This paper presents a novel peer-to-peer protocol to efficiently distribute virtual machine images in a datacenter. A primary idea of it is to improve the performance of peer-to-peer content delivery by employing deduplication to take advantage of similarity both among and within VM images in cloud datacenters. The efficacy of the proposed scheme is validated through an evaluation that demonstrates substantial performance gains.

A system based on application of Fuzzy Cognitive Map (FCM) to perform on-line fault diagnosis is presented. The diagnostic part of the system is composed of two diagnostic schemes. The first one (basic diagnostic algorithm) can be considered as a simple transition of Shiozaki's signed directed graph approach to FCM framework. The second one is an extended version of the basic diagnostic algorithm where an important concept, the temporal associative memories (TAM) recall of FCM, is adopted. In on-line application, self-generated fault FCM model generates predicted pattern sequence through the TAM recall process, which is compared with observed pattern sequence to declare the origin of fault. As the resultant diagnosis scheme takes short computation time, it can be used for on-line fault diagnosis of large and complex processes, and even for incipient fault diagnosis. In practical case, since real observed pattern sequence may be different from predicted one through the TAM recall owing to propagation delay between process variables, the time indexed fault FCM model incorporating delay time is proposed. The utility of the proposed system is illustrated in fault diagnosis of a tank-pipe system.