An online backup system should be quick and reliable in both saving and restoring users' data. To do so in a peer-to-peer implementation, data transfer scheduling and the amount of redundancy must be chosen wisely. We formalize the problem of exchanging multiple pieces of data with intermittently available peers, and we show that random scheduling completes transfers nearly optimally in terms of duration as long as the system is sufficiently large. Moreover, we propose an adaptive redundancy scheme that improves performance and decreases resource usage while keeping the risks of data loss low. Extensive simulations show that our techniques are effective in a realistic trace-driven scenario with heterogeneous bandwidth.
A recurrent problem for P2P applications is creating incentives to encourage nodes in contributing more resources. This can be done via reputation systems @cite_4 or virtual currency @cite_8 . Specifically for storage systems, an easy and efficient solution is segregating nodes in sub-networks with roughly homogeneous characteristics such as uptime and storage space @cite_11 @cite_1 .
@cite_1 @cite_11 @cite_4 @cite_8
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In this paper we model and study the performance of peer-to-peer (P2P) file sharing systems in terms of their 'service capacity'. We identify two regimes of interest: the transient and stationary regimes. We show that in both regimes, the performance of P2P systems exhibits a favorable scaling with the offered load. P2P systems achieve this by efficiently leveraging the service capacity of other peers, who possibly are concurrently downloading the same file. Therefore to improve the performance, it is important to design mechanisms to give peers incentives for sharing cooperation. One approach is to introduce mechanisms for resource allocation that are 'fair', such that a peer's performance improves with his contributions. We find that some intuitive 'fairness' notions may unexpectedly lead to 'unfair' allocations, which do not provide the right incentives for peers. Thus, implementation of P2P systems may want to compromise the degree of 'fairness' in favor of maintaining system robustness and reducing overheads. An important issue in Peer-to-Peer networks is to encourage users to share with others as they use the resources of the network. However, some nodes may only consume from users without giving anything in return. To fix this problem, we can incorporate trust management systems with network infrastructures. Current trust managements are usually made for unstructured overlays and have several shortcomings. They are made to be very similar to e-commerce scoring websites which may not be the best design for fairness in P2P networks. Several problems may arise with their designs such as difficulties to provide a complete history of freeloaders or lack of an autonomous removal mechanism in case of severe attacks. In this paper, we argue that such systems can be deployed more efficiently by using a structured paradigm. For this purpose, we propose C-Trust, a trust management system which is focused on fairness for P2P networks. This is done by getting help from current circular structured designs. This method is able to mark freeloaders, identify their severity of abusion and punish them accordingly. We are also able to effectively protect both Seeder-to-Leecher and Leecher-to-Leecher transactions. This feature is specially important for fairness which other trust systems have not considered so far. Many large decentralized systems rely on information propagation to ensure their proper function. We examine a common scenario in which only participants that are aware of the information can compete for some reward, and thus informed participants have an incentive not to propagate information to others. One recent example in which such tension arises is the 2009 DARPA Network Challenge (finding red balloons). We focus on another prominent example: Bitcoin, a decentralized electronic currency system. Bitcoin represents a radical new approach to monetary systems. It has been getting a large amount of public attention over the last year, both in policy discussions and in the popular press. Its cryptographic fundamentals have largely held up even as its usage has become increasingly widespread. We find, however, that it exhibits a fundamental problem of a different nature, based on how its incentives are structured. We propose a modification to the protocol that can eliminate this problem. Bitcoin relies on a peer-to-peer network to track transactions that are performed with the currency. For this purpose, every transaction a node learns about should be transmitted to its neighbors in the network. As the protocol is currently defined and implemented, it does not provide an incentive for nodes to broadcast transactions they are aware of. In fact, it provides an incentive not to do so. Our solution is to augment the protocol with a scheme that rewards information propagation. Since clones are easy to create in the Bitcoin system, an important feature of our scheme is Sybil-proofness. We show that our proposed scheme succeeds in setting the correct incentives, that it is Sybil-proof, and that it requires only a small payment overhead, all this is achieved with iterated elimination of dominated strategies. We complement this result by showing that there are no reward schemes in which information propagation and no self-cloning is a dominant strategy. In an ad-hoc