Reducing Cost and Contention of P2P Live Streaming through Locality and Piece Selection

The use of locality within peer-to-peer (P2P) networks is ensuring the construction of overlay networks that are both economically viable for network operators and scalable. However, the underlying protocols on which traditional P2P overlays are based are rapidly having to evolve in order to better support more time sensitive, real-time video delivery systems. This shift places greater demand on locality mechanisms to ensure the correct balance between bandwidth savings and successful timely playback. In this paper, we investigate the impact of peer locality within live streaming P2P systems and consider the pertinent challenges when designing locality based algorithms to support efficient P2P live streaming services. Based on our findings we propose an algorithm for supporting locality and harmonised play points in a live streaming P2P system. We present our results and in-depth analysis of its operation though a series of simulations which measure bandwidth consumption at network egress points, failure rates and each peer’s play point relative to the live stream

The Internet-Wide Impact of P2P Traffic Localization on ISP Profitability

We conduct a detailed simulation study to examine how localizing P2P traffic within network boundaries impacts the profitability of an ISP. A distinguishing aspect of our work is the focus on Internet-wide implications, i.e., how adoption of localization within an ISP affects both itself and other ISPs. Our simulations are based on detailed models that estimate inter-autonomous-system (AS) P2P traffic and inter-AS routing, localization models that predict the extent to which P2P traffic is reduced, and pricing models that predict the impact of changes in traffic on the profit of an ISP. We evaluate our models by using a large-scale crawl of BitTorrent containing over 138 million users sharing 2.75 million files. Our results show that the benefits of localization must not be taken for granted. Some of our key findings include: 1) residential ISPs can actually lose money when localization is employed, and some of them will not see increased profitability until other ISPs employ localization; 2) the reduction in costs due to localization will be limited for small ISPs and tends to grow only logarithmically with client population; and 3) some ISPs can better increase profitability through alternate strategies to localization by taking advantage of the business relationships they have with other ISP

