Big Data promises new scientific discovery and economic value. Genetic algorithms (GAs) have proven their flexibility in many application areas and substantial research effort has been dedicated to improving their performance through parallelisation. In contrast with most previous efforts we reject approaches that are based on the centralisation of data in the main memory of a single node or that require remote access to shared/distributed memory. We focus instead on scenarios where data is partitioned across machines.

Data centre applications for batch processing (e.g. map/reduce frameworks) and online services (e.g. search engines) scale by distributing data and computation across many servers. They typically follow a partition/aggregation pattern: tasks are first partitioned across servers that process data locally, and then those partial results are aggregated. This data aggregation step, however, shifts the performance bottleneck to the network, which typically struggles to support many-to-few, high-bandwidth traffic between servers.

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As users of "big data" applications expect fresh results, we witness a new breed of stream processing systems (SPS) that are designed to scale to large numbers of cloud-hosted machines. Such systems face new challenges: (i) to benefit from the "pay-as-you-go" model of cloud computing, they must scale out on demand, acquiring additional virtual machines (VMs) and parallelising operators when the workload increases; (ii) failures are common with deployments on hundreds of VMs-systems must be fault-tolerant with fast recovery times, yet low per-machine overheads. An open question is how to achieve these two goals when stream queries include stateful operators, which must be scaled out and recovered without affecting query results.

Remote Method Invocation (RMI) provides a powerful programming abstraction, well integrated with the object-oriented paradigm. Like conventional method calls, RMI interaction is point-to-point and uses an explicit address to determine the target of invocations. While natural and easy to use, these characteristics limit the applicability of RMI in large scale, dynamic scenarios. In this paper we present MultiCaR: a multicast extension to RMI, which provides a declarative addressing model that maximizes the decoupling among components, supporting a context-aware programming style that nicely fits dynamic scenarios. The set of guarantees provided by MultiCaR have been carefully defined to allow an efficient implementation of the model for large scale deployments. Finally, the MultiCaR prototype we developed exploits a content-based routing infrastructure to provide flexibility and scalability at the implementation level. We argue that these characteristics make MultiCaR a good candidate to develop large scale, object-oriented, dynamic applications, in the same way as content-based publish-subscribe has proved to support large scale, event-driven, dynamic applications.

In this poster, we focus on the cross-layer design of a low-power traffic aware MAC protocol and a content and context based routing protocol. We describe the cross-layering aspects of the MAC-Routing framework and present the performance results on real sensor node testbed.

Complex communication patterns often need to take into account the situation in which the information to be communicated is produced or consumed. Publish-subscribe, and particularly its content-based incarnation, is often used to convey this information by encoding the ldquocontextrdquo of the publisher into the published messages. In this paper we claim that this approach is limiting and inefficient and propose a context-aware publish-subscribe model of communication as a better alternative. We describe a protocol that implements such model in a distributed publish-subscribe middleware, and analyze how it performs w.r.t. traditional content-based routing.

Distributed content-based publish-subscribe middleware is emerging as a promising answer to the demands of modern distributed computing. Nevertheless, currently available systems usually do not provide reliability guarantees. This hampers their use in dynamic and unreliable scenarios, notably including mobile ones. We evaluate the effectiveness of an approach based on epidemic algorithms. Three algorithms we originally proposed in [P. Costa et al., (2003)] are thoroughly compared and evaluated through simulation in challenging unreliable settings. The results show that our use of epidemic algorithms improves significantly event delivery, is scalable, and introduces only limited overhead.