Mode-Suppression: A Simple, Stable and Scalable Chunk-Sharing Algorithm for P2P Networks

The ability of a P2P network to scale its throughput up in proportion to the arrival rate of peers has recently been shown to be crucially dependent on the chunk sharing policy employed. Some policies can result in low frequencies of a particular chunk, known as the missing chunk syndrome, which can dramatically reduce throughput and lead to instability of the system. For instance, commonly used policies that nominally "boost" the sharing of infrequent chunks such as the well known rarest-first algorithm have been shown to be unstable. Recent efforts have largely focused on the careful design of boosting policies to mitigate this issue. We take a complementary viewpoint, and instead consider a policy that simply prevents the sharing of the most frequent chunk(s). Following terminology from statistics wherein the most frequent value in a data set is called the mode, we refer to this policy as mode-suppression. We also consider a more general version that suppresses the mode only if the mode frequency is larger than the lowest frequency by a fixed threshold. We prove the stability of mode-suppression using Lyapunov techniques, and use a Kingman bound argument to show that the total download time does not increase with peer arrival rate. We then design versions of mode-suppression that sample a small number of peers at each time, and construct noisy mode estimates by aggregating these samples over time. We show numerically that the variants of mode-suppression yield near-optimal download times, and outperform all other recently proposed chunk sharing algorithms

A New Stable Peer-to-Peer Protocol with Non-persistent Peers

Recent studies have suggested that the stability of peer-to-peer networks may rely on persistent peers, who dwell on the network after they obtain the entire file. In the absence of such peers, one piece becomes extremely rare in the network, which leads to instability. Technological developments, however, are poised to reduce the incidence of persistent peers, giving rise to a need for a protocol that guarantees stability with non-persistent peers. We propose a novel peer-to-peer protocol, the group suppression protocol, to ensure the stability of peer-to-peer networks under the scenario that all the peers adopt non-persistent behavior. Using a suitable Lyapunov potential function, the group suppression protocol is proven to be stable when the file is broken into two pieces, and detailed experiments demonstrate the stability of the protocol for arbitrary number of pieces. We define and simulate a decentralized version of this protocol for practical applications. Straightforward incorporation of the group suppression protocol into BitTorrent while retaining most of BitTorrent's core mechanisms is also presented. Subsequent simulations show that under certain assumptions, BitTorrent with the official protocol cannot escape from the missing piece syndrome, but BitTorrent with group suppression does.Comment: There are only a couple of minor changes in this version. Simulation tool is specified this time. Some repetitive figures are remove

Spatial Interactions of Peers and Performance of File Sharing Systems

We propose a new model for peer-to-peer networking which takes the network bottlenecks into account beyond the access. This model allows one to cope with key features of P2P networking like degree or locality constraints or the fact that distant peers often have a smaller rate than nearby peers. We show that the spatial point process describing peers in their steady state then exhibits an interesting repulsion phenomenon. We analyze two asymptotic regimes of the peer-to-peer network: the fluid regime and the hard--core regime. We get closed form expressions for the mean (and in some cases the law) of the peer latency and the download rate obtained by a peer as well as for the spatial density of peers in the steady state of each regime, as well as an accurate approximation that holds for all regimes. The analytical results are based on a mix of mathematical analysis and dimensional analysis and have important design implications. The first of them is the existence of a setting where the equilibrium mean latency is a decreasing function of the load, a phenomenon that we call super-scalability.Comment: No. RR-7713 (2012

