Collaborative applications over peer-to-peer systems-challenges and solutions

Emerging collaborative Peer-to-Peer (P2P) systems require discovery and utilization of diverse, multi-attribute, distributed, and dynamic groups of resources to achieve greater tasks beyond conventional file and processor cycle sharing. Collaborations involving application specific resources and dynamic quality of service goals are stressing current P2P architectures. Salient features and desirable characteristics of collaborative P2P systems are highlighted. Resource advertising, selecting, matching, and binding, the critical phases in these systems, and their associated challenges are reviewed using examples from distributed collaborative adaptive sensing systems, cloud computing, and mobile social networks. State-of-the-art resource discovery/aggregation solutions are compared with respect to their architecture, lookup overhead, load balancing, etc., to determine their ability to meet the goals and challenges of each critical phase. Incentives, trust, privacy, and security issues are also discussed, as they will ultimately determine the success of a collaborative P2P system. Open issues and research opportunities that are essential to achieve the true potential of collaborative P2P systems are discussed. © 2012 Springer Science + Business Media, LLC

Community-based caching for enhanced lookup performance in P2P systems

—Large Peer-to-Peer (P2P) systems exhibit the presence of communities based on user interests. Resources commonly shared within individual communities are in general relatively less popular and inconspicuous in the system-wide behavior. Hence, such communities are unable to benefit significantly from caching and replication that focus only on the most dominant queries. A Community-Based Caching (CBC) solution that enhances both communitywide and system-wide lookup performance is proposed. CBC consists of a sub-overlay formation scheme and a Local-Knowledge-based Distributed Caching (LKDC) algorithm. Sub-overlays enable communities to forward queries through their members. While queries are forwarded, LKDC algorithm causes members to identify and cache resources of interests to them, resulting in faster resolution of queries for popular resources within each community. Distributed local caching requires global information (e.g., hop count and popularity of contents) that is difficult and costly to obtain. However, by means of an analysis of globally optimal behavior and structural properties of the overlay, we develop the heuristic-based LKDC algorithm that not only relies on purely local information but also provides close-to-optimal caching performance. CBC is adaptive to changing popularity and user interests, works with any skewed distribution of queries, and introduces minimal modifications and overhead to the overlay network

Resource and query aware, multi-attribute resource discovery for P2P systems

Distributed, multi-attribute Resource Discovery (RD) is a fundamental requirement in collaborative Peer-to-Peer (P2P), grid, and cloud computing. We present an efficient and load balanced, P2P-based multi-attribute RD solution that consists of five heuristics, which can be executed independently and distributedly. First heuristic maintains a minimum number of nodes in a ring-like overlay while pruning nodes that do not significantly contribute to the range query resolution. Removing nonproductive nodes reduces the cost (e.g., hops and latency) of advertising resources and resolving queries. Second and third heuristics dynamically balance the key and query load distribution by transferring some of the keys to its predecessor/successor and by adding new predecessors/successors to handle transferred keys when existing nodes are insufficient, respectively. Last two heuristics form cliques of nodes (that are placed orthogonal to the overlay ring) to dynamically balance the highly skewed key and query loads. By applying these heuristics in the presented order, a RD solution that better responds to real-world resource and query characteristics is developed. Its efficacy is demonstrated using a simulation-based analysis under a variety of single and multi-attribute resource and query distributions derived from real workloads

Distributed, multi-user, multi-application, and multi-sensor data fusion over named data networks

Named Data Networking (NDN) routes data based on their application-layer content names enabling location independence, in-network caching, and enhanced security. A proof-of-concept solution is presented that demonstrates the applicability of NDN for multi-user, multi-application, and multi-sensor data-fusion systems. The system consists of a collaborative network of weather radars name data based on their geographic location and weather feature (e.g., reflectivity of clouds and wind velocity). This enables end users to specify an area of interest for a particular weather feature while being oblivious to the placement of radars and associated computing facilities. Conversely, the data-fusion system can also use its knowledge about the underlying system to decide the best sensing and data processing strategies. Such sensor-independent names also enhance resilience, enable processing data close to the source, and benefit from NDN features such as in-network caching and duplicate query suppression, consequently reducing the bandwidth requirements of the entire data-fusion system. The solution is implemented as an overlaid NDN enabling the benefits of both the NDN and overlay networks. Simulation-based analysis using reflectivity data from an actual weather event showed 84% reduction in peak bandwidth consumption of radars and 95% reduction in peak query resolution latency