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An Architect's Guide to Site Reliability Engineering Nathaniel T
An Architect's Guide to Site Reliability Engineering Nathaniel T. Schutta @ntschutta ntschutta.io https://content.pivotal.io/ ebooks/thinking-architecturally Sofware development practices evolve. Feature not a bug. It is the agile thing to do! We’ve gone from devs and ops separated by a large wall… To DevOps all the things. We’ve gone from monoliths to service oriented to microserivces. And it isn’t all puppies and rainbows. Shoot. A new role is emerging - the site reliability engineer. Why? What does that mean to our teams? What principles and practices should we adopt? How do we work together? What is SRE? Important to understand the history. Not a new concept but a catchy name! Arguably goes back to the Apollo program. Margaret Hamilton. Crashed a simulator by inadvertently running a prelaunch program. That wipes out the navigation data. Recalculating… Hamilton wanted to add error-checking code to the Apollo system that would prevent this from messing up the systems. But that seemed excessive to her higher- ups. “Everyone said, ‘That would never happen,’” Hamilton remembers. But it did. Right around Christmas 1968. — ROBERT MCMILLAN https://www.wired.com/2015/10/margaret-hamilton-nasa-apollo/ Luckily she did manage to update the documentation. Allowed them to recover the data. Doubt that would have turned into a Hollywood blockbuster… Hope is not a strategy. But it is what rebellions are built on. Failures, uh find a way. Traditionally, systems were run by sys admins. AKA Prod Ops. Or something similar. And that worked OK. For a while. But look around your world today. -
Chapter 6 Structural Reliability
MIL-HDBK-17-3E, Working Draft CHAPTER 6 STRUCTURAL RELIABILITY Page 6.1 INTRODUCTION ....................................................................................................................... 2 6.2 FACTORS AFFECTING STRUCTURAL RELIABILITY............................................................. 2 6.2.1 Static strength.................................................................................................................... 2 6.2.2 Environmental effects ........................................................................................................ 3 6.2.3 Fatigue............................................................................................................................... 3 6.2.4 Damage tolerance ............................................................................................................. 4 6.3 RELIABILITY ENGINEERING ................................................................................................... 4 6.4 RELIABILITY DESIGN CONSIDERATIONS ............................................................................. 5 6.5 RELIABILITY ASSESSMENT AND DESIGN............................................................................. 6 6.5.1 Background........................................................................................................................ 6 6.5.2 Deterministic vs. Probabilistic Design Approach ............................................................... 7 6.5.3 Probabilistic Design Methodology..................................................................................... -
Existing Cybernetics Foundations - B
SYSTEMS SCIENCE AND CYBERNETICS – Vol. III - Existing Cybernetics Foundations - B. M. Vladimirski EXISTING CYBERNETICS FOUNDATIONS B. M. Vladimirski Rostov State University, Russia Keywords: Cybernetics, system, control, black box, entropy, information theory, mathematical modeling, feedback, homeostasis, hierarchy. Contents 1. Introduction 2. Organization 2.1 Systems and Complexity 2.2 Organizability 2.3 Black Box 3. Modeling 4. Information 4.1 Notion of Information 4.2 Generalized Communication System 4.3 Information Theory 4.4 Principle of Necessary Variety 5. Control 5.1 Essence of Control 5.2 Structure and Functions of a Control System 5.3 Feedback and Homeostasis 6. Conclusions Glossary Bibliography Biographical Sketch Summary Cybernetics is a science that studies systems of any nature that are capable of perceiving, storing, and processing information, as well as of using it for control and regulation. UNESCO – EOLSS The second title of the Norbert Wiener’s book “Cybernetics” reads “Control and Communication in the Animal and the Machine”. However, it is not recognition of the external similaritySAMPLE between the functions of animalsCHAPTERS and machines that Norbert Wiener is credited with. That had been done well before and can be traced back to La Mettrie and Descartes. Nor is it his contribution that he introduced the notion of feedback; that has been known since the times of the creation of the first irrigation systems in ancient Babylon. His distinctive contribution lies in demonstrating that both animals and machines can be combined into a new, wider class of objects which is characterized by the presence of control systems; furthermore, living organisms, including humans and machines, can be talked about in the same language that is suitable for a description of any teleological (goal-directed) systems. -
An Introduction to Psychometric Theory with Applications in R
