Solutions for a class of iterated singular equations

Some fundamental solutions of radial type for a class of iterated elliptic singular equations including the iterated Euler equation are given.Comment: 7 page

Towards Operator-less Data Centers Through Data-Driven, Predictive, Proactive Autonomics

Continued reliance on human operators for managing data centers is a major impediment for them from ever reaching extreme dimensions. Large computer systems in general, and data centers in particular, will ultimately be managed using predictive computational and executable models obtained through data-science tools, and at that point, the intervention of humans will be limited to setting high-level goals and policies rather than performing low-level operations. Data-driven autonomics, where management and control are based on holistic predictive models that are built and updated using live data, opens one possible path towards limiting the role of operators in data centers. In this paper, we present a data-science study of a public Google dataset collected in a 12K-node cluster with the goal of building and evaluating predictive models for node failures. Our results support the practicality of a data-driven approach by showing the effectiveness of predictive models based on data found in typical data center logs. We use BigQuery, the big data SQL platform from the Google Cloud suite, to process massive amounts of data and generate a rich feature set characterizing node state over time. We describe how an ensemble classifier can be built out of many Random Forest classifiers each trained on these features, to predict if nodes will fail in a future 24-hour window. Our evaluation reveals that if we limit false positive rates to 5%, we can achieve true positive rates between 27% and 88% with precision varying between 50% and 72%.This level of performance allows us to recover large fraction of jobs' executions (by redirecting them to other nodes when a failure of the present node is predicted) that would otherwise have been wasted due to failures. [...

Towards Data-Driven Autonomics in Data Centers

Continued reliance on human operators for managing data centers is a major impediment for them from ever reaching extreme dimensions. Large computer systems in general, and data centers in particular, will ultimately be managed using predictive computational and executable models obtained through data-science tools, and at that point, the intervention of humans will be limited to setting high-level goals and policies rather than performing low-level operations. Data-driven autonomics, where management and control are based on holistic predictive models that are built and updated using generated data, opens one possible path towards limiting the role of operators in data centers. In this paper, we present a data-science study of a public Google dataset collected in a 12K-node cluster with the goal of building and evaluating a predictive model for node failures. We use BigQuery, the big data SQL platform from the Google Cloud suite, to process massive amounts of data and generate a rich feature set characterizing machine state over time. We describe how an ensemble classifier can be built out of many Random Forest classifiers each trained on these features, to predict if machines will fail in a future 24-hour window. Our evaluation reveals that if we limit false positive rates to 5%, we can achieve true positive rates between 27% and 88% with precision varying between 50% and 72%. We discuss the practicality of including our predictive model as the central component of a data-driven autonomic manager and operating it on-line with live data streams (rather than off-line on data logs). All of the scripts used for BigQuery and classification analyses are publicly available from the authors' website.Comment: 12 pages, 6 figure

God and Tawhid in Classical Islamic Theology and Said Nursi's Risale-i Nur

Theology is a rational endeavour to understand everything about God, from within a faith tradition and its scriptures, and in response to problems posed by the conditions of a particular time and place. Islamic theology, in particular, has been a reactive discipline. Bediuzzaman Said Nursi (1876-1960), as a prominent scholar in the modern era, lived through a tumultuous period witnessing the collapse of the Ottoman Empire, the emergence of secular nation states for the first time in Muslim history, two world wars, and the challenges imposed by European modernity on traditional Muslim societies and Islam. Unlike other revivalist leaders, in dealing with the complexity of circumstances and the social and political restrictions around him, Nursi chose to respond following a theological revival method, where he attempted to revive Islam by renewing faith in people through his theological writings. By loading so much significance and revivalist objectives to theology, Nursi produced an original and fresh expression of Islamic theology based on the Qur’an. In this thesis, my original contribution to knowledge is the critical evaluation of Nursi’s writings about God and identification of his contributions to Islamic understanding of God and tawḥīd as the central doctrine of Islam

A Big Data Analyzer for Large Trace Logs

Current generation of Internet-based services are typically hosted on large data centers that take the form of warehouse-size structures housing tens of thousands of servers. Continued availability of a modern data center is the result of a complex orchestration among many internal and external actors including computing hardware, multiple layers of intricate software, networking and storage devices, electrical power and cooling plants. During the course of their operation, many of these components produce large amounts of data in the form of event and error logs that are essential not only for identifying and resolving problems but also for improving data center efficiency and management. Most of these activities would benefit significantly from data analytics techniques to exploit hidden statistical patterns and correlations that may be present in the data. The sheer volume of data to be analyzed makes uncovering these correlations and patterns a challenging task. This paper presents BiDAl, a prototype Java tool for log-data analysis that incorporates several Big Data technologies in order to simplify the task of extracting information from data traces produced by large clusters and server farms. BiDAl provides the user with several analysis languages (SQL, R and Hadoop MapReduce) and storage backends (HDFS and SQLite) that can be freely mixed and matched so that a custom tool for a specific task can be easily constructed. BiDAl has a modular architecture so that it can be extended with other backends and analysis languages in the future. In this paper we present the design of BiDAl and describe our experience using it to analyze publicly-available traces from Google data clusters, with the goal of building a realistic model of a complex data center.Comment: 26 pages, 10 figure

Cultural Literacy Skills of University Students Who Get Arts Education on Art Works

This study aims to determine the perception levels of art educator candidate students of traditional culture and popular culture symbols or expressions on the works of art. This study is important in terms comprehension of students' perception skills and to determine whether these understandings are reflected in their artistic skills or not. A survey was conducted to find out the perception of culture on a total of 70 students studying in the 1st, 2nd, 3rd, 4th grades at the Painting Education Department of Necmettin Erbakan University, Ahmet Keleşoğlu Education Faculty, Fine Arts Education Department. Art works containing cultural symbols and expressions were used by the researcher in the study. Student views were taken over eight works of art with details from different cultures. The data of the research where the qualitative research method was used are collected in the form of written opinions about the works. In the analysis of the work, the data obtained from the views taken with the help of the questions which reveal the nature of the art history of the work was analyzed and interpreted through content analysis. Keywords: Culture, Popular Culture, Art Education, Cultural Perception