network, intermediate nodes on a communication path are expected to forward packets of other nodes so that the mobile nodes can communicate beyond their wireless transmission range. However, because wireless mobile nodes are usually constrained by limited power and computation resources, a selfish node may be unwilling to spend its resources in forwarding packets which are not of its direct interest, even though it expects other nodes to forward its packets to the destination. It has been shown that the presence of such selfish nodes degrades the overall performance of a non-cooperative ad hoc network. To address this problem, we propose a secure and objective reputation-based incentive (SORI) scheme to encourage packet forwarding and discipline selfish behavior. Different from the existing schemes, under our approach, the reputation of a node is quantified by objective measures, and the propagation of reputation is efficiently secured by a one-way-hash-chain-based authentication scheme. Armed with the reputation-based mechanism, we design a punishment scheme to penalize selfish nodes. The experimental results show that the proposed scheme can successfully identify selfish nodes and punish them accordingly. Peer-to-peer storage systems assume that their users consume resources in proportion to their contribution. Unfortunately, users are unlikely to do this without some enforcement mechanism. Prior solutions to this problem require centralized infrastructure, constraints on data placement, or ongoing administrative costs. All of these run counter to the design philosophy of peer-to-peer systems.Samsara enforces fairness in peer-to-peer storage systems without requiring trusted third parties, symmetric storage relationships, monetary payment, or certified identities. Each peer that requests storage of another must agree to hold a claim in return---a placeholder that accounts for available space. After an exchange, each partner checks the other to ensure faithfulness. Samsara punishes unresponsive nodes probabilistically. Because objects are replicated, nodes with transient failures are unlikely to suffer data loss, unlike those that are dishonest or chronically unavailable. Claim storage overhead can be reduced when necessary by forwarding among chains of nodes, and eliminated when cycles are created. Forwarding chains increase the risk of exposure to failure, but such risk is modest under reasonable assumptions of utilization and simultaneous, persistent failure. Multiplayer games played over the Internet have become very popular in the lastfew years. An interesting subcategory are the so-called massively multiplayeronline games (MMOGs) that allow thousands of player characters to share asingle game world. Such a world is usually run on a high-performance and high-availability server cluster. However, even with games that have been extensivelybeta-tested, downtimes of several hours because of hard- or software failures arenot uncommon. Downtimes, especially in the ﬁrst few weeks after the release,can negatively aﬀect the image of the game and the company that created it.Traditionally, a cluster of servers contains one virtual world of a MMOG.Such infrastructure is inﬂexible and error-prone. One would rather like to havea system that allows disconnecting a server at runtime while others take overits tasks. Server-based MMOGs can have performance problems if players areconcentrated in certain parts of the game world or some worlds are overpopu-lated. Thus, there is also a need for load balancing mechanisms. Peer-to-Peer(P2P) systems quite naturally support the use of load balancing.In this paper we use a structured P2P technology for the organization of theinfrastructure and thus for the reduction of downtimes in MMOGs. We splitthe game world in disjunctive rectangular zones and distribute them on diﬀerentnodes of the P2P network.Online games are an interesting challenge and chance for the future devel-opment of the P2P paradigm. A wide variety of aspects of only theoreticallysolved and especially yet completely unsolved problems are covered by this ap-plication. Security and trust problems appear as well as the need to preventcheating. The application is not as tolerant to faults as instant messaging or ﬁlesharing. Consistent data storage is a problem, decisions and transactions haveto be performed in a decentralized way. Moreover, the P2P network is not usedas pure lookup service, but more as a communication and application-speciﬁcsocial structure.The rest of this paper is organized as follows: First we discuss related work inSection 2 and give a brief introduction to P2P and MMOGs and their challengesin Section 3. Section 4 shows our approach to use structured P2P Systems forMMOGs and section 5 the evaluation with player traces from a real MMOG.Finally, Section 6 provides conclusions. In this paper, we focus on the problem of maintaining significant levels of cooperation in peer-to-peer (P2P) networks of selfish adaptive peers. We propose a simple algorithm that maintains high levels of cooperation in such a network while performing the collective task of file sharing. The algorithm is adapted from novel "tag" models of cooperation that do not rely on explicit reciprocity, reputation or trust mechanisms. A sequence of three simulation models is presented-starting with an abstract model of tag-based cooperation (TagWorld) and finishing with a P2P file-sharing model (FileWorld) that puts the technique to work. From analysis of extensive computer