What is psychometrics? What is R? Where did it come from, why use it? Basic statistics and graphics TOD An introduction to Psychometric Theory with applications in R William Revelle Department of Psychology Northwestern University Evanston, Illinois USA February, 2013 1 / 71 What is psychometrics? What is R? Where did it come from, why use it? Basic statistics and graphics TOD Overview 1 Overview Psychometrics and R What is Psychometrics What is R 2 Part I: an introduction to R What is R A brief example Basic steps and graphics 3 Day 1: Theory of Data, Issues in Scaling 4 Day 2: More than you ever wanted to know about correlation 5 Day 3: Dimension reduction through factor analysis, principal components analyze and cluster analysis 6 Day 4: Classical Test Theory and Item Response Theory 7 Day 5: Structural Equation Modeling and applied scale construction 2 / 71 What is psychometrics? What is R? Where did it come from, why use it? Basic statistics and graphics TOD Outline of Day 1/part 1 1 What is psychometrics? Conceptual overview Theory: the organization of Observed and Latent variables A latent variable approach to measurement Data and scaling Structural Equation Models 2 What is R? Where did it come from, why use it? Installing R on your computer and adding packages Installing and using packages Implementations of R Basic R capabilities: Calculation, Statistical tables, Graphics Data sets 3 Basic statistics and graphics 4 steps: read, explore, test, graph Basic descriptive and inferential statistics 4 TOD 3 / 71 What is psychometrics? What is R? Where did it come from, why use it? Basic statistics and graphics TOD What is psychometrics? In physical science a first essential step in the direction of learning any subject is to find principles of numerical reckoning and methods for practicably measuring some quality connected with it. -
Introduction to Cybernetics and the Design of Systems
Introduction to Cybernetics and the Design of Systems © Hugh Dubberly & Paul Pangaro 2004 Cybernetics named From Greek ‘kubernetes’ — same root as ‘steering’ — becomes ‘governor’ in Latin Steering wind or tide course set Steering wind or tide course set Steering wind or tide course set Steering wind or tide course set correction of error Steering wind or tide course set correction of error Steering wind or tide course set correction of error correction of error Steering wind or tide course set correction of error correction of error Steering wind or tide course set correction of error correction of error Cybernetics named From Greek ‘kubernetes’ — same root as ‘steering’ — becomes ‘governor’ in Latin Cybernetic point-of-view - system has goal - system acts, aims toward the goal - environment affects aim - information returns to system — ‘feedback’ - system measures difference between state and goal — detects ‘error’ - system corrects action to aim toward goal - repeat Steering as a feedback loop compares heading with goal of reaching port adjusts rudder to correct heading ship’s heading Steering as a feedback loop detection of error compares heading with goal of reaching port adjusts rudder feedback to correct heading correction of error ship’s heading Automation of feedback thermostat heater temperature of room air Automation of feedback thermostat compares to setpoint and, if below, activates measured by heater raises temperature of room air The feedback loop ‘Cybernetics introduces for the first time — and not only by saying it, but -
Training-Sre.Pdf
C om p lim e nt s of Training Site Reliability Engineers What Your Organization Needs to Create a Learning Program Jennifer Petoff, JC van Winkel & Preston Yoshioka with Jessie Yang, Jesus Climent Collado & Myk Taylor REPORT Want to know more about SRE? To learn more, visit google.com/sre Training Site Reliability Engineers What Your Organization Needs to Create a Learning Program Jennifer Petoff, JC van Winkel, and Preston Yoshioka, with Jessie Yang, Jesus Climent Collado, and Myk Taylor Beijing Boston Farnham Sebastopol Tokyo Training Site Reliability Engineers by Jennifer Petoff, JC van Winkel, and Preston Yoshioka, with Jessie Yang, Jesus Climent Collado, and Myk Taylor Copyright © 2020 O’Reilly Media. All rights reserved. Printed in the United States of America. Published by O’Reilly Media, Inc., 1005 Gravenstein Highway North, Sebastopol, CA 95472. O’Reilly books may be purchased for educational, business, or sales promotional use. Online editions are also available for most titles (http://oreilly.com). For more infor‐ mation, contact our corporate/institutional sales department: 800-998-9938 or [email protected]. Acquistions Editor: John Devins Proofreader: Charles Roumeliotis Development Editor: Virginia Wilson Interior Designer: David Futato Production Editor: Beth Kelly Cover Designer: Karen Montgomery Copyeditor: Octal Publishing, Inc. Illustrator: Rebecca Demarest November 2019: First Edition Revision History for the First Edition 2019-11-15: First Release See http://oreilly.com/catalog/errata.csp?isbn=9781492076001 for release details. The O’Reilly logo is a registered trademark of O’Reilly Media, Inc. Training Site Reli‐ ability Engineers, the cover image, and related trade dress are trademarks of O’Reilly Media, Inc. -
Environmental Systems Analysis Tools for Decision-Making
