705 research outputs found
Optimally Efficient Prefix Search and Multicast in Structured P2P Networks
Searching in P2P networks is fundamental to all overlay networks.
P2P networks based on Distributed Hash Tables (DHT) are optimized for single
key lookups, whereas unstructured networks offer more complex queries at the
cost of increased traffic and uncertain success rates. Our Distributed Tree
Construction (DTC) approach enables structured P2P networks to perform prefix
search, range queries, and multicast in an optimal way. It achieves this by
creating a spanning tree over the peers in the search area, using only
information available locally on each peer. Because DTC creates a spanning
tree, it can query all the peers in the search area with a minimal number of
messages. Furthermore, we show that the tree depth has the same upper bound as
a regular DHT lookup which in turn guarantees fast and responsive runtime
behavior. By placing objects with a region quadtree, we can perform a prefix
search or a range query in a freely selectable area of the DHT. Our DTC
algorithm is DHT-agnostic and works with most existing DHTs. We evaluate the
performance of DTC over several DHTs by comparing the performance to existing
application-level multicast solutions, we show that DTC sends 30-250% fewer
messages than common solutions
GRIDKIT: Pluggable overlay networks for Grid computing
A `second generation' approach to the provision of Grid middleware is now emerging which is built on service-oriented architecture and web services standards and technologies. However, advanced Grid applications have significant demands that are not addressed by present-day web services platforms. As one prime example, current platforms do not support the rich diversity of communication `interaction types' that are demanded by advanced applications (e.g. publish-subscribe, media streaming, peer-to-peer interaction). In the paper we describe the Gridkit middleware which augments the basic service-oriented architecture to address this particular deficiency. We particularly focus on the communications infrastructure support required to support multiple interaction types in a unified, principled and extensible manner-which we present in terms of the novel concept of pluggable overlay networks
Storytelling Security: User-Intention Based Traffic Sanitization
Malicious software (malware) with decentralized communication infrastructure, such as peer-to-peer botnets, is difficult to detect. In this paper, we describe a traffic-sanitization method for identifying malware-triggered outbound connections from a personal computer. Our solution correlates user activities with the content of outbound traffic. Our key observation is that user-initiated outbound traffic typically has corresponding human inputs, i.e., keystroke or mouse clicks. Our analysis on the causal relations between user inputs and packet payload enables the efficient enforcement of the inter-packet dependency at the application level.
We formalize our approach within the framework of protocol-state machine. We define new application-level traffic-sanitization policies that enforce the inter-packet dependencies. The dependency is derived from the transitions among protocol states that involve both user actions and network events. We refer to our methodology as storytelling security.
We demonstrate a concrete realization of our methodology in the context of peer-to-peer file-sharing application, describe its use in blocking traffic of P2P bots on a host. We implement and evaluate our prototype in Windows operating system in both online and offline deployment settings. Our experimental evaluation along with case studies of real-world P2P applications demonstrates the feasibility of verifying the inter-packet dependencies. Our deep packet inspection incurs overhead on the outbound network flow. Our solution can also be used as an offline collect-and-analyze tool
Approximate Matching for Peer-to-Peer Overlays with Cubit
Keyword search is a critical component in most content retrieval systems. Despite the emergence of completely decentralized and
efficient peer-to-peer techniques for content distribution, there have not been similarly efficient, accurate, and decentralized mechanisms for content discovery based on approximate search keys. In this paper,
we present a scalable and efficient peer-to-peer system called Cubit with a new search primitive that can efficiently find the k data items with keys most similar to a given search key. The system works by creating a keyword metric space that encompasses both the nodes and the objects in the system, where the distance between two points is a measure of the similarity between the strings that the points represent. It provides a loosely-structured overlay that can efficiently navigate this space. We evaluate Cubit through both a real deployment as a search plugin for a popular BitTorrent client and a large-scale simulation and show that it provides an efficient, accurate and robust method to handle imprecise string search in filesharing applications.This work was supported in part by NSF-TRUST 0424422 and NSF-CAREER 0546568 grants
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