-based services (LBSs) makes more and more people pay close attention to personal privacy. In order to preserve users privacy, several schemes utilized a trusted third party (TTP) to obfuscate users, but these schemes were suspected as the TTP may become the single point of failure or service performance bottleneck. To alleviate the suspicion, schemes with collaborative users to achieve [Formula: see text]-anonymity were proposed. In these schemes, users equipped with short-range communication devices could communicate with adjacent users to establish an anonymous group. With this group, the user can obfuscate and hide herself behind at least [Formula: see text] other users. However, these schemes are usually more efficient in snapshot services than continuous ones. To cope with the inadequacy, with the help of caching in mobile devices, we propose a query information blocks random exchange and results caching scheme (short for CaQBE). In this scheme, a particular user is hidden behind collaborative users in snapshot service, and then the caches further preserve the privacy in continuous service. In case of the active adversary launching the query correlation attack and the passive adversary launching the impersonation attack, a random collaborative user selection and a random block exchange algorithm are also utilized. Then based on the feature of entropy, a metric to measure the privacy of the user against attacks from the active and passive adversaries is proposed. Finally, security analysis and experimental comparison with other similar schemes further verify the optimal of our scheme in effectiveness of preservation and efficiency of performance.

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46

Zhang, Mingchuan, Bowei Hao, Fei Song, Meiyi Yang, Junlong Zhu, and Qingtao Wu. "Smart collaborative video caching for energy efficiency in cognitive Content Centric Networks." Journal of Network and Computer Applications 158 (May 2020): 102587. http://dx.doi.org/10.1016/j.jnca.2020.102587.

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47

Baccour, Emna, Aiman Erbad, Amr Mohamed, Mohsen Guizani, and Mounir Hamdi. "Collaborative hierarchical caching and transcoding in edge network with CE-D2D communication." Journal of Network and Computer Applications 172 (December 2020): 102801. http://dx.doi.org/10.1016/j.jnca.2020.102801.

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48

Li, Yan, Shifang Dai, and Xiangmao Chang. "Collaborative video caching scheme over OFDM-based long-reach passive optical networks." Optical Fiber Technology 43 (July 2018): 72–81. http://dx.doi.org/10.1016/j.yofte.2018.04.010.

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49

Yu, Philip S., and Edward A. MacNair. "Performance study of a collaborative method for hierarchical caching in proxy servers." Computer Networks and ISDN Systems 30, no. 1-7 (April 1998): 215–24. http://dx.doi.org/10.1016/s0169-7552(98)00015-4.

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50

Khreishah, Abdallah, Jacob Chakareski, and Ammar Gharaibeh. "Joint Caching, Routing, and Channel Assignment for Collaborative Small-Cell Cellular Networks." IEEE Journal on Selected Areas in Communications 34, no. 8 (August 2016): 2275–84. http://dx.doi.org/10.1109/jsac.2016.2577199.

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