Top-k query processing techniques are useful in unstructured peer-to-peer (P2P) systems, to avoid overwhelming users with too many results. However, existing approaches suffer from long waiting times. This is because top-k results are returned only when all queried peers have finished processing the query. As a result, query response time is dominated by the slowest queried peer. In this paper, we address this users' waiting time problem. For this, we revisit top-k query processing in P2P systems by introducing two novel notions in addition to response time: the stabilization time and the cumulative quality gap. Using these notions, we formally define the as-soon-as-possible (ASAP) top-k processing problem. Then, we propose a family of algorithms called ASAP to deal with this problem. We validate our solution through implementation and extensive experimentation. The results show that ASAP significantly outperforms baseline algorithms by returning final top-k result to users in much better times.

In this paper, we address data reconciliation in peer-to-peer (P2P) collaborative applications. We propose P2P-LTR (Logging and Timestamping for Reconciliation) which provides P2P logging and timestamping services for P2P reconciliation over a distributed hash table (DHT). While updating at collaborating peers, updates are timestamped and stored in a highly available P2P log. During reconciliation, these updates are retrieved in total order to enforce eventual consistency. In this paper, we first give an overview of P2P-LTR with its model and its main procedures. We then present our prototype used to validate P2P-LTR. To demonstrate P2P-LTR, we propose several scenarios that test our solutions and measure performance. In particular, we demonstrate how P2P-LTR handles the dynamic behavior of peers with respect to the DHT.

Top-k query processing in P2P systems has focused on efficiently computing the top-k results while reducing network traffic and query response time. However, in overloaded P2P systems (with very high query loads), some peers may take a long time to answer, thus making the user wait a long time to obtain the final top-k result. In this paper, we address this problem, which we reformulate as early top-k query processing in P2P systems. First, to complement response time, we introduce two new metrics, stabilization time and cumulative quality gap, with which we formally define the problem. Then, we propose an efficient algorithm that dynamically adapts to query loads of peers in order to return to the user top-k results as soon as possible, without waiting for the final result. We validated our solution through simulations over a real dataset. The results show that our solution significantly outperforms baseline solutions by returning high quality top-k results to users in much better times.

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