Pricing and Equilibrium Analysis of Network Market Systems

Markets have been the most successful method of identifying value of goods and services. Both large and small scale markets have gradually been moving into the Internet domain, with increasingly large numbers of diverse participants. In this dissertation, we consider several problems pertaining to equilibria in networked marketplaces under different application scenarios and market sizes. We approach the question of pricing and market design from two perspectives. On the one hand, we desire to understand how self-interested market participants would set prices and respond to prices resulting in certain allocations. On the other hand, we wish to evaluate how best to allocate resources so as to attain efficient equilibria. There might be a gap between these viewpoints, and characterizing this gap is desirable. Our technical approaches follow the number of market participants, and the nature of trades happening in the market. In our first problem, we consider a market of providing communication services at the level of providing Internet transit. Here, the transit Internet Service Provider (ISP) must determine billing volumes and set prices for its customers who are _rms that are content providers, sinks, or subsidiary ISPs. Demand from these customers is variable, and they have different impacts on the resources that the transit ISP needs to provision. Using measured data from several networks, we design a fair and flexible billing scheme that correctly identifies the impact of each customer on the amount of provisioning needed. While the customer set in the first problem is finite, many marketplaces deal with a very large number of agents that each have ephemeral lifetimes. Here, agents arrive, participate in the market for some time, and then vanish. We consider two such markets in such a regime. The first is one of apps on mobile devices that compete against each other for cellular data service, while the second is on service marketplaces wherein many providers compete with each other for jobs that consider both prices and provider reputations while making choices between them. Our goal is to show that a Mean Field Game can be used to accurately approximate these systems, determine how prices are set, and characterize the nature of equilibria in such markets. Finally, we consider efficiency metrics in large scale resource sharing networks in which bilateral exchange of resources is the norm. In particular, we consider peer-to-peer (P2P) file sharing under which peers obtain chunks of a file from each other. Here, contrary to the intuition that chunks must be shared whenever one peer has one of value to another, we show that a measure of suppression is needed to utilize resources efficiently. In particular, we propose a simple and stable algorithm entitled Mode suppression that attains near optimal file sharing times by disallowing the sharing of the most frequent chunks in the system

Pricing and Equilibrium Analysis of Network Market Systems

Markets have been the most successful method of identifying value of goods and services. Both large and small scale markets have gradually been moving into the Internet domain, with increasingly large numbers of diverse participants. In this dissertation, we consider several problems pertaining to equilibria in networked marketplaces under different application scenarios and market sizes. We approach the question of pricing and market design from two perspectives. On the one hand, we desire to understand how self-interested market participants would set prices and respond to prices resulting in certain allocations. On the other hand, we wish to evaluate how best to allocate resources so as to attain efficient equilibria. There might be a gap between these viewpoints, and characterizing this gap is desirable. Our technical approaches follow the number of market participants, and the nature of trades happening in the market. In our first problem, we consider a market of providing communication services at the level of providing Internet transit. Here, the transit Internet Service Provider (ISP) must determine billing volumes and set prices for its customers who are _rms that are content providers, sinks, or subsidiary ISPs. Demand from these customers is variable, and they have different impacts on the resources that the transit ISP needs to provision. Using measured data from several networks, we design a fair and flexible billing scheme that correctly identifies the impact of each customer on the amount of provisioning needed. While the customer set in the first problem is finite, many marketplaces deal with a very large number of agents that each have ephemeral lifetimes. Here, agents arrive, participate in the market for some time, and then vanish. We consider two such markets in such a regime. The first is one of apps on mobile devices that compete against each other for cellular data service, while the second is on service marketplaces wherein many providers compete with each other for jobs that consider both prices and provider reputations while making choices between them. Our goal is to show that a Mean Field Game can be used to accurately approximate these systems, determine how prices are set, and characterize the nature of equilibria in such markets. Finally, we consider efficiency metrics in large scale resource sharing networks in which bilateral exchange of resources is the norm. In particular, we consider peer-to-peer (P2P) file sharing under which peers obtain chunks of a file from each other. Here, contrary to the intuition that chunks must be shared whenever one peer has one of value to another, we show that a measure of suppression is needed to utilize resources efficiently. In particular, we propose a simple and stable algorithm entitled Mode suppression that attains near optimal file sharing times by disallowing the sharing of the most frequent chunks in the system