An immune inspired co-evolutionary affinity network for prefetching of distributed object

In distributed object computing (DOC) containers, cache strategy as a passive approach improves the performance by caching the recently accessed objects. By the advent of large-scale enterprise applications of DOC, caching methods fail to keep pace with the increasing importance of performance and the increasing scale of DOC system. Prefetching is an effective approach to improve the performance. It generates and stores pages or objects in caches in advance, by predicting the requests. In the current DOC container, prefetching strategy is not supported and seldom studied in the literature. The immune system with faster secondary response and its affinity network inspire applying the immune mechanisms to build holistic model of DOC for performance improvement. In this study, a co-evolutionary affinity network (CEA-Net) is proposed for prefetching distributed objects. In CEA-Net, objects are antibodies and computation tasks are antigens. Invocation relations among object classes are modeled by the immune network of antibody genotypes. Multiple affinity measures among antibody, antigen, genotypes of antibody and antigen, genotype set and antibody population are defined to model the complex relations among distributed objects. Especially in the antibody population, immune principles including clonal proliferation, immune memory, immune toleration and elimination are designed to add evolutionary features to the antibody population. Based on CEA-Net, the prefetching architecture for DOC is built including 5 main procedures, Network Abstractor, Access Recorder, Object Factory, Cache Engine and Prefetch Engine. Finally, the experimental study shows the promising access performance and the evolutionary features of CEA-Net. CEA-Net is instructive for the future design of high performance DOC containers, such as WebSphere Application Server and BEA WebLogic Application Server.

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