18821733
Reliability Modeling Using UML
System reliability has become an increasingly important benchmark in measuring service continuity. As part of many service level agreements, system performance is gauged by how long it provides service without downtime. It is essential to understand and estimate system reliability during the system design and development phase. Most reliability modeling techniques are based on reliability block diagrams, fault trees, Markov chains and various other elements. Such formalisms may not be familiar to system architects, product managers and software developers. They normally use other high level notations such as Unified Modeling Language (UML) [10] to describe the behaviors and aspects of computing systems. Consequently, there is inevitably a gap between the design process and reliability modeling that system designers are facing. This paper proposes a method, modeling framework and proof-of-concept tool that will attempt to fill the gap. We will then demonstrate our tool that uses UML technology to model two-tier computer system’s reliability, obtain the failure/repair rate specified in the UML model, and calculate the system’s reliability using the well-known SHARPE tool [8].
1043193
Dependability Prediction of High Availability OSCAR Cluster Server
High availability (HA) computing has recently gained much attention, especially in enterprise and mission critical systems. The HA is now a necessity that is no longer regarded as a luxury feature. Thus, we, conjunctively with the open source community, are in process of enhancing the HA feature to Open Source Cluster Application Resources (OSCAR), a widely adopted Linux PC cluster system. Server redundancy will be our initial key aspect of the next generation HA OSCAR cluster system. In this paper, we introduce a HA server for OSCAR cluster system. Its architecture and mechanism is discussed, and then we model and predict the dependability of the system by a Petri net-based model, Stochastic Reword Net (SRN). The reliability and instantaneous availability of the system are presented as a result.
31684732
Achieving high availability and performance computing with an HA-OSCAR cluster
High availability (HA) computing has long gained much attention in enterprise and mission critical systems. HA goals are to maximize the uptime, thus undoubtedly complementing high-performance computing (HPC) objectives. HA-OSCAR is a project that aims to improve HA in commercial-off-the-shelf (COTS)-based HPC environments. In this paper, we introduce a multi-head HPC cluster architecture. Server redundancy is an initial key aspect aiming toward downtime reduction. Two HA-OSCAR types, active-standby and active-active, are studied. We evaluate system dependability for given two models. Stochastic Reward Nets (SRN) are used to model the system availability. We describe our SRN modeling using Stochastic Petri Net Package, and compute several interesting results that characterize HA-OSCAR availability.
13929087
Availability prediction and modeling of high mobility OSCAR cluster
Since the initial introduction of open source cluster application resources (OSCAR), this software package has been a well-accepted choice for building high performance computing systems. As it continues to be applied to mission-critical environments, high availability (HA) features therefore are needed to be included in OSCAR cluster. In this paper, we provide a HA solution for OSCAR cluster. As a widely used technique in HA solutions, component redundancy is adopted to improve the system availability. Based on the proposed architecture, we develop a detailed failure-repair model for predicting the availability of HA OSCAR cluster. stochastic reward nets (SRN) are used to model the behavior of the system. We specify our SRN model to stochastic Petri net package, describe and compute several interesting output measures that characterize availability features of HA OSCAR cluster.
6400431
Applying rough sets to market timing decisions
A lot of research has been done to predict economic development. The problem studied here is about the stock prediction for use of investors. More specifically, the stock market's movements will be analyzed and predicted. We wish to retrieve knowledge that could guide investors on when to buy and sell. 
 
Through a detailed case study on trading S&P 500 index, rough sets is shown to be an applicable and effective tool to achieve this goal. Some problems concerning time series transformation, indicator selection, trading system building in real implementation are also discussed.
15158980
Large-Scale Modeling of Parametric Surfaces Using Spherical Harmonics
We present an approach for large-scale modeling of parametric surfaces using spherical harmonics (SHs). A standard least square fitting (LSF) method for SH expansion is not scalable and cannot accurately model large 3D surfaces. We propose an iterative residual fitting (IRF) algorithm, and demonstrate its effectiveness and scalability in creating accurate SH models for large 3D surfaces. These large-scale and accurate parametric models can be used in many applications in computer vision, graphics, and biomedical imaging. As a simple extension of LSF, IRF is very easy to implement and requires few machine resources.
811735
Classifying Market States with WARS
In this paper, a new indicator - WARS (Weighted Accumulated Reconstruction Series) at classifying the state of financial market, either trending state or mean-reverting state, was presented. Originated from the computation of Entropy, this new indicator was found to be able to reflect the market behavior accurately and easily. The algorithm of generating WARS and its meaning related to Entropy were introduced and some comparison results between WARS and the Daily Profit Curve were listed. As a new indicator, WARS also can be used to build a trading system - to provide buy, sell and hold signals. Through the application on S&P 500 index, it was verified to be effective and was a promising indicator.
23994030
Fast Association Discovery in Derivative Transaction Collections
Abstract. Association discovery from a transaction collection is an important data-mining task. We study a new problem in this area whose solution can provide users with valuable association rules in some relevant collections: association discovery in derivative transaction collections. In this problem, we are given association rules in two transaction collections D1 and D2, and aim to find new association rules in derivative transaction collections D1∖D2, D1∩D2, D2∖D1 and D1∪D2. Direct application of existing algorithms can solve this problem, but in an expensive way. We propose an efficient solution through making full use of already discovered information, taking advantage of the relationships existing among relevant collections, and avoiding unnecessary but expensive support-counting operations by scanning databases. Experiments on well-known synthetic data show that our solution consistently outperforms the naive solution by factors from 2 to 3 in most cases. We also propose an efficient parallelization of our approach, as parallel algorithms are often interesting and necessary in the area of data mining.
14826038
A Spatio-Temporal Modeling Method for Shape Representation
The spherical harmonic (SPHARM) description is a powerful surface modeling technique that can model arbitrarily shaped but simply connected three dimensional (3D) objects. Because SPHARM based 3D models can derive functional information analysis and classify different pathological symptoms, it has been used in many applications in biomedical image computing. There is an urgent requirement for efficient spatio-temporal shape modeling to represent the dynamic anatomical structures in many applications (e.g., medical image analysis, geospatial information systems). In this paper we propose a novel real spherical harmonics based spatio-temporal shape modeling method to efficiently and flexibly represent the shapes sequence of anatomical structures in medical images. Our method works well on the simply connected 3D objects and the effectiveness of our approach is demonstrated through theoretic and experimental exploration of a set of medical image applications. Furthermore, an evaluation criterion for spatio-temporal shape modeling efficiency is proposed and the comparison results showed the good performance of our method.