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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

Power and energy optimization of applications running on a High Performance Computing infrastructure is an important research area in today's HPC driven world. Considerable amount of investments are being made for the same, emphasizing on the importance of such a research. The above mentioned optimization is a two step process. First, one must measure and analyze the power consumption accurately and then optimize the application by modifying the relevant sections of the application software responsible for it. In this paper we propose an API, called the Unified Power Profiling API (UPPAPI), which allows a parallel application developer to measure the power and energy consumed by the heterogeneous system during the application execution. The first important feature of this API is the abstraction created over different co-processors. Second, we have incorporated a corrective power model to remove the discrepancy arising out of the on-board GPU sensors. We illustrate the working of the above mentioned tool using Rodinia and SHOC Benchmarks. We further verify the correctness of the API generated measurement by comparing the results returned by the API with the actual power measured with a Krykard ALM10 power analyzer. This paper is an early attempt to develop a well rounded software tool, which enables the developer to optimize the resource usage and create efficient applications.

Unified power and energy measurement API for HPC co-processors

Parallelization of an existing sequential application to achieve a good speed-up on a data-parallel infrastructure is quite difficult and time consuming effort. One of the important steps towards this is to assess whether the existing application in its current form can be parallelized to get the desired speedup. In this paper, we propose a method of analyzing an existing sequential source code that contains data-parallel loops, and give a reasonably accurate prediction of the extent of speedup possible from this algorithm. The proposed method performs static and dynamic analysis of the sequential source code to determine the time required by various portions of the code, including the data-parallel portions. Subsequently, it uses a set of novel invariants to calculate various bottlenecks that exists if the program is to be transferred to a GPGPU platform and predicts the extent of parallelization necessary by the GPU in order to achieve the desired end-to-end speedup. Our approach does not require creation of GPU code skeletons of the data parallel portions in the sequential code, thereby reducing the performance prediction effort. We observed a reasonably accurate speedup prediction when we tested our approach on multiple well-known Rodinia benchmark applications, a popular matrix multiplication program and a fast Walsh transform program.

Execution profile driven speedup estimation for porting sequential code to GPU

The National Institute of Standards and Technology (NIST) defined cloud computing as a model that enables on-demand access of computing resources These resources are provisioned rapidly, without unnecessary client-provider interaction or management Consumers of cloud get everything, such as software, platform and even the computing infrastructure as a service, rather than an on-premise deployed product The cloud is perceived to be the game-changer of this decade as it offers a flexible, on-demand and elastic delivery method for the business especially servicesTo realize the potential and promise of the cloud, we need to step back and revisit the entire lifecycle of a service from six dimensions, namely the way a service is i) designed, ii) engineered, iii) deployed, iv) measured, v) managed and vi) experienced This will help us identify the key concerns and barriers to its wide scale adoption In this paper, we propose the notion of "next generation service" that embraces cloud computing as its backbone Then we describe each of these six aspects and identify a set of important technical challenges that hinders the complete realization of the next generation service These challenges are still unresolved to a large extent We believe that a satisfactory solution to these challenge will largely help in making the benefits of the next generation service available for a wider consumption

Cloud Based Next Generation Service and Key Challenges

Among all the transportation systems that people use, the public traffic-ways are most common and dangerous resulting in a significant number of fatalities per day worldwide. Statistics have shown that the mortality rates related to traffic accident are more among youth. Although various road safety strategies and rules are developed by the government and law-enforcement agencies to combat the situation, these methods mainly target design, operation, and usability of traffic-ways. Most of the recent data-driven analysis papers model the traffic patterns or predict accidents from the past data. In this paper, we consider a comprehensive, year long fatality analysis reporting system (FARS) data to analyze the role of various factors related to humans, weather and physical conditions (e.g., road surface, light condition etc.) involved in traffic accidents. We build an intelligent risk prediction model that can help decision-makers to ensure road safety. The proposed model estimates (i.) the accident risk over a future time frame, and (ii.) the risk associated with the drivers present on the traffic-way based on the driver’s behavior, history, environmental conditions and physical conditions related to traffic-way.

Analyzing Risky Behavior in Traffic Accidents

Software-as-a-Service (SaaS) offers immense advantages to a subscriber, as the SaaS subscriber pays for the amount of service he has consumed. This pay-per-use model offered by the SaaS provider is supported by the underlying virtualization technology, which allows sharing of the physical infrastructure among several clients who subscribe to the SaaS cloud to optimize the cost of usage. However, with this cost-benefit come several risks related to reliability, security and availability (RAS). Consequently, a potential subscriber of a SaaS offering wants to perform several reviews and validations related to RAS before the subscription. In fact, the subscriber often prefers an independent validation of these QoS aspects, as an on-going basis. In this work, we propose a validation methodology and a tool iCirrus-Val for a SaaS subscriber, to perform an independent analysis and verification of functional errors and vulnerabilities of a SaaS cloud from its weblogs. iCirrus-Val groups the logged URLs into whitelist and suspect categories. A whitelisted URL belonging to a business process, is analyzed for permanent and transient faults. A suspect URL is further analyzed to check if it falls into an “attempt to an attack category”. Our approach is lightweight and does not require data from other parts of the system that is typically unavailable to a SaaS subscriber. It is restricted to a study of RAS from the subscribers entry point. However, we believe that our approach has a potential to identify a large number of the vulnerabilities. Our belief, though not empirically validated here, rests upon recent research findings which indicate that vulnerabilities are increasingly targeted at the entry point such as the web server, as attackers find it difficult to hack the core server.We illustrate our approach using a real-life data of an organization that has adopted a SaaS public cloud.

Santonu Sarkar

Papers

Unified power and energy measurement API for HPC co-processors

Execution profile driven speedup estimation for porting sequential code to GPU

Cloud Based Next Generation Service and Key Challenges

Analyzing Risky Behavior in Traffic Accidents

An independent verification of errors and vulnerabilities in SaaS cloud