On The Difficulty of Finding the Nearest Peer in P2P Systems

Finding the nearest peer, in terms of latency, is an important problem in many Internet applications. In this paper, we argue that solutions that only examine inter-peer latencies as part of their operation will find it infeasible, in certain cases, to discover the nearest peer in P2P systems. The difficulty arises out of the way the last hop is typically laid out in the Internet, where a single PoP (point of presence) belonging to an ISP provides connectivity to numerous client networks. This setup leads to a large number of peers being at about the same latency from one another, which, we argue, presents a serious obstacle when a peer tries to discover another peer residing in the same network as itself. We use large-scale measurements over hosts in the Azureus P2P network and DNS servers to show that this condition does occur in real settings, and use simulations of the Meridian closest-server algorithm to show that the condition does indeed lead to difficulty in finding the exact-closest peer. We also propose a few heuristics that help address this issue

Substrate Support for Peer-to-Peer Applications

Peer-to-peer (P2P) applications in general, and unstructured applications in particular, have been very popular in the recent past. In this thesis, we identify common problems encountered in the course of developing different diverse peer-to-peer applications, and propose solutions to them. The broad problems we study are, namely, (i) load-balancing in heterogeneous unstructured P2P networks, where capacities to support load differ among the different members of the network, and application load is to be distributed in accordance with members' capacities to support it, and (ii) "extreme" nearest neighbor discovery in P2P networks, where the intent is to discover the latency-wise nearest peer in a P2P network, even when the nearest peer is in the same extended LAN or campus network. The goal of this thesis is to come up with a powerful set of basic mechanisms that the developers of many different P2P applications can reuse, rather than having to repeatedly reinvent the same solutions. We examine two main causes of load in unstructured networks: the underlying random graph, and the process of random node selection, where random peers are periodically picked to handle application load. We extensively evaluate different approaches to do heterogeneous random graph construction and peer selection, and identify our Swaplinks algorithm as the best approach. Swaplinks builds robust graphs where node degrees are close to their desired degrees, provides a good base to perform random peer selection, and is virtually free of tuning knobs, making it very practical to deploy. In our study of the extreme-nearest neighbor discovery problem, we identify and demonstrate a condition we call the clustering condition caused by the Internet last-hop architecture -- under the clustering condition, many different peers are located at about the same latency from one another, making it expensive for previous nearest-peer solutions to correctly find the nearest peer. We propose different solutions to overcome this problem, and show, using preliminary evaluations, that one of them is very attractive

On heterogeneous overlay construction and random node selection in unstructured p2p networks

Abstract — Unstructured p2p and overlay network applications often require that a random graph be constructed, and that some form of random node selection take place over that graph. A key and difficult requirement of many such applications is heterogeneity: peers have different node degrees in the random graph based on their capacity. Using simulations, this paper compares a number of techniques—some novel and some variations on known approaches—for heterogeneous graph construction and random node selection on top of such graphs. Our focus is on practical criteria that can lead to a genuinely deployable toolkit that supports a wide range of applications. These criteria include simplicity of operation, support for node heterogeneity, quality of random selection, efficiency and scalability, load balance, and robustness. We show that all these criteria can more-or-less be met by all the approaches. Our novel approach, however, stands out as the best from a practical perspective because of its simplicity: it achieves the criteria while requiring each node to set only a single tuning parameter, its desired relative load. I

KARMA: A Secure Economic Framework for Peer-to-Peer Resource Sharing

Peer-to-peer systems are typically designed around the assumption that all peers will willingly contribute resources to a global pool. They thus suffer from freeloaders, that is, participants who consume many more resources than they contribute. In this paper, we propose a general economic framework for avoiding freeloaders in peer-to-peer systems. Our system works by keeping track of the resource consumption and resource contribution of each participant. The overall standing of each participant in the system is represented by a single scalar value, called their karma. A set of nodes, called a bankset, keeps track of each node's karma, increasing it as resources are contributed, and decreasing it as they are consumed. Our framework is resistant to malicious attempts by the resource provider, consumer, and a fraction of the members of the bank set. We illustrate the application of this framework to a peer-to-peer filesharing application

Content Based Rate Estimation using Lazy Membership Testing

Fast IP flow rate estimation has many potential applications in network management, monitoring, security, and traffic engineering. Recently, low cost and memory efficient techniques to accurately estimate flow-rates in real-time have been developed. These techniques rely on flow definitions being constrained to being subsets of the fields in the packet header making flow-membership tests relatively inexpensive. In this paper, we consider a more general flow-rate estimation problem where flow membership testing is non-trivial and may involve more complex processing such as packet-payload based tests. An example is to estimate the amount of traffic that contains a given set of patterns (e.g., virus or worm signatures). We design new flow estimation techniques to reduce the number of membership tests. These techniques track pairs of arrivals that have the given property of interest and use lazy membership testing to avoid complex property testing unless absolutely necessary. The efficiency of the new schemes is evaluated by both analysis and simulation. I

Bitcoin meets strong consistency

The Bitcoin system only provides eventual consistency. For everyday life, the time to confirm a Bitcoin transaction is prohibitively slow. In this paper we propose a new sys-tem, built on the Bitcoin blockchain, which enables strong consistency. Our system, PeerCensus, acts as a certifica-tion authority, manages peer identities in a peer-to-peer network, and ultimately enhances Bitcoin and similar sys-tems with strong consistency. Our extensive analysis shows that PeerCensus is in a secure state with high probability. We also show how Discoin, a Bitcoin variant that decouples block creation and transaction confirmation, can be built on top of PeerCensus, enabling real-time payments. Unlike Bitcoin, once transactions in Discoin are committed, they stay committed