simulations, we demonstrate the technique to be scalable, robust, and decentralized; it requires no central servers or authorities. The algorithm is relatively simple: peers do not need to store additional trust information about other nodes or to perform significant additional processing. We address content discovery in wireless networks with infrastructure, where mobile nodes store, advertise, and consume content while Broker entities running on infrastructure devices let demand and offer meet. We refer to this paradigm as match-making, highlighting its features within the confines of the standard publish-and-subscribe paradigm. We study its performance in terms of success probability of a content query, a parameter that we strive to increase by acting as follows: 1) We design a credit-based scheme that makes it convenient for rational users to provide their content (thus discouraging free-riding behavior), and it guarantees them a fair treatment. 2) We increase the availability of either popular or rare content, through an efficient caching scheme. 3) We counter malicious nodes whose objective is to disrupt the system performance by not providing the content they advertise. To counter the latter as well as free riders, we introduce a feedback mechanism that enables a Broker to tell apart well- and misbehaving nodes in a very reliable manner, and to ban the latter. The properties of our match-making scheme are analyzed through game theory. Furthermore, via ns-3 simulations, we show its resilience to different attacks by malicious users and its good performance with respect to other existing solutions. Distributed cloud applications, including batch processing, streaming, and scale-out databases, generate a significant amount of network traffic and a considerable fraction of their runtime is due to network activity. This paper initiates the study of deterministic algorithms for collocating frequently communicating nodes in a distributed networked systems in an online fashion. In particular, we introduce the Balanced RePartitioning (BRP) problem: Given an arbitrary sequence of pairwise communication requests between n nodes, with patterns that may change over time, the objective is to dynamically partition the nodes into ( ) clusters, each of size k, at a minimum cost. Every communication request needs to be served: if the communicating nodes are located in the same cluster, the request is served locally, at cost 0; if the nodes are located in different clusters, the request is served remotely using inter-cluster communication, at cost 1. The partitioning can be updated dynamically (i.e., repartitioned), by migrating nodes between clusters at cost ( ) per node migration. The goal is to devise online algorithms which find a good trade-off between the communication and the migration cost, i.e., “rent” or “buy”, while maintaining partitions which minimize the number of inter-cluster communications. BRP features interesting connections to other well-known online problems. In particular, we show that scenarios with ( =2 ) generalize online paging, and scenarios with (k=2 ) constitute a novel online version of maximum matching. We consider settings both with and without cluster-size augmentation. Somewhat surprisingly (and unlike online paging), we prove that any deterministic online algorithm has a competitive ratio of at least k, even with augmentation. Our main technical contribution is an (O(k k ) )-competitive deterministic algorithm for the setting with (constant) augmentation. This is attractive as, in contrast to ( ), k is likely to be small in practice. For the case of matching ( (k=2 )), we present a constant competitive algorithm that does not rely on augmentation. Peer-to-peer (P2P) systems show numerous advantages over centralized systems, such as load balancing, scalability, and fault tolerance, and they require certain functionality, such as search, repair, and message and data transfer. In particular, structured P2P networks perform an exact search in logarithmic time proportional to the number of peers. However, keyword similarity search in a structured P2P network remains a challenge. Similarity search for service discovery can significantly improve service management in a distributed environment. As services are often described informally in text form, keyword similarity search can find the required services or data items more reliably. This paper presents a fast similarity search algorithm for structured P2P systems. The new algorithm, called P2P fast similarity search (P2PFastSS), finds similar keys in any distributed hash table (DHT) using the edit distance metric, and is independent of the underlying P2P routing algorithm. Performance analysis shows that P2PFastSS carries out a similarity search in time proportional to the logarithm of the number of peers. Simulations on PlanetLab confirm these results and show that a similarity search with 34,000 peers performs in less than three seconds on average. Thus, P2PFastSS is suitable for similarity search in large-scale network infrastructures, such as service description matching in service discovery or searching for similar terms in P2P storage networks. In citepapa, Papadimitriou formalized the notion of routing stability in BGP as the following coalitional game theoretic problem: Given a network with a multicommodity flow satisfying node capacity and demand constraints, the payoff of a node is the total flow originated or terminated at it. A payoff allocation is in the