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Letteratura scientifica selezionata sul tema "Collaborative Caching"

Autore: Grafiati

Pubblicato: 4 giugno 2021

Ultima modifica: 8 febbraio 2022

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Articoli di riviste sul tema "Collaborative Caching"

1

Hu, Qing, Chengming Li, Touhidul Hasan, Chengjun Li e 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 e Yujun Zhang. "Adaptive In-Network Collaborative Caching for Enhanced Ensemble Deep Learning at Edge". Mathematical Problems in Engineering 2021 (25 settembre 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 e A. Wahaballa. "A Literature Review on Collaborative Caching Techniques in MANETs: Issues and Methods used in Serving Queries". Engineering, Technology & Applied Science Research 9, n. 5 (9 ottobre 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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4

Tang, Qinqin, Renchao Xie, Tao Huang e Yunjie Liu. "Hierarchical collaborative caching in 5G networks". IET Communications 12, n. 18 (20 novembre 2018): 2357–65. http://dx.doi.org/10.1049/iet-com.2018.5553.

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5

Gu, Xiaoming, e Chen Ding. "A generalized theory of collaborative caching". ACM SIGPLAN Notices 47, n. 11 (8 gennaio 2013): 109–20. http://dx.doi.org/10.1145/2426642.2259012.

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6

Gao, Guoqiang, e Ruixuan Li. "Collaborative Caching in P2P Streaming Networks". Journal of Network and Systems Management 27, n. 3 (4 gennaio 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 e Hai Jin. "Online Collaborative Data Caching in Edge Computing". IEEE Transactions on Parallel and Distributed Systems 32, n. 2 (1 febbraio 2021): 281–94. http://dx.doi.org/10.1109/tpds.2020.3016344.

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8

Yang, Jiong, Wei Wang e Richard Muntz. "Collaborative Web caching based on proxy affinities". ACM SIGMETRICS Performance Evaluation Review 28, n. 1 (giugno 2000): 78–89. http://dx.doi.org/10.1145/345063.339360.

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9

Wu, Kun-Lung, e Philip S. Yu. "Latency-sensitive hashing for collaborative Web caching". Computer Networks 33, n. 1-6 (giugno 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 e Ajit Singh. "SPACE: A lightweight collaborative caching for clusters". Peer-to-Peer Networking and Applications 3, n. 2 (13 maggio 2009): 83–99. http://dx.doi.org/10.1007/s12083-009-0047-5.

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Più fonti

Tesi sul tema "Collaborative Caching"

1

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.

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2

Mahdavi, Mehregan Computer Science &amp 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.

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Abstract (sommario):

Web portals are one of the rapidly growing applications, providing a single interface to access different sources (providers). The results from the providers are typically obtained by each provider querying a database and returning an HTML or XML document. Performance and in particular providing fast response time is one of the critical issues in such applications. Dissatisfaction of users dramatically increases with increasing response time, resulting in abandonment of Web sites, which in turn could result in loss of revenue by the providers and the portal. Caching is one of the key techniques that address the performance of such applications. In this work we focus on improving the performance of portal applications via caching. We discuss the limitations of existing caching solutions in such applications and introduce a caching strategy based on collaboration between the portal and its providers. Providers trace their logs, extract information to identify good candidates for caching and notify the portal. Caching at the portal is decided based on scores calculated by providers and associated with objects. We evaluate the performance of the collaborative caching strategy using simulation data. We show how providers can trace their logs and calculate cache-worthiness scores for their objects and notify the portal. We also address the issue of heterogeneous scoring policies by different providers and introduce mechanisms to regulate caching scores. We also show how portal and providers can synchronize their meta-data in order to minimize the overhead associated with collaboration for caching.

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3

Cardenas, 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.

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4

Cardenas, 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.

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This thesis proposes an approach for the design and implementation of collaborative cache systems in grids that supports capabilities for monitoring and controlling cache interactions. Our approach permits to compose and evaluate high-level collaborative cache functions in a flexible way. Our proposal is based on a multilayer model that defines the main functions of a collaborative grid cache system. This model and the provided specification are used to build a flexible and generic software infrastructure for the operation and control of collaborative caches. This infrastructure is composed of a group of autonomous cache elements called Grid Cache Services (GCS). The GCS is a local administrator of temporary storage and data which is implemented as a grid service that provides the cache capabilities defined by the model. We study a possible configuration for a group of GCS that constitutes a basic management system of temporary data called Temporal Storage Service (TSS)
Cette 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)

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5

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.

