The superior I/O performance of solid-state storage (e.g., solid-state drives) makes it become an attractive replacement for the traditional magnetic storage (e.g., harddisk drives). More and more storage systems start to integrate solid-state storage into their architecture. To understand the impacts of solid-state storage on the performance of Hadoop applications, we consider a hybrid Hadoop storage system consisting of both HDDs and SSDs, and conduct a series of experiments to evaluate the Hadoop performance under various system configurations. We find that the Hadoop performance can be increased almost linearly with the increasing fraction of SSDs in the storage system. The improvement is more significant for a larger dataset size. In addition, the performance of Hadoop applications running on SSD-dominant storage systems is insensitive to the variations of block size and buffer size, which significantly differs from HDD-dominant storage systems. By increasing the fraction of SSDs, there is no need for the Hadoop operators to consider how to carefully tune block size and buffer size to achieve the optimal performance. Our findings also indicate that the upgrade of the hadoop storage system can be achieved by increasing the capacity of SSDs linearly according to the scale of the applications.
The reaction of meso-formyl Ni(II) porphyrin 1 with zirconacyclopentadiene 2 in the presence of AlCl 3 afforded four products 3, 4, 5, and 6 with a total yield of over 85%. The structures of these compounds are well-characterized by 1 H NMR an d 13 C NMR spectroscopy, HRMS, and X-ray single-crystal diffraction. The mechanism is proposed mainly on the basis of isotopic labeling experiments, which showed that a Friedel−Crafts-type reaction and β-H shift may be critical during the formation of 5 and 6.
With the rise of electric vehicles and the increase of electric vehicle charging pile equipment in the community, the community is prone to the problems of large peak-to-valley difference and high electricity cost during the peak period of the power load. Aiming at this problem, this paper established a community energy platform model according to the different electricity load characteristics in the community. Taking the minimum difference between the peak and valley of the community electricity load and the minimum total electricity cost as the objective function, the improved constrained multi-objective particle swarm algorithm was used to optimize the scheduling of the community power system, and it was verified by an example. The results showed that by effectively regulating the output of electric vehicle charging, residential air conditioning, commercial air conditioning, and energy storage, the load peak in the community during the peak period of electricity consumption could be greatly reduced. It could effectively realize peak shaving and valley filling, reduce the cost of electricity consumption in the community, and improve economic benefits.
The pattern matching calculi introduced by the first author are a refinement of the λ-calculus that integrates mechanisms appropriate for fine-grained modelling of non-strict pattern matching.While related work in the literature only uses a single monad, typically Maybe, for matchings, we present an axiomatic approach to semantics of these pattern matching calculi using two monads, one for expressions and one for matchings.Although these two monads only need to be relatively lightly coupled, this semantics implies soundness of all core PMC rules, and is a useful tool for exploration of the design space for pattern matching calculi.Using lifting and Maybe monads, we obtain standard Haskell semantics, and by adding another level of Maybe to both, we obtain a denotational semantics of the "matching failure as exceptions" approach of Erwig and Peyton Jones. Using list-like monads opens up interesting extensions in the direction of functional-logic programming.A short version of this report appears as [Kahl, Carette + 2006].
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