Reducing upload and Download Time on Cloud using Content Distribution Algorithm

Cloud computing is a term, which involves virtualization, distributed computing, networking, software and web services. Cloud services provide resources efficiently based on demand. Central of these lies in the establishment of an effective algorithm to Achieve minimum distribution time (MDT). Achieving MDT is crucial for bulk-synchronous applications, when every client in the set to finish their download before being able to make use of the downloaded content. In this paper, we propose the use of dedicated Servers to accelerate peer - assisted content distribution using content distribution algorithm. Downloading time is reduced using BitTorrent application and Steiner tree algorithm. BitTorrent , a popular Peer-to -Peer file sharing protocol for mass distributions. Steiner tree algorithm, a star based protocol to effectively reduce the distribution time

AngelCast: cloud-based peer-assisted live streaming using optimized multi-tree construction

Increasingly, commercial content providers (CPs) offer streaming solutions using peer-to-peer (P2P) architectures, which promises significant scalabil- ity by leveraging clients’ upstream capacity. A major limitation of P2P live streaming is that playout rates are constrained by clients’ upstream capac- ities – typically much lower than downstream capacities – which limit the quality of the delivered stream. To leverage P2P architectures without sacri- ficing quality, CPs must commit additional resources to complement clients’ resources. In this work, we propose a cloud-based service AngelCast that enables CPs to complement P2P streaming. By subscribing to AngelCast, a CP is able to deploy extra resources (angel), on-demand from the cloud, to maintain a desirable stream quality. Angels do not download the whole stream, nor are they in possession of it. Rather, angels only relay the minimal fraction of the stream necessary to achieve the desired quality. We provide a lower bound on the minimum angel capacity needed to maintain a desired client bit-rate, and develop a fluid model construction to achieve it. Realizing the limitations of the fluid model construction, we design a practical multi- tree construction that captures the spirit of the optimal construction, and avoids its limitations. We present a prototype implementation of AngelCast, along with experimental results confirming the feasibility of our service.Supported in part by NSF awards #0720604, #0735974, #0820138, #0952145, #1012798 #1012798 #1430145 #1414119. (0720604 - NSF; 0735974 - NSF; 0820138 - NSF; 0952145 - NSF; 1012798 - NSF; 1430145 - NSF; 1414119 - NSF

Cognitive networking techniques on content distribution networks

First we want to design a strategy based on Artificial Intelligence (AI) techniques with the aim of increasing peers download performance. Some AI algorithms can find patterns in the information available to a peer locally, and use it to predict values that cannot be calculated by means of mathematical formulas. An important aspect of these techniques is that can be trained in order to improve its interpretation of the local available information. With this process they can make more accurate predictions and perform better results. We will use this prediction system to increase our knowledge about the swarm and the peers who are part of it. This global knowledge increase can be used to optimize the algorithms of BitTorrent and can represent a great improvement in peers download capacity. Our second challenge is to create a reduced group of peers (Crowd) that focus their efforts on improving the condition of the swarm through collaborative techniques. The basic idea of this approach is to organize a group of peers to act as a single node and focus them on getting all pieces of the content they are interested in. This involves avoiding, as far as possible, to download pieces that any of the members already have. The main goal of this technique consists of reaching as quickly as possible a copy of the content distributed between all members of the Crowd. Getting a distributed copy of the content is expected to increase the availability of parts and reduce dependence on the seeds (users who have the complete content), which would represent a great benefit for the whole swarm. Another aspect that we want to investigate is the use of a priority system among members of the Crowd. We consider that in certain situations to prioritize the Crowd peers at expense of regular peers can result in a significant increase of the download ratio

A Survey on Adaptive Multimedia Streaming

Internet was primarily designed for one to one applications like electronic mail, reliable file transfer etc. However, the technological growth in both hardware and software industry have written in unprecedented success story of the growth of Internet and have paved the paths of modern digital evolution. In today’s world, the internet has become the way of life and has penetrated in its every domain. It is nearly impossible to list the applications which make use of internet in this era however, all these applications are data intensive and data may be textual, audio or visual requiring improved techniques to deal with these. Multimedia applications are one of them and have witnessed unprecedented growth in last few years. A predominance of that is by virtue of different video streaming applications in daily life like games, education, entertainment, security etc. Due to the huge demand of multimedia applications, heterogeneity of demands and limited resource availability there is a dire need of adaptive multimedia streaming. This chapter provides the detail discussion over different adaptive multimedia streaming mechanism over peer to peer network

Doctor of Philosophy

dissertationWe develop a novel framework for friend-to-friend (f2f) distributed services (F3DS) by which applications can easily offer peer-to-peer (p2p) services among social peers with resource sharing governed by approximated levels of social altruism. Our frame- work differs significantly from typical p2p collaboration in that it provides a founda- tion for distributed applications to cooperate based on pre-existing trust and altruism among social peers. With the goal of facilitating the approximation of relative levels of altruism among social peers within F3DS, we introduce a new metric: SocialDistance. SocialDistance is a synthetic metric that combines direct levels of altruism between peers with an altruism decay for each hop to approximate indirect levels of altruism. The resulting multihop altruism levels are used by F3DS applications to proportion and prioritize the sharing of resources with other social peers. We use SocialDistance to implement a novel flash file/patch distribution method, SocialSwarm. SocialSwarm uses the SocialDistance metric as part of its resource allocation to overcome the neces- sity of (and inefficiency created by) resource bartering among friends participating in a BitTorrent swarm. We find that SocialSwarm achieves an average file download time reduction of 25% to 35% in comparison with standard BitTorrent under a variety of configurations and conditions, including file sizes, maximum SocialDistance, as well as leech and seed counts. The most socially connected peers yield up to a 47% decrease in download completion time in comparison with average nonsocial BitTorrent swarms. We also use the F3DS framework to implement novel malware detection application- F3DS Antivirus (F3AV)-and evaluate it on the Amazon cloud. We show that with f2f sharing of resources, F3AV achieves a 65% increase in the detection rate of 0- to 1-day-old malware among social peers as compared to the average of individual scanners. Furthermore, we show that F3AV provides the greatest diversity of mal- ware scanners (and thus malware protection) to social hubs-those nodes that are positioned to provide strategic defense against socially aware malware