32,919 research outputs found
Locating a bioenergy facility using a hybrid optimization method
In this paper, the optimum location of a bioenergy generation facility for district energy applications is sought. A bioenergy facility usually belongs to a wider system, therefore a holistic approach is adopted to define the location that optimizes the system-wide operational and investment costs. A hybrid optimization method is employed to overcome the limitations posed by the complexity of the optimization problem. The efficiency of the hybrid method is compared to a stochastic (genetic algorithms) and an exact optimization method (Sequential Quadratic Programming). The results confirm that the hybrid optimization method proposed is the most efficient for the specific problem. (C) 2009 Elsevier B.V. All rights reserved
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Optimizing the beacon exchange rate for proactive autonomic configuration in ubiquitous MANETs
Proactive self-configuration is indispensable for MANETs like ubiquitous sensor networks (USNs), as component devices of the network are usually exposed to natural or man-made disasters due to the hostile deployment and ad hoc nature of the USNs. Network state beacons (NSBs) are exchanged among the key nodes of the network for crucial and effective monitoring of the network for steady state operation. The rate of beacon exchange (F/sub E/) and its contents, define the time and nature of the proactive action. Therefore it is very important to optimize these parameters to tune the functional response of the USN. This paper presents a comprehensive model for monitoring and proactively reconfiguring the network by optimizing the F/sub E/. The results confirm the improved throughput while maintaining QoS over longer periods of network operation
Distributed-Memory Breadth-First Search on Massive Graphs
This chapter studies the problem of traversing large graphs using the
breadth-first search order on distributed-memory supercomputers. We consider
both the traditional level-synchronous top-down algorithm as well as the
recently discovered direction optimizing algorithm. We analyze the performance
and scalability trade-offs in using different local data structures such as CSR
and DCSC, enabling in-node multithreading, and graph decompositions such as 1D
and 2D decomposition.Comment: arXiv admin note: text overlap with arXiv:1104.451
Persistent Buffer Management with Optimistic Consistency
Finding the best way to leverage non-volatile memory (NVM) on modern database
systems is still an open problem. The answer is far from trivial since the
clear boundary between memory and storage present in most systems seems to be
incompatible with the intrinsic memory-storage duality of NVM. Rather than
treating NVM either solely as memory or solely as storage, in this work we
propose how NVM can be simultaneously used as both in the context of modern
database systems. We design a persistent buffer pool on NVM, enabling pages to
be directly read/written by the CPU (like memory) while recovering corrupted
pages after a failure (like storage). The main benefits of our approach are an
easy integration in the existing database architectures, reduced costs (by
replacing DRAM with NVM), and faster peak-performance recovery
An Optimized Data Structure for High Throughput 3D Proteomics Data: mzRTree
As an emerging field, MS-based proteomics still requires software tools for
efficiently storing and accessing experimental data. In this work, we focus on
the management of LC-MS data, which are typically made available in standard
XML-based portable formats. The structures that are currently employed to
manage these data can be highly inefficient, especially when dealing with
high-throughput profile data. LC-MS datasets are usually accessed through 2D
range queries. Optimizing this type of operation could dramatically reduce the
complexity of data analysis. We propose a novel data structure for LC-MS
datasets, called mzRTree, which embodies a scalable index based on the R-tree
data structure. mzRTree can be efficiently created from the XML-based data
formats and it is suitable for handling very large datasets. We experimentally
show that, on all range queries, mzRTree outperforms other known structures
used for LC-MS data, even on those queries these structures are optimized for.
Besides, mzRTree is also more space efficient. As a result, mzRTree reduces
data analysis computational costs for very large profile datasets.Comment: Paper details: 10 pages, 7 figures, 2 tables. To be published in
Journal of Proteomics. Source code available at
http://www.dei.unipd.it/mzrtre
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