core if and only if there is no subset of nodes that can increase their payoff by seceding from the network. We answer one of the open problems in citepapa by proving that for any network, the core is non-empty in both the transferable (where the nodes can compensate each other with side payments) and the non-transferable case. In the transferable case we show that such an allocation can be computed in polynomial time. We also generalize this result to the case where a strictly concave utility function is associated with each commodity. Motivated by the study of peer-to-peer file swarming systems a la BitTorrent, we introduce a probabilistic model of coupon replication systems. These systems consist of users aiming to complete a collection of distinct coupons. Users enter the system with an initial coupon provided by a bootstrap server, acquire other coupons from other users, and leave once they complete their coupon collection. For open systems, with exogenous user arrivals, we derive stability condition for a layered scenario, where encounters are between users holding the same number of coupons. We also consider a system where encounters are between users chosen uniformly at random from the whole population. We show that sojourn time in both systems is asymptotically optimal as the number of coupon types becomes large. We also consider closed systems with no exogenous user arrivals. In a special scenario where users have only one missing coupon, we evaluate the size of the population ultimately remaining in the system, as the initial number of users N goes to infinity. We show that this size decreases geometrically with the number of coupons K. In particular, when the ratio K log(N) is above a critical threshold, we prove that this number of leftovers is of order log(log(N)). These results suggest that, under the assumption that the bootstrap server is not a bottleneck, the performance does not depend critically on either altruistic user behavior or on load-balancing strategies such as rarest first. One of the main reasons of the recent success of peer to peer (P2P)file sharing systems such as BitTorrent is their built-in tit-for-tat mechanism. In this paper, we model the bandwidth allocation in a P2P system as an exchange economy and study a tit-for-tat dynamics, namely the proportional response dynamics, in this economy. In aproportional response dynamics each player distributes its good to its neighbors proportional to the utility it received from them in thelast period. We show that this dynamics not only converges but converges to a market equilibrium, a standard economic characterization of efficient exchanges in a competitive market. In addition, for some classes of utility functions we consider, it converges much faster than the classical tat process and any existingalgorithms for computing market equilibria. As a part of our proof we study the double normalization of a matrix, an operation that linearly scales the rows of a matrix sothat each row sums to a prescribed positive number, followed by a similar scaling of the columns. We show that the iterative double normalization process of any non-negative matrix always converges. This complements the previous studies in matrix scaling that has focused on the convergence condition of the process when the row and column normalizations are considered as separate steps. We study the 'service capacity' of peer to peer (P2P) file sharing applications. We begin by considering a transient regime which is key to capturing the ability of such systems to handle bursty traffic, e.g., flash crowds. In this context our models, based on age dependent branching processes, exhibit exponential growth in service capacity, and permit the study of sensitivity of this growth to system policies and parameters. Then we consider a model for such systems in steady state and show how the average delay seen by peers would scale in the offered load and rate at which peers exit the system. We find that the average delays scale well in the offered load. In particular the delays are upper bounded by some constant given any offered load and even decrease in the offered load if peers exit the system slowly. We validate many of our findings by analyzing traces obtained from a second generation P2P application called BitTorrent. Even though they were introduced only a few years ago, peer-to-peer (P2P) filesharing systems are now one of the most popular Internet applications and have become a major source of Internet traffic. Thus, it is extremely important that these systems be scalable. Unfortunately, the initial designs for P2P systems have significant scaling problems; for example, Napster has a centralized directory service, and Gnutella employs a flooding based search mechanism that is not suitable for large systems. In a Peer-to-Peer (P2P) file-sharing system, a node finds and retrieves its desired file. If multiple nodes cache the same file to provide others, we can achieve a dependable file-sharing system with low latency and high file availability. However, a node has to spend costs, e.g., processing load or storage capacity, on caching a file. Consequently, a node may selfishly behave and hesitate to cache a file. In such a case, unpopular files are likely to disappear from the system. In this paper, we aim to reveal whether effective caching in the whole system emerges from autonomous and selfish node behavior. We discuss relationship between selfish node behavior and system dynamics by using evolutionary