Abstract—Modern applications for distributed publish/subscribe systems often require stream aggregation capabilities along with rich data filtering. When compared to other distributed systems, aggregation in pub/sub differentiates itself as a complex problem which involves dynamic dissemination paths that are difficult to predict and optimize for a priori, temporal fluctuations in publication rates, and the mixed presence of aggregated and non-aggregated workloads. In this paper, we propose a formalization for the problem of minimizing communication traffic in the context of aggregation in pub/sub. We present a solution to this minimization problem by using a reduction to the well-known problem of minimum vertex cover in a bipartite graph. This solution is optimal under the strong assumption of complete knowledge of future publications. We call the resulting algorithm “Aggregation Decision, Optimal with Complete Knowledge ” (ADOCK). We also show that under a dynamic setting without full knowledge, ADOCK can still be applied to produce a low, yet not necessarily optimal, communication cost. We also devise a computationally cheaper dynamic approach called “Aggregation Decision with Weighted Publication ” (WAD). We compare our solutions experimentally using two real datasets and explore the trade-offs with respect to communication and computation costs. I.

The publish/subscribe paradigm has found wide acceptance in a broad variety of use cases that differ dramatically in the character-istics of their workloads. Many different systems have been devel-oped both by academia as well as industry, but there is no definitive benchmark, which enables a fair comparison between the different systems. In this demo, we present PSBench, a benchmark specification and suite for publish/subscribe systems that covers a broad variety of publish/subscribe workloads and scenarios. The benchmark suite is extensible and generic, but the specification targets social games. Social games are the ideal use case since they have a very broad range of requirements and produce a variety of publications and subscriptions. We draw from our experience in massive multi-player online games to construct a highly realistic workload. In this demo, we present the toolchain, the workload and the graph-ical interfaces that enable an extensive performance evaluation of publish/subscribe systems.