온라인 게임과 컨텐트 공유 네트워크 분석을 통한 온라인 군집 현상의 이해

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2015. 2. 권태경.Quantification of collective human behavior and understanding the group characteristics in the Internet is important in user behavior studies since people tend to gather together and form groups due to their inherent nature. On the Internet, people are also often forming a group for a specific purpose such as i) an online group in games (e.g., MMORPGs) to experience various social interactions with other players or accomplish a difficult quest with teammates or ii) a swarm in peer-to-peer network to share a content to utilize a higher download rate with an availability. To this end, we studied the two most well-known major applications in the Internet that people are actively using with different purposesi) MMORPGs and ii) BitTorrent. In this dissertation, we analyze the i) group activities of users in Aion, one of the largest MMORPGs, based on the records of the activities of 94,497 users and ii) crowd phenomena of BitTorrent. First, in a case study of Aion, we focus on (i) how social interactions within a group differ from the ones across groups, (ii) what makes a group rise, sustain, or fall, (iii) how group members join and leave a group, and (iv) what makes a group end. We first find that structural patterns of social interactions within a group are more likely to be close-knit and reciprocative than the ones across groups.We also observe that members in a rising group (i.e., the number of members increases) are more cohesive, and communicate with more evenly within the group than the ones in other groups. Our analysis further reveals that if a group is not cohesive, not actively communicating, or not evenly communicating among members, members of the group tend to leave. Second, we investigate what kinds of crowd phenomena of content exist and why different patterns of crowd phenomena appears and how we can exploit content crowd phenomena considering the content category, publisher, and population of content in BitTorrent. To this end,We conduct comprehensive measurements on content locality in one of the largest BitTorrent portals: The Pirate Bay. In particular, we focus on (i) how content is consumed from spatial and temporal perspectives, (ii) what makes content be consumed with disparity in spatial and temporal domains, and (iii) how we can exploit the content locality. We find that content consumption in real swarms is 4.56 times and 1.46 times skewed in spatial (country) and temporal (time) domains, respectively. We observe that a cultural factor (e.g., language) mainly affects spatial locality of content. Not only the time-sensitivity of content but also the publishing purpose affects temporal locality of content.We reveal that spatial locality of content iii rarely changes on a daily basis (microscopic level), but there is notably spatial spread of content consumption over the years (macroscopic level). Based on the observation, we conduct simulations to show that bundling and caching can exploit the content locality.Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii I. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1 Crowd Phenomena in Massively Multi-player Online Role-Playing Games (MMORPGs) . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.2 Crowd Phenomena in BitTorrent . . . . . . . . . . . . . . . . . . . 3 II. RelatedWork . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.1 Crowd Phenomena in MMORPGs . . . . . . . . . . . . . . . . . . 6 2.1.1 Social Interactions in MMORPGs . . . . . . . . . . . . . . 6 2.1.2 Group Activities in MMORPGs . . . . . . . . . . . . . . . 7 2.1.3 Group Activities in Other Online Services . . . . . . . . . . 7 2.2 Crowd Phenomena (Locality) in BitTorrent . . . . . . . . . . . . . 8 2.2.1 Peer Localization . . . . . . . . . . . . . . . . . . . . . . . 8 2.2.2 Crowd Phenomena in BitTorrent . . . . . . . . . . . . . . . 9 2.2.3 Locality in Other Domains . . . . . . . . . . . . . . . . . . 10 III. Group Activities in Online Social Game . . . . . . . . . . . . . . . . 11 3.1 Aion overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 3.1.1 Game Features . . . . . . . . . . . . . . . . . . . . . . . . 11 3.1.2 Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 3.2 Group Affiliation . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 v 3.2.1 How prevalent are group activities in Aion? . . . . . . . . . 14 3.2.2 Effect of Joining a Group . . . . . . . . . . . . . . . . . . . 16 3.2.3 Social Interactions Within a Group . . . . . . . . . . . . . . 16 3.3 Group Dynamics . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 3.3.1 Group Cohesion . . . . . . . . . . . . . . . . . . . . . . . 20 3.3.2 Group Diversity . . . . . . . . . . . . . . . . . . . . . . . . 24 3.3.3 Group Locality . . . . . . . . . . . . . . . . . . . . . . . . 28 3.3.4 Survival Rate . . . . . . . . . . . . . . . . . . . . . . . . . 31 3.3.5 Dichotomy in Stable Groups . . . . . . . . . . . . . . . . . 32 3.4 Group Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 3.4.1 Properties of the Group Network . . . . . . . . . . . . . . . 36 3.4.2 Structural Holes . . . . . . . . . . . . . . . . . . . . . . . 38 3.5 Implications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 3.5.1 Why people leave groups? . . . . . . . . . . . . . . . . . . 40 3.5.2 Why a group ends? . . . . . . . . . . . . . . . . . . . . . . 42 IV. Crowd phenomena of BitTorrent in Spatial and Temporal Perspective 46 4.1 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 4.1.1 Discovering Swarm Topology . . . . . . . . . . . . . . . . 46 4.1.2 Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 4.1.3 Representativeness . . . . . . . . . . . . . . . . . . . . . . 49 4.2 Spatial Locality . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 4.2.1 Locality Metrics . . . . . . . . . . . . . . . . . . . . . . . 51 4.2.2 Swarm, Community, and Neighbor . . . . . . . . . . . . . 53 vi 4.2.3 Content Categories, Publishers, and Popularity . . . . . . . 55 4.2.4 Spatial Locality Over Time . . . . . . . . . . . . . . . . . . 58 4.3 Temporal Locality . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 4.3.1 Existence of Temporal Locality . . . . . . . . . . . . . . . 61 4.3.2 Categories, Publishers, and Popularity . . . . . . . . . . . . 63 4.3.3 Temporal Usage Trends . . . . . . . . . . . . . . . . . . . 68 4.4 How to Exploit Locality . . . . . . . . . . . . . . . . . . . . . . . 70 V. Summary & Future Work . . . . . . . . . . . . . . . . . . . . . . . . 74 Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76Docto

Content Distribution in P2P Systems

The report provides a literature review of the state-of-the-art for content distribution. The report's contributions are of threefold. First, it gives more insight into traditional Content Distribution Networks (CDN), their requirements and open issues. Second, it discusses Peer-to-Peer (P2P) systems as a cheap and scalable alternative for CDN and extracts their design challenges. Finally, it evaluates the existing P2P systems dedicated for content distribution according to the identied requirements and challenges

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

CoShare: A Cost-effective Data Sharing System for Data Center Networks

Numerous research groups and other organizations collect data from popular data sources such as online social networks. This leads to the problem of data islands, wherein all this data is isolated and lying idly, without any use to the community at large. Using existing centralized solutions such as Dropbox to replicate data to all interested parties is prohibitively costly, given the large size of datasets. A practical solution is to use a Peer-to-Peer (P2P) approach to replicate data in a self-organized manner. However, existing P2P approaches focus on minimizing downloading time without taking into account the bandwidth cost. In this paper, we present CoShare, a P2P inspired decentralized cost effective sharing system for data replication. CoShare allows users to specify their requirements on data sharing tasks and maps these requirements into resource requirements for data transfer. Through extensive simulations, we demonstrate that CoShare finds the desirable tradeoffs for a given cost and performance while varying user requirements and request arrival rates