game theory. Through theoretic analysis, we show that a file-sharing system can be robust to file disappearance depending on a cost and demand model for caching even if nodes behave selfishly. Furthermore, we also conduct several simulation-based analysis in terms of network structures, evolving network, load balancing, and system stability. As a result, we demonstrate that a file-sharing system with good properties, i.e., robustness to file disappearance, low search latency, well load-balancing, and high stability, can be achieved independent of network structures and dynamics. Some misbehavior detection and reputation systems in mobile ad-hoc networks rely on the dissemination of information of observed behavior, which makes them vulnerable to false accusations. This vulnerability could be removed by forbidding the dissemination of information on observed behavior in the first place, but, as we show here, this has more drawbacks than a solution that allows dissemination and copes with false accusations. We propose a method for reducing the impact of false accusations. In our approach, nodes collect first-hand information about the behavior of other nodes by direct observation. In addition, nodes maintain a rating about every other node that they care about, in the form of a continuous variable per node. From time to time nodes exchange their first-hand information with others, but, using the Bayesian approach we designed and present in this paper, only second-hand information that is not incompatible with the current rating is accepted. Ratings are slightly modified by accepted information. The reputation of a given node is the collection of ratings maintained by others about this node. By means of simulation we evaluated the robustness of our approach against several types of adversaries that spread false information, and its efficiency at detecting malicious nodes. The simulation results indicate that our system largely reduces the impact of false accusations, while still benefiting from the accelerated detection of malicious nodes provided by second-hand information. We also found that when information dissemination is not used, the time until malicious nodes are detected can be unacceptable. We introduce autonomous gossiping (A G), a new genre epidemic algorithm for selective dissemination of information in contrast to previous usage of epidemic algorithms which flood the whole network. A G is a paradigm which suits well in a mobile ad-hoc networking (MANET) environment because it does not require any infrastructure or middleware like multicast tree and (un)subscription maintenance for publish subscribe, but uses ecological and economic principles in a self-organizing manner in order to achieve any arbitrary selectivity (flexible casting). The trade-off of using a stateless self-organizing mechanism like A G is that it does not guarantee completeness deterministically as is one of the original objectives of alternate selective dissemination schemes like publish subscribe. We argue that such incompleteness is not a problem in many non-critical real-life civilian application scenarios and realistic node mobility patterns, where the overhead of infrastructure maintenance may outweigh the benefits of completeness, more over, at present there exists no mechanism to realize publish subscribe or other paradigms for selective dissemination in MANET environments. Mobile ad-hoc networks rely on the cooperation of nodes for routing and forwarding. For individual nodes there are however several advantages resulting from noncooperation, the most obvious being power saving. Nodes that act selfishly or even maliciously pose a threat to availability in mobile adhoc networks. Several approaches have been proposed to detect noncooperative nodes. In this paper, we investigate the effect of using rumors with respect to the detection time of misbehaved nodes as well as the robustness of the reputation system against wrong accusations. We propose a Bayesian approach for reputation representation, updates, and view integration. We also present a mechanism to detect and exclude potential lies. The simulation results indicate that by using this Bayesian approach, the reputation system is robust against slander while still benefitting from the speed-up in detection time provided by the use of rumors. Peer-to-peer (P2P) communication in networks for file distribution and other applications is a powerful multiplier of network utility, due to its ability to exploit parallelism in a distributed way. As new variations are engineered, to provide less impact on service providers and to provide better quality of service, it is important to have a theoretical underpinning, to weigh the effectiveness of various methods for enhancing the service. This paper focuses on the stationary portion of file download in an unstructured P2P network, which typically follows for many hours after a flash crowd initiation. The contribution of the paper is to identify how much help is needed from the seeds, either fixed seeds or peers dwelling in the system after obtaining the complete file, to stabilize the system. It is shown that dominant cause for instability is the missing piece syndrome, whereby one piece becomes very rare in the network. It is shown that very little dwell time is necessary--even if there is very little help from a fixed seed, peers need to dwell on average no longer than it takes to upload one additional piece, after they have obtained a complete collection.