Browsing by Subject "Data transfer"

Now showing 1 - 7 of 7

Open Access
A comparison of epidemic algorithms in wireless sensor networks
(Elsevier BV, 2006-08-21) Akdere, M.; Bilgin, C. C.; Gerdaneri, O.; Korpeoglu, I.; Ulusoy, O.; Çetintemel, U.
We consider the problem of reliable data dissemination in the context of wireless sensor networks. For some application scenarios, reliable data dissemination to all nodes is necessary for propagating code updates, queries, and other sensitive information in wireless sensor networks. Epidemic algorithms are a natural approach for reliable distribution of information in such ad hoc, decentralized, and dynamic environments. In this paper we show the applicability of epidemic algorithms in the context of wireless sensor environments, and provide a comparative performance analysis of the three variants of epidemic algorithms in terms of message delivery rate, average message latency, and messaging overhead on the network. © 2006 Elsevier B.V. All rights reserved.
Open Access
Discriminative fine-grained mixing for adaptive compression of data streams
(Institute of Electrical and Electronics Engineers, 2014) Gedik, B.
This paper introduces an adaptive compression algorithm for transfer of data streams across operators in stream processing systems. The algorithm is adaptive in the sense that it can adjust the amount of compression applied based on the bandwidth, CPU, and workload availability. It is discriminative in the sense that it can judiciously apply partial compression by selecting a subset of attributes that can provide good reduction in the used bandwidth at a low cost. The algorithm relies on the significant differences that exist among stream attributes with respect to their relative sizes, compression ratios, compression costs, and their amenability to application of custom compressors. As part of this study, we present a modeling of uniform and discriminative mixing, and provide various greedy algorithms and associated metrics to locate an effective setting when model parameters are available at run-time. Furthermore, we provide online and adaptive algorithms for real-world systems in which system parameters that can be measured at run-time are limited. We present a detailed experimental study that illustrates the superiority of discriminative mixing over uniform mixing. © 2013 IEEE.
Open Access
Effective kernel mapping for OpenCL applications in heterogeneous platforms
(Institute of Electrical and Electronics Engineers, 2012-09) Albayrak, Ömer Erdil; Aktürk, İsmail; Öztürk, Özcan
Many core accelerators are being deployed in many systems to improve the processing capabilities. In such systems, application mapping need to be enhanced to maximize the utilization of the underlying architecture. Especially in GPUs mapping becomes critical for multi-kernel applications as kernels may exhibit different characteristics. While some of the kernels run faster on GPU, others may refer to stay in CPU due to the high data transfer overhead. Thus, heterogeneous execution may yield to improved performance compared to executing the application only on CPU or only on GPU. In this paper, we propose a novel profiling-based kernel mapping algorithm to assign each kernel of an application to the proper device to improve the overall performance of an application. We use profiling information of kernels on different devices and generate a map that identifies which kernel should run on where to improve the overall performance of an application. Initial experiments show that our approach can effectively map kernels on CPU and GPU, and outperforms to a CPU-only and GPU-only approach. © 2012 IEEE.
Open Access
Efficient parallel spatial subdivision algorithm for object-based parallel ray tracing
(Pergamon Press, 1994) Aykanat, Cevdet; İşler, V.; Özgüç, B.
Parallel ray tracing of complex scenes on multicomputers requires the distribution of both computation and scene data to the processors. This is carried out during preprocessing and usually consumes too much time and memory. The paper presents an efficient parallel subdivision algorithm that decomposes a given scene into rectangular regions adaptively and maps the resultant regions to the node processors of a multicomputer. The proposed algorithm uses efficient data structures to identify the splitting planes quickly. Furthermore the mapping of the regions and the objects to the node processors is performed while parallel spatial subdivision proceeds. The proposed algorithm is implemented on an Intel iPSC/2 hypercube multicomputer and promising results have been obtained. © 1994.
Open Access
Interactive training of advanced classifiers for mining remote sensing image archives
(ACM, 2004) Aksoy, Selim; Koperski, K.; Tusk, C.; Marchisio G.
Advances in satellite technology and availability of down-loaded images constantly increase the sizes of remote sensing image archives. Automatic content extraction, classification and content-based retrieval have become highly desired goals for the development of intelligent remote sensing databases. The common approach for mining these databases uses rules created by analysts. However, incorporating GIS information and human expert knowledge with digital image processing improves remote sensing image analysis. We developed a system that uses decision tree classifiers for interactive learning of land cover models and mining of image archives. Decision trees provide a promising solution for this problem because they can operate on both numerical (continuous) and categorical (discrete) data sources, and they do not require any assumptions about neither the distributions nor the independence of attribute values. This is especially important for the fusion of measurements from different sources like spectral data, DEM data and other ancillary GIS data. Furthermore, using surrogate splits provides the capability of dealing with missing data during both training and classification, and enables handling instrument malfunctions or the cases where one or more measurements do not exist for some locations. Quantitative and qualitative performance evaluation showed that decision trees provide powerful tools for modeling both pixel and region contents of images and mining of remote sensing image archives.
Open Access
PetaShare: A reliable, efficient and transparent distributed storage management system
(2011) Kosar, T.; Akturk I.; Balman, M.; Wang X.
Modern collaborative science has placed increasing burden on data management infrastructure to handle the increasingly large data archives generated. Beside functionality, reliability and availability are also key factors in delivering a data management system that can efficiently and effectively meet the challenges posed and compounded by the unbounded increase in the size of data generated by scientific applications. We have developed a reliable and efficient distributed data storage system, PetaShare, which spans multiple institutions across the state of Louisiana. At the back-end, PetaShare provides a unified name space and efficient data movement across geographically distributed storage sites. At the front-end, it provides light-weight clients the enable easy, transparent and scalable access. In PetaShare, we have designed and implemented an asynchronously replicated multi-master metadata system for enhanced reliability and availability, and an advanced buffering system for improved data transfer performance. In this paper, we present the details of our design and implementation, show performance results, and describe our experience in developing a reliable and efficient distributed data management system for data-intensive science. © 2011 - IOS Press and the authors. All rights reserved.
Open Access
Preventing unauthorized data flows
(Springer, Cham, 2017) Uzun, Emre; Parlato, G.; Atluri, V.; Ferrara, A. L.; Vaidya, J.; Sural, S.; Lorenzi, D.
Trojan Horse attacks can lead to unauthorized data flows and can cause either a confidentiality violation or an integrity violation. Existing solutions to address this problem employ analysis techniques that keep track of all subject accesses to objects, and hence can be expensive. In this paper we show that for an unauthorized flow to exist in an access control matrix, a flow of length one must exist. Thus, to eliminate unauthorized flows, it is sufficient to remove all one-step flows, thereby avoiding the need for expensive transitive closure computations. This new insight allows us to develop an efficient methodology to identify and prevent all unauthorized flows leading to confidentiality and integrity violations. We develop separate solutions for two different environments that occur in real life, and experimentally validate the efficiency and restrictiveness of the proposed approaches using real data sets. © IFIP International Federation for Information Processing 2017.