Live Streaming with Gossip

Peer-to-peer (P2P) architectures have emerged as a popular paradigm to support the dynamic and scalable nature of distributed systems. This is particularly relevant today, given the tremendous increase in the intensity of information exchanged over the Internet. A P2P system is typically composed of participants that are willing to contribute resources, such as memory or bandwidth, in the execution of a collaborative task providing a benefit to all participants. File sharing is probably the most widely used collaborative task, where each participant wants to receive an individual copy of some file. Users collaborate by sending fragments of the file they have already downloaded to other participants. Sharing files containing multimedia content, files that typically reach the hundreds of megabytes to gigabytes, introduces a number of challenges. Given typical bandwidths of participants of hundreds of kilobits per second to a couple of megabits per second, it is unacceptable to wait until completion of the download before actually being able to use the file as the download represents a non negligible time. From the point of view of the participant, getting the (entire) file as fast as possible is typically not good enough. As one example, Video on Demand (VoD) is a scenario where a participant would like to start previewing the multimedia content (the stream), offered by a source, even though only a fraction of it has been received, and then continue the viewing while the rest of the content is being received. Following the same line of reasoning, new applications have emerged that rely on live streaming: the source does not own a file that it wants to share with others, but shares content as soon as it is produced. In other words, the content to distribute is live, not pre-recorded and stored. Typical examples include the broadcasting of live sports events, conferences or interviews. The gossip paradigm is a type of data dissemination that relies on random communication between participants in a P2P system, sharing similarities with the epidemic dissemination of diseases. An epidemic starts to spread when the source randomly chooses a set of communication partners, of size fanout, and infects them, i.e., it shares a rumor with them. This set of participants, in turn, randomly picks fanout communication partners each and infects them, i.e., share with them the same rumor. This paradigm has many advantages including fast propagation of rumors, a probabilistic guarantee that each rumor reaches all participants, high resilience to churn (i.e., participants that join and leave) and high scalability. Gossip therefore constitutes a candidate of choice for live streaming in large-scale systems. These advantages, however, come at a price. While disseminating data, gossip creates many duplicates of the same rumor and participants usually receive multiple copies of the same rumor. While this is obviously a feature when it comes to guaranteeing good dissemination of the rumor when churn is high, it is a clear disadvantage when spreading large amounts of multimedia data (i.e., ordered and time-critical) to participants with limited resources, namely upload bandwidth in the case of high-bandwidth content dissemination. This thesis therefore investigates if and how the gossip paradigm can be used as a highly effcient communication system for live streaming under the following specific scenarios: (i) where participants can only contribute limited resources, (ii) when these limited resources are heterogeneously distributed among nodes, and (iii) where only a fraction of participants are contributing their fair share of work while others are freeriding. To meet these challenges, this thesis proposes (i) gossip++: a gossip-based protocol especially tailored for live streaming that separates the dissemination of metadata, i.e., the location of the data, and the dissemination of the data itself. By first spreading the location of the content to interested participants, the protocol avoids wasted bandwidth in sending and receiving duplicates of the payload, (ii) HEAP: a fanout adaptation mechanism that enables gossip to adapt participants' contribution with respect to their resources while still preserving its reliability, and (iii) LiFT: a protocol to secure high-bandwidth gossip-based dissemination protocols against freeriders

LiFTinG: Lightweight Freerider-Tracking Protocol in Gossip

This report presents LiFTinG, the first protocol to detect freeriders, including colluding ones, in gossip-based content dissemination systems with asymmetric data exchanges. LiFTinG relies on nodes tracking abnormal behaviors by cross-checking the history of their previous interactions, and exploits the fact that nodes pick neighbors at random to prevent colluding nodes from covering up each others' bad actions. We present a methodology to set the parameters of LiFTinG based on a theoretical analysis. In addition to simulations, we report on the deployment of LiFTinG on PlanetLab. In a 300-node system, where a stream of 674 kbps is broadcast, LiFTinG incurs a maximum overhead of only 8% while providing good results: for instance, with 10% of freeriders decreasing their contribution by 30%, LiFTinG detects 86% of the freeriders after only 30 seconds and wrongfully expels only a few honest nodes

