In the last decade Peer to Peer technology has been thoroughly explored, because it overcomes many limitations compared to the traditional client server paradigm. Despite its advantages over a traditional approach, the ubiquitous availability of high speed, high bandwidth and low latency networks has supported the traditional client-server paradigm. Recently, however, the surge of streaming services has spawned renewed interest in Peer to Peer technologies. In addition, services like geolocation databases and browser technologies like Web-RTC make a hybrid approach attractive. In this paper we present algorithms for the construction and the maintenance of a hybrid P2P overlay multicast tree based on topological distances. The essential idea of these algorithms is to build a multicast tree by choosing neighbours close to each other. The topological distances can be easily obtained by the browser using the geolocation API. Thus the implementation of algorithms can be done web-based in a distributed manner. We present proofs of our algorithms as well as practical results and evaluations.

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Software response time distributions can be of high variance and multi-modal. Such characteristics reduce confidence or applicability in various statistical evaluations.We contribute an approach to correlating response times to their corresponding operation execution sequence. This provides calling-context sensitive timing behavior models. The approach is based on three equivalence relations: caller-context, stack-context, and trace-context equivalence. To prevent model size explosion, a tree-based hierarchy provides timing behavior models that provide a trade-off between timing behavior model size and the amount of calling-context information considered.In the case study, our approach provides response time distributions with significantly lower standard deviation, compared to using less or no calling-context information. An example from a performance analysis of an industry system demonstrates that multi-modal distributions can be replaced by multiple unimodal distributions using trace-context analysis.