The success of a P2P file-sharing network highly depends on the scalability and versatility of its search mechanism. Two particularly desirable search features are scope (ability to find infrequent items) and support for partial-match queries (queries that contain typos or include a subset of keywords). While centralized-index architectures (such as Napster) can support both these features, existing decentralized architectures seem to support at most one: prevailing unstructured P2P protocols (such as Gnutella and FastTrack) deploy a "blind" search mechanism where the set of peers probed is unrelated to the query; thus they support partial-match queries but have limited scope. On the other extreme, the recently-proposed distributed hash tables (DHTs) such as CAN and CHORD, couple index location with the item's hash value, and thus have good scope but can not effectively support partial-match queries. Another hurdle to DHTs deployment is their tight control of the overlay structure and the information (part of the index) each peer maintains, which makes them more sensitive to failures and frequent joins and disconnects.

The study of complex networks has emerged over the past several years as a theme spanning many disciplines, ranging from mathematics and computer science to the social and biological sciences. A significant amount of recent work in this area has focused on the development of random graph models that capture some of the qualitative properties observed in large-scale network data; such models have the potential to help us reason, at a general level, about the ways in which real-world networks are organized. We survey one particular line of network research, concerned with small-world phenomena and decentralized search algorithms, that illustrates this style of analysis. We begin by describing a well-known experiment that provided the first empirical basis for the "six degrees of separation" phenomenon in social networks; we then discuss some probabilistic network models motivated by this work, illustrating how these models lead to novel algorithmic and graph-theoretic questions, and how they are supported by recent empirical studies of large social networks.

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Abstract—A “botnet ” consists of a network of compromised computers controlled by an attacker (“botmaster”). Recently botnets have become the root cause of many Internet attacks. To be well prepared for future attacks, it is not enough to study how to detect and defend against the botnets that have appeared in the past. More importantly, we should study advanced botnet designs that could be developed by botmasters in the near future. In this paper, we present the design of an advanced hybrid peer-to-peer botnet. Compared with current botnets, the proposed botnet is harder to be shut down, monitored, and hijacked. It provides robust network connectivity, individualized encryption and control traffic dispersion, limited botnet exposure by each bot, and easy monitoring and recovery by its botmaster. In the end, we suggest and analyze several possible defenses against this advanced botnet. Index Terms—Botnet, peer-to-peer, robustness, honeypot

Distributed hash tables (DHTs) provide scalable, key-based lookup of objects in dynamic network environments. Although DHTs have been studied extensively from an analytical perspective, only recently have wide deployments enabled empirical examination. This paper reports measurement results obtained from profiling the Azureus BitTorrent client’s DHT, which is in active use by more than 1 million nodes on a daily basis. The Azureus DHT operates on untrusted, unreliable end-hosts, offering a glimpse into the implementation challenges associated with making structured overlays work in practice. Our measurements provide characterizations of churn, overhead, and performance in this environment. We leverage these measurements to drive the design of a modified DHT lookup algorithm that reduces median DHT lookup time by an order of magnitude for a nominal increase in overhead. 1.

Abstract. Cloud computing providers have setup several data centers at different geographical locations over the Internet in order to optimally serve needs of their customers around the world. However, existing systems do not support mechanisms and policies for dynamically coordinating load distribution among different Cloud-based data centers in order to determine optimal location for hosting application services to achieve reasonable QoS levels. Further, the Cloud computing providers are unable to predict geographic distribution of users consuming their services, hence the load coordination must happen automatically, and distribution of services must change in response to changes in the load. To counter this problem, we advocate creation of federated Cloud computing environment (InterCloud) that facilitates just-in-time, opportunistic, and scalable provisioning of application services, consistently achieving QoS targets under variable workload, resource and network conditions. The overall goal is to create a computing environment that supports dynamic expansion or contraction of capabilities (VMs, services, storage, and database) for handling sudden variations in service demands. This paper presents vision, challenges, and architectural elements of Inter-Cloud for utility-oriented federation of Cloud computing environments. The proposed InterCloud environment supports scaling of applications across multiple vendor clouds. We have validated our approach by conducting a set of rigorous performance evaluation study using the CloudSim toolkit. The results demonstrate that federated Cloud computing model has immense potential as it offers significant performance gains as regards to response time and cost saving under dynamic workload scenarios.

The concurrent growth of on-line communities exhibiting large-scale social structure, and of large decentralized peer-to-peer file-sharing systems, has stimulated new interest in understanding networks of interacting agents as economic systems. Here we formulate a model for query incentive networks, motivated by such systems: users seeking information or services can pose queries, together with incentives for answering them, that are propagated along paths in the network. This type of information-seeking process can be formulated as a game among the nodes in the network, and this game has a natural Nash equilibrium. In such systems, it is a fundamental question to understand how much incentive is needed in order for a node to achieve a reasonable probability of extracting an answer to a query from the network. We study the size of query incentives as a function both of the rarity of the answer and the structure of the underlying network. This leads to natural questions related to strategic behavior in branching processes. Whereas the classically studied criticality of branching processes is centered around the region where the branching parameter is 1, we show in contrast that strategic interaction in incentive propagation exhibits critical behavior when the branching parameter is 2.

Abstract—We evaluate the performance of a large-scale live P2P video multicast session comprising more than 120, 000 peers on the Internet. Our analysis highlights P2P video multicast characteristics such as high bandwidth requirements, high peer churn, low peer persistence in the P2P multicast system, significant variance in the media stream quality delivered to peers, relatively large channel start times, and flash crowd effects of popular video content. Our analysis also indicates that peers are widely spread across the IP address space, spanning dozens of countries and hundreds of ISPs and Internet ASes. As part of the P2P multicast evaluation several QoS measures such as fraction of stream blocks correctly received, number of consecutive stream blocks lost, and channel startup time across peers. We correlate the observed quality with the underlying network and with peer behavior, suggesting several avenues for optimization and research in P2P video multicast systems. I.

Abstract—Peer-to-peer streaming systems are becoming highly popular for IP Television (IPTV). Most systems can be categorized as either tree-based or mesh-based, and as either pushbased or pull-based. However, there is a lack of clear understanding of how these different mechanisms perform comparatively in a real-world setting. In this paper, we compare two representative streaming systems using mesh-based and multiple tree-based overlay routing through deployments on the PlanetLab widearea experimentation platform. To the best of our knowledge, this is the first study to directly compare streaming overlay architectures in real Internet settings. Our results indicate that mesh-based systems inject a much higher number of duplicate packets into the network, but they perform better under a variety of conditions. In particular, mesh-based systems give consistently higher application goodput when the number of overlay nodes, or the streaming rates increase. They also perform better under churn and large flash crowds. Their performance suffers when latencies among peers are high, however. Overall, mesh-based systems appear to be a better choice than multi-tree based systems for peer-to-peer streaming at a large scale. I.

This paper gives two contributions; First, it presents an architecture for customized content delivery for Ambient Intelligent Environments. We demonstrate how physical peers made up of a Bluetooth-based network of Java-enabled mobile phones can be used to provide customized content delivery from the web without the need of a dedicated web connection per device. Secondly, we present two algorithms Self-OrganiziNG random walkerS (SONGS) and Peerto-peeR self-organIZ ed tEmporary overlayS (PRIZES), both providing mechanisms of temporary overlay formation in limited connectivity ad-hoc networks. SONGS is an extension of k-random walk algorithm whereas PRIZES is a forest-fire type flooding mechanism. We then show how adding even naive self-organization to these algorithms significantly improves the leftover queries as well as latency in terms of hop-counts.