Cooperative announcement-based caching for video-on-demand streaming

Recently, video-on-demand (VoD) streaming services like Netflix and Hulu have gained a lot of popularity. This has led to a strong increase in bandwidth capacity requirements in the network. To reduce this network load, the design of appropriate caching strategies is of utmost importance. Based on the fact that, typically, a video stream is temporally segmented into smaller chunks that can be accessed and decoded independently, cache replacement strategies have been developed that take advantage of this temporal structure in the video. In this paper, two caching strategies are proposed that additionally take advantage of the phenomenon of binge watching, where users stream multiple consecutive episodes of the same series, reported by recent user behavior studies to become the everyday behavior. Taking into account this information allows us to predict future segment requests, even before the video playout has started. Two strategies are proposed, both with a different level of coordination between the caches in the network. Using a VoD request trace based on binge watching user characteristics, the presented algorithms have been thoroughly evaluated in multiple network topologies with different characteristics, showing their general applicability. It was shown that in a realistic scenario, the proposed election-based caching strategy can outperform the state-of-the-art by 20% in terms of cache hit ratio while using 4% less network bandwidth

Distributed Selfish Coaching

Although cooperation generally increases the amount of resources available to a community of nodes, thus improving individual and collective performance, it also allows for the appearance of potential mistreatment problems through the exposition of one node's resources to others. We study such concerns by considering a group of independent, rational, self-aware nodes that cooperate using on-line caching algorithms, where the exposed resource is the storage at each node. Motivated by content networking applications -- including web caching, CDNs, and P2P -- this paper extends our previous work on the on-line version of the problem, which was conducted under a game-theoretic framework, and limited to object replication. We identify and investigate two causes of mistreatment: (1) cache state interactions (due to the cooperative servicing of requests) and (2) the adoption of a common scheme for cache management policies. Using analytic models, numerical solutions of these models, as well as simulation experiments, we show that on-line cooperation schemes using caching are fairly robust to mistreatment caused by state interactions. To appear in a substantial manner, the interaction through the exchange of miss-streams has to be very intense, making it feasible for the mistreated nodes to detect and react to exploitation. This robustness ceases to exist when nodes fetch and store objects in response to remote requests, i.e., when they operate as Level-2 caches (or proxies) for other nodes. Regarding mistreatment due to a common scheme, we show that this can easily take place when the "outlier" characteristics of some of the nodes get overlooked. This finding underscores the importance of allowing cooperative caching nodes the flexibility of choosing from a diverse set of schemes to fit the peculiarities of individual nodes. To that end, we outline an emulation-based framework for the development of mistreatment-resilient distributed selfish caching schemes. Our framework utilizes a simple control-theoretic approach to dynamically parameterize the cache management scheme. We show performance evaluation results that quantify the benefits from instantiating such a framework, which could be substantial under skewed demand profiles.National Science Foundation (CNS Cybertrust 0524477, CNS NeTS 0520166, CNS ITR 0205294, EIA RI 0202067); EU IST (CASCADAS and E-NEXT); Marie Curie Outgoing International Fellowship of the EU (MOIF-CT-2005-007230

Mobile-Based Video Caching Architecture Based on Billboard Manager

Video streaming services are very popular today. Increasingly, users can now access multimedia applications and video playback wirelessly on their mobile devices. However, a significant challenge remains in ensuring smooth and uninterrupted transmission of almost any size of video file over a 3G network, and as quickly as possible in order to optimize bandwidth consumption. In this paper, we propose to position our Billboard Manager to provide an optimal transmission rate to enable smooth video playback to a mobile device user connected to a 3G network. Our work focuses on serving user requests by mobile operators from cached resource managed by Billboard Manager, and transmitting the video files from this pool. The aim is to reduce the load placed on bandwidth resources of a mobile operator by routing away as much user requests away from the internet for having to search a video and, subsequently, if located, have it transferred back to the user.Comment: 8 pages, 1 figure, GridCom-201

Intelligent Cooperative Adaptive Weight Ranking Policy via dynamic aging based on NB and J48 classifiers

The increased usage of World Wide Web leads to increase in network traffic and create a bottleneck over the internet performance.  For most people, the accessing speed or the response time is the most critical factor when using the internet. Reducing response time was done by using web proxy cache technique that storing a copy of pages between client and server sides. If requested pages are cached in the proxy, there is no need to access the server. But, the cache size is limited, so cache replacement algorithms are used to remove pages from the cache when it is full. On the other hand, the conventional algorithms for replacement such as Least Recently Use (LRU), First in First Out (FIFO), Least Frequently Use (LFU), Randomised Policy, etc. may discard essential pages just before use. Furthermore, using conventional algorithms cannot be well optimized since it requires some decision to evict intelligently before a page is replaced. Hence, this paper proposes an integration of Adaptive Weight Ranking Policy (AWRP) with intelligent classifiers (NB-AWRP-DA and J48-AWRP-DA) via dynamic aging factor.  To enhance classifiers power of prediction before integrating them with AWRP, particle swarm optimization (PSO) automated wrapper feature selection methods are used to choose the best subset of features that are relevant and influence classifiers prediction accuracy.   Experimental Result shows that NB-AWRP-DA enhances the performance of web proxy cache across multi proxy datasets by 4.008%,4.087% and 14.022% over LRU, LFU, and FIFO while, J48-AWRP-DA increases HR by 0.483%, 0.563% and 10.497% over LRU, LFU, and FIFO respectively.  Meanwhile, BHR of NB-AWRP-DA rises by 0.9911%,1.008% and 11.5842% over LRU, LFU, and FIFO respectively while 0.0204%, 0.0379% and 10.6136 for LRU, LFU, FIFO respectively using J48-AWRP-DA

Object Distribution Networks for World-wide Document Circulation

This paper presents an Object Distribution System (ODS), a distributed system inspired by the ultra-large scale distribution models used in everyday life (e.g. food or newspapers distribution chains). Beyond traditional mechanisms of approaching information to readers (e.g. caching and mirroring), this system enables the publication, classification and subscription to volumes of objects (e.g. documents, events). Authors submit their contents to publication agents. Classification authorities provide classification schemes to classify objects. Readers subscribe to topics or authors, and retrieve contents from their local delivery agent (like a kiosk or library, with local copies of objects). Object distribution is an independent process where objects circulate asynchronously among distribution agents. ODS is designed to perform specially well in an increasingly populated, widespread and complex Internet jungle, using weak consistency replication by object distribution, asynchronous replication, and local access to objects by clients. ODS is based on two independent virtual networks, one dedicated to the distribution (replication) of objects and the other to calculate optimised distribution chains to be applied by the first network