With the vision of Internet of Things gaining popularity at a global level, efficient publish/subscribe middleware for communication within and across data centers is extremely desirable. In this respect, the very popular Software-Defined Networking, which enables publish/subscribe middleware to perform line-rate filtering of events directly on hardware, can prove to be very useful. While deploying content filters directly on switches of a software-defined network allows optimized paths, high throughput rates, and low end-to-end latency, it suffers from certain inherent limitations with respect to number of bits available on hardware switches to represent these filters. Such a limitation affects expressiveness of filters, resulting in unnecessary traffic in the network. In this paper, we explore various complementary techniques to represent content filters expressively while being limited by hardware. We implement and evaluate techniques that i) use workload, in terms of events and subscriptions, to represent content, and ii) efficiently select attributes to reduce redundancy in content. Our detailed performance evaluations show the potential of these techniques in reducing unnecessary traffic when subjected to different workloads. Furthermore, the techniques proposed in this paper require significant updates to the network, i.e., the data plane, which must be performed in a consistent manner to ensure desired system behavior. As a result, in this paper, we, also, design and evaluate a light-weight approach that ensures data plane consistency in the presence of dynamic network updates.

In this paper we present a novel cluster paradigm and silicon operating system. Our approach in developing the competent cluster design revolves around an execution model to aid the execution of multiple independent applications simultaneously on the cluster, leading to cost sharing across applications. The execution model should envisage simultaneous execution of multiple applications (running traces of multiple independent applications in the same node at an instant, without time sharing) and on all the partitionsf nodes) of a single cluster, without sacrificing the performance of individual application, unlike in the current cluster models. Performance scalability is achieved as we increase the number of nodes, the problem size of the individual independent applications, due to non-dependency across applications and hence increase in the number of non-dependent operations (as the problem sizes of the applications get increased) and this leads to better utilization of the unused resources within the node. This execution model is very much dependent on the node architecture for performance scalability. This would be a major initiative towards achieving performance cost-effective supercomputing.