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Les réseaux cellulaires traditionnels ont évolué de la première génération à base de communications analogiques à la quatrième génération de communications numériques, où les technologies de la couche physique se sont améliorées de façon à considérablement augmenter la capacité du réseau. Selon la théorie de Shannon, les gains apportés par la couche physique vont être progressivement saturés, ce qui ne permet pas de suivre l'augmentation rapide de la demande de trafic des utilisateurs dans l'ère actuelle de l'Internet mobile. Ces dernières années, les communautés universitaires ont commencé à utiliser les données réelles pour analyser le déploiement de l'infrastructure des réseaux sans fil et la demande de trafic des utilisateurs mobiles, afin de tirer parti des modèles statistiques sous-jacents. Dans le même temps, avec la récente montée en puissance des techniques d'apprentissage automatique, l’analyse de données est considérée comme le prochain verrou de croissance économique. Ainsi, l'industrie accorde de plus en plus d'attention à l'accumulation de données et les services liés à l'exploitation des connaissances et les opérateurs de télécommunications commencent à prendre conscience de l'importance croissante des données enregistrées à partir de leurs propres réseaux. Par conséquent, le progrès technologique axé sur les données réelles est considéré comme une orientation prometteuse pour la prochaine évolution des réseaux cellulaires.Dans cette thèse, nous avons tout d'abord passé en revue les mesures de données réelles issues d’opérateurs dans le chapitre 2 qui non seulement mettent en lumière l'importance de l'analyse de données réelles, mais ouvrent également la voie à la possibilité d’améliorer les performances de service des réseaux cellulaires. Dans cette analyse, nous avons conclu qu'il existe un modèle périodique pour l'hypothèse de trafic temporel de grande zone de couverture dans les réseaux cellulaires, tandis que pour une cellule unique, une distribution de type « heavy tail » caractérise la caractérisation temporelle et spatiale. Ce phénomène de déséquilibre apparaît plus significativement dans la durée de l'appel, dans les demandes d'arrivée et la préférence de contenu des utilisateurs mobiles.Ensuite, sur la base d'une grande quantité de données réelles collectées à partir des réseaux cellulaires en fonctionnement, nous avons effectué une identification à grande échelle sur la modélisation spatiale des stations de base (BS) dans le chapitre 3. Selon les résultats de cet ajustement, nous avons vérifié l'inexactitude de la distribution de Poisson pour les emplacements de BS, et mise en évidence le regroupement dans le déploiement de BS dans les réseaux cellulaires. Cependant, bien que les modèles de regroupement typiques aient amélioré la précision de la modélisation, ils ne sont pas encore valides pour reproduire fidèlement le déploiement pratique des BS, ce qui conduit à la caractérisation de la densité spatiale de BS.Dans le chapitre 4, nous avons caractérisé la densité du déploiement BS et de la demande de trafic, à la fois dans le domaine spatial et mais aussi dans le domaine temporel. En accord avec les phénomènes de type « heavy tail » du chapitre 2, nous avons trouvé que la distribution α-Stable était le modèle le plus précis pour les densités spatiales des BS et de la distribution de trafic, entre lesquelles une dépendance linéaire est révélée en analysant les données réelles. De plus, les exactitudes des lois de puissance et lognormales pour la longueur des paquets et l'heure d'arrivée des requêtes des utilisateurs sont vérifiées respectivement, ce qui conduit de manière convaincante à la distribution α-Stable du volume de trafic agrégé temporellement au niveau BS.[...]
Traditional 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.[...]

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6

Lorrillere, Maxime. "Caches collaboratifs noyau adaptés aux environnements virtualisés". Thesis, Paris 6, 2016. http://www.theses.fr/2016PA066036/document.

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Avec l'avènement du cloud computing, la virtualisation est devenue aujourd'hui incontournable. Elle offre isolation et flexibilité, en revanche elle implique une fragmentation des ressources, et notamment de la mémoire. Les performances des applications qui effectuent beaucoup d'entrées/sorties (E/S) en sont particulièrement impactées. En effet, celles-ci reposent en grande partie sur la présence de mémoire libre, utilisée par le système pour faire du cache et ainsi accélérer les E/S. Ajuster dynamiquement les ressources d'une machine virtuelle devient donc un enjeu majeur. Dans cette thèse nous nous intéressons à ce problème, et nous proposons Puma, un cache réparti permettant de mutualiser la mémoire inutilisée des machines virtuelles pour améliorer les performances des applications qui effectuent beaucoup d'E/S. Contrairement aux solutions existantes, notre approche noyau permet à Puma de fonctionner avec les applications sans adaptation ni système de fichiers spécifique. Nous proposons plusieurs métriques, reposant sur des mécanismes existants du noyau Linux, qui permettent de définir le niveau d'activité « cache » du système. Ces métriques sont utilisées par Puma pour automatiser le niveau de contribution d'un noeud au cache réparti. Nos évaluations de Puma montrent qu'il est capable d'améliorer significativement les performances d'applications qui effectuent beaucoup d'E/S et de s'adapter dynamiquement afin de ne pas dégrader leurs performances
With 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

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7

Wang, Yu-Hsiang, e 王煜祥. "Collaborative Computation Offloading in Mobile Edge Computing with Caching". Thesis, 2019. http://ndltd.ncl.edu.tw/handle/f35asd.

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碩士
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.

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Liu, Chao-Shiu, e 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.

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碩士
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 (CC) 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 CC 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.

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Capitoli di libri sul tema "Collaborative Caching"

1

Gao, Guoqiang, e 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.

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Lefèvre, Laurent, Jean-Marc Pierson e 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.

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O’Neill, John Paul, e 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.

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Wang, XiaoYu, WeeSiong Ng, BengChin Ooi, Kian-Lee Tan e 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.

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Guo, Shuo, Haiyong Xie e 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.

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Jiang, Hao, Hehe Huang, Ying Jiang, Yuan Wang, Yuanyuan Zeng e 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.

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Jones, Andrew, e 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.

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Qian, Weining, Linhao Xu, Shuigeng Zhou e 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.

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9

Wang, Ruichao, Jizhao Lu, Yongjie Li, Xingyu Chen e 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.

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Ren, Jianji, Tingting Hou e 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.

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Atti di convegni sul tema "Collaborative Caching"

1

Gu, Xiaoming, e 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.

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2

Khreishah, Abdallah, e 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.

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3

Zhong, Nan, Hongmei Zhang e 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.

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4

Gu, 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.

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Yao, Chao, Changkun Jiang, Zun Liu, Jie Chen e 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.

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Gu, 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.

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Yang, Yi-Jen, Ming-Hsun Yang, Y. W. Peter Hong e 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.

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Yang, Jiong, Wei Wang e 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.

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Miyoshi, Yuta, Takuya Wada e 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.

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Li, Xiuhua, Peiran Wu, Xiaofei Wang, Keqiu Li, Zhu Han e 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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