The era of big data has led to the emergence of new systems for real-time distributed stream processing, e.g., Apache Storm is one of the most popular stream processing systems in industry today. However, Storm, like many other stream processing systems lacks an intelligent scheduling mechanism. The default round-robin scheduling currently deployed in Storm disregards resource demands and availability, and can therefore be inefficient at times. We present R-Storm (Resource-Aware Storm), a system that implements resourceaware scheduling within Storm. R-Storm is designed to increase overall throughput by maximizing resource utilization while minimizing network latency. When scheduling tasks, R-Storm can satisfy both soft and hard resource constraints as well as minimizing network distance between components that communicate with each other. We evaluate R-Storm on set of micro-benchmark Storm applications as well as Storm applications used in production at Yahoo! Inc. From our experimental results we conclude that R-Storm achieves 30-47% higher throughput and 69-350% better CPU utilization than default Storm for the micro-benchmarks. For the Yahoo! Storm applications, R-Storm outperforms default Storm by around 50% based on overall throughput. We also demonstrate that R-Storm performs much better when scheduling multiple Storm applications than default Storm.
The advent of smart sensing technologies has opened up new avenues for addressing the billion dollar problem in the wastewater industry of H2S corrosion in concrete sewer pipes, where there is a growing interest in monitoring the environmental properties that govern the rate of corrosion. In this context, this paper proposes a methodology to predict the moisture content of concretes through data-driven approach by using Gaussian Process Regression modeling. The experimental program in this study practices measurements during wetting and drying phases of concrete. The obtained moisture data is used to train the prediction model against interpreted electrical resistivity data. The data of analytical model formulated from Archie's Law is then analyzed with experimental and Gaussian Process prediction data.
In this paper, we present two methods for performing design verification of switching power converters. The first method can be used to compute the set of reachable states from an initial set of states with nondeterministic parameters. We demonstrate the method on a buck converter in an open-loop configuration. The method is automatic and uses the hybrid systems reachability analysis tool SpaceEx. The second method uses model checking to verify circuits that can naturally be modeled as timed automata. We demonstrate the method on an openloop multilevel converter used to convert several DC inputs to one AC output. The method is also automatic and uses the timed automata model checker Uppaal. Finally, we mention that in contrast to simulation or testing based approaches-for instance, the standard Monte Carlo analysis used when analyzing component variation in circuit designs-the methods presented in this paper perform the verification for all runs of the circuits and all possible component parameter variations.
This paper presents a low-power voltage regulator for wireless sensor nodes. The circuit, implemented in a 90 nm CMOS process, generates a process-temperature-voltage independent 0.5 V while only consuming 26 nA. The output voltage variation is within 27 mV when power supply changes from 1 to 2 V and can provide a 15 μA load current. A fast settling technique is employed to improve the startup response without increasing design complexity. Keywords-regulator, wireless sensor network, power managementI.
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