Recently Peer-to-Peer Content Delivery Networks (P2P CDNs) have attracted considerable attention as a cost-effective way to disseminate digital contents to paid users in a scalable and dependable manner. However, due to its peer-to-peer nature, it faces threat from “colluders” who paid for the contents but illegally share them with unauthorized peers. This means that the detection of colluders is a crucial task for P2P CDNs to preserve the right of contents holders and paid users. In this paper, we propose two colluder detection schemes for P2P CDNs. The first scheme is based on the reputation collected from all peers participating in the network and the second scheme improves the quality of colluder identification by using a technique which is well known in the field of system level diagnosis. The performance of the schemes is evaluated by simulation. The simulation results indicate that even when 10% of authorized peers are colluders, our schemes identify all colluders without causing misidentifications.

We define a d-matched digraph and propose a recursive procedure for designing an optimal d-matched digraph without bidirectional edges. The digraph represents an optimal highly structured system which is a special class of self-diagnosable systems and identifies all of the faulty units independently and locally in O(|E|) time complexity. The procedure is straightforward and gives a system flexible in network connections. Hence the procedure is applicable to real systems such as the Internet or cooperative robotic systems which change their topology dynamically.

System-level diagnosis is a very important technique for identifying faulty processors in a system with a large number of processors. Processors can test other processors, and then output the test results. The aim of diagnosis is to determine correctly the faulty/fault-free status of all processors. The adaptive diagnosis have been studied in order to perform diagnosis more efficiently. In this paper, we present adaptive diagnosis algorithms for a system modeled by butterfly networks. Our algorithms identify all faulty nodes in butterfly networks with the optimal number of tests. Then, we design another algorithm for diagnosis with very small constant number of rounds.

In this paper, we investigate self diagnosable systems on multi-processor systems, known as one-step t-diagnosable systems introduced by Preparata et al. Kohda has proposed "highly structured system" to design diagnosable systems such that faulty processors are diagnosed efficiently. On the other hand, it is known that Cayley graphs have been investigated as good models for architectures of large-scale parallel processor systems. We investigate some conditions for Cayley graphs to be topologies for optimal highly structured diagnosable systems, and present several examples of optimal diagnosable systems represented by Cayley graphs.

We examine diagnosis of processor array systems formed as two-dimensional grids, with boundaries, and either four or eight neighbors for each interior processor. We employ a parallel test schedule. Neighboring processors test each other and report the results. Our diagnostic objective is to find a fault-free processor or set of processors. The system may then be sequentially diagnosed by repairing those processors tested faulty according to the identified fault-free set. We establish an upper bound on the maximum number of faults that can be sustained without invalidating the test results under worst case conditions. We give test schedules and diagnostic algorithms that meet the upper bound as far as the highest order term. We compare these near optimal diagnostic algorithms to alternative algorithms--both new and already in the literature.

System level diagnosis that can identify the faulty units in the system was introduced by Preparata, Metze, and Chien. In this area, the fundamental problem is to decide the diagnosability of given networks. We study the diagnosability of networks represented by the cartesian product. Our result is the optimal one with respect to the restriction of degrees of vertices of graphs that represent the networks.