Modeling and Control of Rare Segments in BitTorrent with Epidemic Dynamics

Despite its existing incentives for leecher cooperation, BitTorrent file sharing fundamentally relies on the presence of seeder peers. Seeder peers essentially operate outside the BitTorrent incentives, with two caveats: slow downlinks lead to increased numbers of "temporary" seeders (who left their console, but will terminate their seeder role when they return), and the copyright liability boon that file segmentation offers for permanent seeders. Using a simple epidemic model for a two-segment BitTorrent swarm, we focus on the BitTorrent rule to disseminate the (locally) rarest segments first. With our model, we show that the rarest-segment first rule minimizes transition time to seeder (complete file acquisition) and equalizes the segment populations in steady-state. We discuss how alternative dissemination rules may {\em beneficially increase} file acquisition times causing leechers to remain in the system longer (particularly as temporary seeders). The result is that leechers are further enticed to cooperate. This eliminates the threat of extinction of rare segments which is prevented by the needed presence of permanent seeders. Our model allows us to study the corresponding trade-offs between performance improvement, load on permanent seeders, and content availability, which we leave for future work. Finally, interpreting the two-segment model as one involving a rare segment and a "lumped" segment representing the rest, we study a model that jointly considers control of rare segments and different uplinks causing "choking," where high-uplink peers will not engage in certain transactions with low-uplink peers.Comment: 18 pages, 6 figures, A shorter version of this paper that did not include the N-segment lumped model was presented in May 2011 at IEEE ICC, Kyot

Social-aware hybrid mobile offloading

Mobile offloading is a promising technique to aid the constrained resources of a mobile device. By offloading a computational task, a device can save energy and increase the performance of the mobile applications. Unfortunately, in existing offloading systems, the opportunistic moments to offload a task are often sporadic and short-lived. We overcome this problem by proposing a social-aware hybrid offloading system (HyMobi), which increases the spectrum of offloading opportunities. As a mobile device is always co- located to at least one source of network infrastructure throughout of the day, by merging cloudlet, device-to-device and remote cloud offloading, we increase the availability of offloading support. Integrating these systems is not trivial. In order to keep such coupling, a strong social catalyst is required to foster user's participation and collaboration. Thus, we equip our system with an incentive mechanism based on credit and reputation, which exploits users' social aspects to create offload communities. We evaluate our system under controlled and in-the-wild scenarios. With credit, it is possible for a device to create opportunistic moments based on user's present need. As a result, we extended the widely used opportunistic model with a long-term perspective that significantly improves the offloading process and encourages unsupervised offloading adoption in the wild

Mathematical modeling of incentive policies in P2P systems.

Zhao, Qiao.Thesis (M.Phil.)--Chinese University of Hong Kong, 2009.Includes bibliographical references (leaves 35-36).Abstracts also in Chinese.Abstract --- p.iAcknowledgement --- p.vChapter 1 --- Introduction --- p.1Chapter 2 --- Model Description --- p.3Chapter 2.1 --- An Incentive Model for P2P Networks --- p.3Chapter 2.2 --- Learning Models for P2P Networks --- p.5Chapter 2.2.1 --- Current-best Learning Model (CBLM) --- p.5Chapter 2.2.2 --- Opportunistic Learning Model (OLM) --- p.6Chapter 2.3 --- Incentive Policies for P2P Networks --- p.7Chapter 2.3.1 --- Mirror Incentive Policy Vmirror --- p.8Chapter 2.3.2 --- Proportional Incentive Policy Vprop --- p.9Chapter 2.3.3 --- Linear Incentive Policy Class CLIP --- p.9Chapter 2.4 --- Performance and Robustness of Incentive Policies --- p.10Chapter 2.4.1 --- Robustness Analysis of Mirror Incentive Policy using the current-best learning method --- p.10Chapter 2.4.2 --- Robustness Analysis of Mirror Incentive Policy using the opportunistic learning method --- p.12Chapter 2.4.3 --- Robustness Analysis of Proportional Incentive Policy Using the current-best learning method --- p.12Chapter 2.4.4 --- Robustness Analysis of Proportional Incentive Policy Using the opportunistic learning method --- p.13Chapter 2.4.5 --- Robustness Analysis for Incentive Protocol in the Linear Incentive Class --- p.14Chapter 2.5 --- Connection with Evolutionary Game Theory --- p.17Chapter 3 --- Performance Evaluation --- p.21Chapter 3.1 --- Performance and Robustness of the Mirror Incentive Policy (Pmirror): --- p.21Chapter 3.2 --- Performance and Robustness of the Proportional Incentive Policy {Pprop): --- p.23Chapter 3.3 --- Performance and Robustness of incentive policy in the Linear Incentive Class (CLIP)： --- p.24Chapter 3.4 --- The Effect of Non-adaptive Peers: --- p.25Chapter 4 --- Adversary Effect of Altruism --- p.29Chapter 4.1 --- The Effect of Protocol Cost --- p.29Chapter 4.2 --- The Tradeoff between Altruism and System Robustness --- p.30Chapter 5 --- Related Work --- p.33Chapter 6 --- Conclusion --- p.34Bibliography --- p.3

A game theoretic approach to provide incentive and service differentiation in P2P networks.

Ma Tianbai.Thesis (M.Phil.)--Chinese University of Hong Kong, 2004.Includes bibliographical references (leaves 49-51).Abstracts in English and Chinese.Chapter 1 --- Introduction --- p.1Chapter 2 --- Incentive P2P System Overview --- p.6Chapter 3 --- Resource Distribution Mechanism --- p.11Chapter 4 --- Resource Competition Game --- p.22Chapter 4.1 --- Theoretical Competition Game --- p.22Chapter 4.2 --- Practical Competition Game Protocol --- p.26Chapter 5 --- Generalized Mechanism and Game --- p.33Chapter 5.1 --- Generalized Mechanism with Incentive --- p.33Chapter 5.2 --- Generalized Mechanism with Utility --- p.35Chapter 6 --- Experiments --- p.38Chapter 7 --- Conclusion --- p.4

Game theoretic approaches to cooperation in wireless networks

Ph.DDOCTOR OF PHILOSOPH