Towards Fair Event Dissemination

Event dissemination in large scale dynamic systems is typically claimed to be best achieved using decentralized peer-to-peer architectures. The rationale is to have every participant in the system act both as a client (information consumer) and as a server (information dissemination enabler), thus, precluding specific brokers which would prevent scalability and fault-tolerance. We argue that, for such decentralized architectures to be really meaningful, participants should serve the system as much as they benefit from it. That is, the system should be fair in the sense that the extend to which a participant acts as a server should depend on the extend to which it has the opportunity to act as a client. This is particularly crucial in selective information dissemination schemes where clients are not all interested in the same information. In this position paper, we discuss what a notion of fairness could look like, explain why current architectures are not fair, and raise several challenges towards achieving fairness

Diffusion en directe avec du Gossip

Video streaming has become a killer application for peer-to-peer technologies. By aggregating scarce resources such as upload bandwidth, decentralized video streaming protocols make it possible to serve a video stream to huge numbers of users while requiring very limited investments from broadcasters. In this paper, we present HEAP, a novel peer-to-peer streaming protocol designed for heterogeneous scenarios. Gossip protocols have already shown their effec- tiveness in the context of live video streaming. HEAP, HEterogeneity-Aware gossip Protocol, goes beyond their applicability and performance by incorporating several novel features. First, HEAP includes a fanout-adaptation scheme that tunes the contribution of nodes to the streaming process based on their bandwidth capabilities. Second, HEAP comprises heuristics that improve reliability, as well as operation in the presence of heterogeneous network latency. We extensively evaluate HEAP on a real deployment over 200 nodes on the Grid5000 platform in a variety of settings, and assess its scalability with up to 100k simulated nodes. Our results show that HEAP significantly improves the quality of streamed videos over standard homogeneous gossip protocols, especially when the stream rate is close to the average available bandwidth.Le streaming vidéo est devenu une killer application pour les technologies pair-à- pair. En agrégeant les ressources rares telles que le debit maximale téléversement, les protocoles de diffusion vidéo décentralisée permettent servir un flux vidéo à un grand nombre d’utilisateurs tout en limitant les couts. Dans cet article, nous présentons HEAP, un nouveau protocole de streaming pair-à-pair conçu pour des réseaux hétérogènes. Les protocoles de gossip ont déjà mon- tré leur efficacité dans le contexte du streaming vidéo en direct. HEAP, em HEterogeneity-Aware Gossip Protocol, va au-delà de protocoles existantes en incorporant plusieurs caractéristiques nouvelles. Premièrement, HEAP adapte la contribution des noeuds en fonction de leurs debit maximal. Deuxièmement, HEAP inclut des heuristiques qui améliorent la fiabilité, en présence de latence de réseau hétérogène. Nous évaluons HEAP sur un déploiement réel sur 200 noeuds sur la plate-forme Grid5000 avec une variété de paramètres, et évaluons son passage à l’échelle avec jusqu’à 100k noeuds simulé. Nos résultats montrent que HEAP améliore significativement la qualité des vidéos diffusées par rapport au protocoles standard, surtout lorsque le débit est proche de la bande passante moyenne disponible

