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Batched stream processing is a new distributed data processing paradigm that models recurring batch computations on incrementally bulk-appended data streams. The model is inspired by our empirical study on a trace from a large-scale production data-processing cluster; it allows a set of effective query optimizations that are not possible in a traditional batch processing model.We have developed a query processing system called Comet that embraces batched stream processing and integrates with DryadLINQ. We used two complementary methods to evaluate the effectiveness of optimizations that Comet enables. First, a prototype system deployed on a 40-node cluster shows an I/O reduction of over 40% using our benchmark. Second, when applied to a real production trace covering over 19 million machine-hours, our simulator shows an estimated I/O saving of over 50%.

In vehicular ad hoc networks, opportunistic routing can effectively improve the reliability and throughput. However, opportunistic routing also has security issues. For example, malicious nodes can easily mix into node candidate sets, which can interfere with network performance. In this paper, a trust model based on node behavior is proposed for solving the problem of malicious nodes in the opportunistic routing and forwarding candidate set. The proposed trust model uses pruning and filtering mechanisms to remove malicious suggestions,and uses dynamic weight calculation methods to combine direct trust and indirect trust when calculating the comprehensive trust value, which can screen and filter low-trust nodes in the network. Then, combining the ETX (Expected Transmission Count) value and the node trust value, an opportunity routing algorithm based on trust model (BTOR) is proposed. Extensive simulation results represent that the algorithm can significantly improve the network performance and reduce the interference of malicious nodes to the network system.

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