8769201
Virtual workspaces: Achieving quality of service and quality of life in the Grid
By defining standardized protocols for discovering, accessing, monitoring, and managing remote computers, storage systems, networks, and other resources, Grid technologies make it possible—in principle—to allocate resources to applications dynamically, in an on-demand fashion [1]. However, while Grids offer users access to many diverse and powerful resources, they do little to ensure that once a resource is accessed, it fulfills user expectations for
2953976
A performance study of monitoring and information services for distributed systems
Monitoring and information services form a key component of a distributed system, or Grid. A quantitative study of such services can aid in understanding the performance limitations, advise in the deployment of the monitoring system, and help evaluate future development work. To this end, we study the performance of three monitoring and information services for distributed systems: the Globus Toolkit/spl reg/ Monitoring and Discovery Service (MDS2), the European Data Grid Relational Grid Monitoring Architecture (R-GMA) and Hawkeye, part of the Condor project. We perform experiments to test their scalability with respect to number of users, number of resources and amount of data collected. Our study shows that each approach has different behaviors, often due to their different design goals. In the four sets of experiments we conducted to evaluate the performance of the service components under different circumstances, we found a strong advantage to caching or pre-fetching the data, as well as the need to have primary components at well-connected sites because of the high load seen by all systems.
14209049
Maintaining data warehouses over changing information sources
I n recent years, the number of digital information storage and retrieval systems has increased immensely. These information sources are generally interconnected via some network, and hence the task of integrating data from different sources to serve it up to users is an increasingly important one [10]. Applications that could benefit from this wealth of digital information are thus experiencing a pressing need for suitable integration tools that allow them to make effective use of such distributed and diverse data sets. In contrast to the on-demand approach to information integration, the approach of tailored information repository construction, commonly referred to as data warehousing, is characterized by the following properties:
3555343
Matching execution histories of program versions
We develop a method for matching dynamic histories of program executions of two program versions. The matches produced can be useful in many applications including software piracy detection and several debugging scenarios. Unlike some static approaches for matching program versions, our approach does not require access to source code of the two program versions because dynamic histories can be collected by running instrumented versions of program binaries. We base our matching algorithm on comparison of rich program execution histories which include: control flow taken, values produced, addresses referenced, as well as data dependences exercised. In developing a matching algorithm we had two goals: producing an accurate match and producing it quickly. By using rich execution history, we are able to compare the program versions across many behavioral dimensions. The result is a fast and highly precise matching algorithm. Our algorithm first uses individual histories of instructions to identify multiple potential matches and then it refines the set of matches by matching the data dependence structure established by the matching instructions. To test our algorithm we attempted matching of execution histories of unoptimized and optimized program versions. Our results show that our algorithm produces highly accurate matches which are highly effective when used in comparison checking approach to debugging optimized code.
6465422
CAEP: Classification by Aggregating Emerging Patterns
Emerging patterns (EPs) are itemsets whose supports change significantly from one dataset to another; they were recently proposed to capture multi-attribute contrasts between data classes, or trends over time. In this paper we propose a new classifier, CAEP, using the following main ideas based on EPs: (i) Each EP can sharply differentiate the class membership of a (possibly small) fraction of instances containing the EP, due to the big difference between its supports in the opposing classes; we define the differentiating power of the EP in terms of the supports and their ratio, on instances containing the EP. (ii) For each instance t, by aggregating the differentiating power of a fixed, automatically selected set of EPs, a score is obtained for each class. The scores for all classes are normalized and the largest score determines t’s class. CAEP is suitable for many applications, even those with large volumes of high (e.g. 45) dimensional data; it does not depend on dimension reduction on data; and it is usually equally accurate on all classes even if their populations are unbalanced. Experiments show that CAEP has consistent good predictive accuracy, and it almost always outperforms C4.5 and CBA. By using efficient, border-based algorithms (developed elsewhere) to discover EPs, CAEP scales up on data volume and dimensionality. Observing that accuracy on the whole dataset is too coarse description of classifiers, we also used a more accurate measure, sensitivity and precision, to better characterize the performance of classifiers. CAEP is also very good under this measure.
14579845
Texture image retrieval based on a Gaussian mixture model and similarity measure using a Kullback divergence
In a content-based image retrieval (CBIR) system, indexing feature vectors and the similarity measure between feature vectors are two key factors for retrieval performance. We present a new CBIR system with statistical-model based image feature extraction in the wavelet domain and a Kullback divergence based similarity measure. A two component Gaussian mixture model (GMM) in the wavelet domain is employed and the model parameters are used to form features for image indexing. A new Kullback divergence based similarity measure is then presented for image retrieval. The experimental results demonstrate that the similarity measure based on the Kullback divergence is more effective than conventional similarity measures, such as the city-block distance and the Euclidean distance. It is shown that the new CBIR system, with the combination of the GMM and the new Kullback divergence based similarity measure, outperforms most other methods in retrieval performance for texture images, while keeping a comparable level of computational complexity.
736327
A usage-based authorization framework for collaborative computing systems
Collaborative systems such as Grids provide efficient and scalable access to distributed computing capabilities and enable seamless resource sharing between users and platforms. This heterogeneous distribution of resources and the various modes of collaborations that exist between users, virtual organizations, and resource providers require scalable, flexible, and fine-grained access control to pro-tect both individual and shared computing resources. In this paper we propose a usage control (UCON) based authorization frame-work for collaborative applications. In our framework, usage con-trol policies are defined using subject and object attributes, along with system attributes as conditions. General attributes include not only persistent attributes such as role and group memberships, but also mutable usage attributes of subjects and objects. Conditions in UCON can be used to support context-based authorizations in ad-hoc collaborations. As a proof-of-concept we implement a pro-totype system based on our proposed architecture and conduct ex-perimental studies to demonstrate the feasibility and performance of our approach.