Cache replacement for transcoding proxy caching

© 2005 IEEE.In this paper, we address the problem of cache replacement for transcoding proxy caching. First, an efficient cache replacement algorithm is proposed. Our algorithm considers both the aggregate effect of caching multiple versions of the same multimedia object and cache consistency. Second, a complexity analysis is presented to show the efficiency of our algorithm. Finally, some preliminary simulation experiments are conducted to compare the performance of our algorithm with some existing algorithms. The results show that our algorithm outperforms others in terms of the various performance metrics.Keqiu Li, Keishi Tajima, Hong She

On a caching system with object sharing

We consider a content-caching system thatis shared by a number of proxies. The cache could belocated in an edge-cloud datacenter and the proxies couldeach serve a large population of mobile end-users. Eachproxy operates its own LRU-list of a certain capacity inthe shared cache. The length of objects simultaneouslyappearing in plural LRU-lists is equally divided amongthem,i.e., object sharing among the LRUs. We provide a "working-set" approximation for this system to quicklyestimate the cache-hit probabilities under such objectsharing, which can be used to facilitate admission control.Also, a way to reduce ripple evictions,i.e.,setrequestoverhead, is suggested. We give numerical results for ourMemCacheD with Object Sharing (MCD-OS) prototype

Modelling Data Dissemination in Opportunistic Networks

In opportunistic networks data dissemination is an impor- tant, although not widely explored, topic. Since oppor- tunistic networks topologies are very challenged and un- stable, data-centric approaches are an interesting direction to pursue. Data should be proactively and cooperatively disseminated from sources towards possibly interested re- ceivers, as sources and receivers might not be aware of each other, and never get in touch directly. In this paper we con- sider a utility-based cooperative data dissemination system in which the utility of data is defined based on the social relationships between users. Specifically, we study the per- formance of this system through an analytical model. Our model allows us to completely characterise the data dissem- ination process, as it describes both its stationary and tran- sient regimes. After validating the model, we study the sys- tem\u27s behaviour with respect to key parameters such as the definition of the data utility function, the initial data allo- cation on nodes, the number of users in the system, and the data popularity

Tag-based Recommender System for Context-Aware Content Dissemination in Opportunistic Networks

Content dissemination in opportunistic networks is a hot research topic that attracted a lot of interest in the last few years. The key idea is to optimise the diffusion of content among nodes in opportunistic networks to ensure that users are always able to obtain the most relevant items according to their interests. The classical approach is to statically define a set of interests for each user, and make sure that they receive items matching those interests. In this paper, we propose a novel approach, based on the dynamic and automatic identification of interests. To do so, we exploit the tags that users assign to the items they create, and the tags of the items that they download. We model these actions through a folksonomy and the related tripartite graph, with different nodes for users, items, and tags. We use this graph as the basis for identifying the relevance of the items. Specifically, we use a tag-based recommender system on the graph, called PLIERS, that is able to calculate the relevance of an item for a certain user, with respect to the items that are already linked to this user. We validate our approach through a series of simulations. We emulate the presence of a variable number of agents which randomly move, create and tag items, and possibly encounter other agents. Each agent maintains a tripartite graph locally, representing its actions, and it integrates this graph with information received from other encountered nodes. The agents use PLIERS on their local graph to assess the relevance of the items they find, and they decide whether these items are relevant for them or not. We evaluate the accuracy of the results by comparing the recommendations on the local graphs with the relevance of the items (calculated through PLIERS) on a global graph obtained by merging together all the local graphs of the nodes. This graph represents the complete knowledge of all actions in the network and it allows us to obtain the best possible recommendations for a target user, that could be obtained if all the nodes had the full knowledge of the actions of other nodes. The results indicate that the recommendations on the local graph are accurate and that the local knowledge of nodes reaches the global knowledge in the network through a sufficiently high number of contacts

Design and Performance Evaluation of Data Dissemination Systems for Opportunistic Networks Based on Cognitive Heuristics

It is often argued that the Future Internet will be a very large scale content-centric network. Scalability issues will stem even more from the amount of content nodes will gen- erate, share and consume. In order to let users become aware and retrieve the content they really need, these nodes will be required to swiftly react to stimuli and assert the rele- vance of discovered data under uncertainty and only partial information. The human brain performs the task of infor- mation ltering and selection using the so-called cognitive heuristics, i.e. simple, rapid, low-resource demanding, yet very eective schemes that can be modeled using a func- tional approach. In this paper we propose a solution based on one such heuristics, namely the recognition heuristic, for dealing with data dissemination in opportunistic networks. We show how to implement an algorithm that exploits the environmental information in order to implement an eec- tive dissemination of data based on the recognition heuristic, and provide a performance evaluation of such a solution via simulation