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Using Multivariate Statistics

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

[서평]「The Unified Modeling Language User Guide」

Technical foundations introduction software reliability and system reliability the operational profile software reliability modelling survey model evaluation and recalibration techniques practices and experiences best current practice of SRE software reliability measurement experience measurement-based analysis of software reliability software fault and failure classification techniques trend analysis in validation and maintenance software reliability and field data analysis software reliability process assessment emerging techniques software reliability prediction metrics software reliability and testing fault-tolerant SRE software reliability using fault trees software reliability process simulation neural networks and software reliability. Appendices: software reliability tools software failure data set repository.

/pdf/handbook-of-software-reliability-engineering-32dwvaljmm.pdf

Handbook of software reliability engineering

コンピュータ・サイエンス : ACM computing surveys

Invariants represent properties of a system that are expected to hold when everything goes well. Thus, the violation of an invariant most likely corresponds to the occurrence of an anomaly in the system. In this paper, we discuss the accuracy and the completeness of an anomaly detection system based on invariants. The case study we have taken is a back-end operation of a SaaS platform. Results show the rationality of the approach and discuss the impact of the invariant mining strategy on the detection capabilities, both in terms of accuracy and of time to reveal violations.

Using Invariants for Anomaly Detection: The Case Study of a SaaS Application

This paper presents the design of a 2.4 GHz RF transceiver front-end chipset in 0.25 /spl mu/m CMOS technology. The designed chipset includes a fully monolithic receiver front end consisting of LNA, mixer and two variations of power amplifiers (PA), one for high output power and efficiency, and the other for good linearity. The integrated receiver provides simulated voltage gain of 22.7 dB, NF of 6.6 dB, IIP3 of -15.5 dBm, and consumes 21 mW power from a 1.5 volt power supply. The high-efficiency versions utilize class F/inverse class F matching to achieve power added efficiency (PAE) of over 50% with an output power of up to 350 mW. The linear PA utilizes differential class B push pull architecture and provides an IM3 less than -35 dB with a 22.5 dBm output power and power added efficiency (PAE) of 20%. The circuits are under fabrication in National Semiconductor's 0.25 /spl mu/m CMOS facility and the measurement results will be presented in the final version.

Development of 2.4 GHz RF transceiver front-end chipset in 0.25 /spl mu/m CMOS

A modern cloud service that is getting popular is the supply of bare metal servers on demand to consumers who need to run high-performance algorithms for short periods of time. However, provisioning-time latencies are unpredictable in bare metal commerce, primarily because suppliers cannot exploit virtualization levers to adjust and optimize capacity. This impacts bare metal service leverage because, in the face of indeterministic fulfillment times, consumers often pre-provision for peak demand, which goes against the fundamental tenets and advantages of cloud adoption. To address this, we advocate that a cloud service broker offers time-sensitive bare metal provisioning services by building and maintaining an inventory of bare metal servers. This, however, leads to inventory optimization and profit maximization problems that resemble what traditional supply chains face, but with characteristics unique to on-demand compute economics. In this paper, we build a brokered bare metal supply chain model that identifies impacting variables, their characteristics, and inter-relationships. We argue and demonstrate that, given complex inter-variable relationships and environmental uncertainties, simulation runs are needed to complete the model and yield recommendations to optimize inventory and maximize profits.

Time-Sensitive Provisioning of Bare Metal Compute as a Cloud Service

Technical design of a business application is an involved process which needs an experienced designer to describe an implementation of the application functionality using a set of hardware and software infrastructure elements such that the non-functional requirements are satisfied. The process is extremely knowledge intensive. For instance, it demands the designer to thoroughly understand various architecture styles, a set of technical capabilities available, how different COTS products can realize these capabilities, and how the functional modules can make use of these capabilities. In reality, such experienced designers are hard to come by. Furthermore, to optimize the budget, the project team often involves inexperienced designers to create the technical design. In order to assist an inexperienced designer to create the technical design, we have built a knowledge-based design assistant tool, called Technical Design Assistant Tool (TDAT). In this paper we describe the ontology knowledge model that describes the underlying technical design knowledge. The ontology models the notion of a architecture style, various technical capabilities, a COTS component, and capability features. We have conducted a few experiments to evaluate the usefulness of the ontology and the tool and found encouraging responses. As a part of the project we are populating the knowledge base for the popular open source and commercial software products.

Accelerating technical design of business applications: a knowledge-based approach

The present invention describes a process, system and computer program product for assessing the modularity of an object-oriented program. The process includes calculation of metrics associated with various properties of the object-oriented program. Analysis is performed on the basis of the calculated metrics.

Santonu Sarkar

Papers

Using Invariants for Anomaly Detection: The Case Study of a SaaS Application

Development of 2.4 GHz RF transceiver front-end chipset in 0.25 /spl mu/m CMOS

Time-Sensitive Provisioning of Bare Metal Compute as a Cloud Service

Accelerating technical design of business applications: a knowledge-based approach

Process and system for assessing modularity of an object-oriented program