Decentralized Polling with Respectable Participants

International audienceWe consider the polling problem in a social network: participants express support for a given option and expect an outcome reflecting the opinion of the majority. Individuals in a social network care about their reputation: they do not want their vote to be disclosed or any potential misbehavior to be publicly exposed. We exploit this social aspect of users to model dishonest behavior, and show that a simple secret sharing scheme, combined with lightweight verification procedures, enables private and accurate polling without requiring any central authority or cryptography. We present DPol, a simple and scalable distributed polling protocol in which misbehaving nodes are exposed with positive probability and in which the probability of honest participants having their privacy violated is traded off with the impact of dishonest participants on the accuracy of the polling result. The trade-off is captured by a generic parameter of the protocol, an integer k called the privacy parameter. In a system of N nodes with B dishonest participants, the probability of disclosing a participant's vote is bounded by (B/N)^{k+1}, whereas the impact on the score of each polling option is at most (3k+2) B with high probability when dishonest users are a minority (i.e., B < N/2), assuming nodes are uniformly spread across groups used by the system. When dishonest users are few (i.e., B < sqrt{N}), the impact bound holds deterministically and our protocol is asymptotically accurate: there is negligible difference between the true result score of the poll and the outcome of our protocol. To demonstrate the practicality of DPol, we report on its deployment on 400 PlanetLab nodes. The relative error of the polling result is less than 10% when faced with the message loss, crashes and delays inherent in PlanetLab. Our experiments show that the impact on the score of each polling option by dishonest nodes is (2k+1) B on average, consistently lower that the theoretical bound of (3k+2) B

Frugal Mobile Objects

This paper presents a computing model for resource-limited mobile devices. The originality of the model lies in the integration of a strongly-typed event-based communication paradigm with abstractions for frugal control, assuming a small footprint runtime. With our model, an application consists of a set of distributed reactive objects, called FROBs, that communicate through typed events and dynamically adapt their behavior reacting to notifications typically based on resource availability. FROBs have a logical time-slicing execution pattern that helps monitor resource consuming tasks and determine resource profiles in terms of CPU, memory, and bandwidth. The behavior of a FROB is represented by a set of stateless first-class objects. Both state and behavioral objects are referenced through a level of indirection within the FROB. This facilitates the dynamic changes of the set of event types a FROB can accept, say based on the available resources, without requiring a significant footprint of the underlying FROB runtime. We demonstrate the usability of the FROB model through our Java-based prototype and a peer-to-peer audio streaming scenario where an audio provider dynamically adjusts its quality of service by adapting to demand. The performance results of our prototype convey the small footprint of our FROB runtime (86 kilobytes). We also augmented the KVM to enable resource profiling with however a negligible overhead (less than 0.5%) and a decrease in speed of the virtual machine of at most 7%

Looking Ahead in Open Multithreaded Transactions

Open multithreaded transactions constitute building blocks that allow a developer to design and structure the execution of complex distributed systems featuring cooperative and competitive concurrency in a reliable way. In this paper we describe an optimization to the standard open multithreaded transaction model that does not impose any participant synchronization when committing a transaction, but still provides the same execution semantics. This optimization – letting participants “look ahead” and continue their execution on the outside of the transaction – makes it possible to speed up the execution of in individual transaction with multiple participants tremendously. The paper describes all technical issues that had to be solved, e.g. adapting concurrency control of transactional objects to be look-ahead aware, adapting joining rules for lookahead participants, and re-defining exception handling in the presence of look-ahead

On Tracking Freeriders in Gossip Protocols

Peer-to-peer content dissemination applications suffer immensely from freeriders, i.e., nodes that do not provide their fair share. The Tit-for-Tat (TfT) incentives have received much attention as they help make such systems more robust against freeriding. However, these rely on an asymmetric component, namely opportunistic pushes, that let peers receive content without sending anything in return. Opportunistic push constitutes the Achilles ’ heel of TfTbased protocols as illustrated by the fact that all known attacks against them exploit it. This problem becomes even more serious when used by colluding freeriders. In this paper, we discuss the possibility of using accountability to secure gossip-based dissemination protocols based on asymmetric exchanges. The fact that gossip protocols are dynamic and randomized makes our approach robust against collusion and alleviates the need for cryptography. We present the challenges raised by an auditing approach and give insights into how to build a freeridertracking protocol for gossip-based content dissemination. 1