Environmental systems analysis tools for decision-making LCA and Swedish waste management as an example Åsa Moberg Licentiate thesis Royal Institute of Technology Department of Urban Planning and Environment Environmental Strategies Research Stockholm 2006 Titel: Environmental systems analysis tools for decision‐making LCA and Swedish waste management as an example Author: Åsa Moberg Cover page photo: Marianne Lockner TRITA‐SOM 06‐002 ISSN 1653‐6126 ISRN KTH/SOM/‐‐06/002‐‐SE ISBN 91‐7178‐304‐0 Printed in Sweden by US AB, Stockholm, 2006 2 Abstract Decisions are made based on information of different kinds. Several tools have been developed to facilitate the inclusion of environmental aspects in decision‐making on different levels. Which tool to use in a specific decision‐making situation depends on the decision context. This thesis discusses the choice between different environmental systems analysis (ESA) tools and suggests that key factors influencing the choice of ESA tool are object of study, impacts considered and information type regarding site‐specificity and according to the DPSIR‐framework. Waste management in Sweden is used as an example to illustrate decision‐making situations, but discussions concerning choice of tools are also thought to be of general concern. It is suggested that there is a need for a number of ESA tools in waste management decision‐making. Procedural tools like Environmental Impact Assessment (EIA) and Strategic Environmental Assessment (SEA) should be used e.g. by companies applying for development of waste management facilities and by public authorities preparing plans and programmes. Within these procedural tools analytical tools providing relevant information could be used, e.g. -
Cluster Analysis for Gene Expression Data: a Survey
Cluster Analysis for Gene Expression Data: A Survey Daxin Jiang Chun Tang Aidong Zhang Department of Computer Science and Engineering State University of New York at Buffalo Email: djiang3, chuntang, azhang @cse.buffalo.edu Abstract DNA microarray technology has now made it possible to simultaneously monitor the expres- sion levels of thousands of genes during important biological processes and across collections of related samples. Elucidating the patterns hidden in gene expression data offers a tremen- dous opportunity for an enhanced understanding of functional genomics. However, the large number of genes and the complexity of biological networks greatly increase the challenges of comprehending and interpreting the resulting mass of data, which often consists of millions of measurements. A first step toward addressing this challenge is the use of clustering techniques, which is essential in the data mining process to reveal natural structures and identify interesting patterns in the underlying data. Cluster analysis seeks to partition a given data set into groups based on specified features so that the data points within a group are more similar to each other than the points in different groups. A very rich literature on cluster analysis has developed over the past three decades. Many conventional clustering algorithms have been adapted or directly applied to gene expres- sion data, and also new algorithms have recently been proposed specifically aiming at gene ex- pression data. These clustering algorithms have been proven useful for identifying biologically relevant groups of genes and samples. In this paper, we first briefly introduce the concepts of microarray technology and discuss the basic elements of clustering on gene expression data. -
A Quantitative Reliability, Maintainability and Supportability Approach for NASA's Second Generation Reusable Launch Vehicle
A Quantitative Reliability, Maintainability and Supportability Approach for NASA's Second Generation Reusable Launch Vehicle Fayssai M. Safie, Ph. D. Marshall Space Flight Center Huntsville, Alabama Tel: 256-544-5278 E-mail: Fayssal.Safie @ msfc.nasa.gov Charles Daniel, Ph.D. Marshall Space Flight Center Huntsville, Alabama Tel: 256-544-5278 E-mail: Charles.Daniel @msfc.nasa.gov Prince Kalia Raytheon ITSS Marshall Space Flight Center Huntsville, Alabama Tel: 256-544-6871 E-mail: Prince.Kalia @ msfc.nasa.gov ABSTRACT The United States National Aeronautics and Space Administration (NASA) is in the midst of a 10-year Second Generation Reusable Launch Vehicle (RLV) program to improve its space transportation capabilities for both cargo and crewed missions. The objectives of the program are to: significantly increase safety and reliability, reduce the cost of accessing low-earth orbit, attempt to leverage commercial launch capabilities, and provide a growth path for manned space exploration. The safety, reliability and life cycle cost of the next generation vehicles are major concerns, and NASA aims to achieve orders of magnitude improvement in these areas. To get these significant improvements, requires a rigorous process that addresses Reliability, Maintainability and Supportability (RMS) and safety through all the phases of the life cycle of the program. This paper discusses the RMS process being implemented for the Second Generation RLV program. 1.0 INTRODUCTION The 2nd Generation RLV program has in place quantitative Level-I RMS, and cost requirements [Ref 1] as shown in Table 1, a paradigm shift from the Space Shuttle program. This paradigm shift is generating a change in how space flight system design is approached. -
Reliability Engineering: Today and Beyond
Reliability Engineering: Today and Beyond Keynote Talk at the 6th Annual Conference of the Institute for Quality and Reliability Tsinghua University People's Republic of China by Professor Mohammad Modarres Director, Center for Risk and Reliability Department of Mechanical Engineering Outline – A New Era in Reliability Engineering – Reliability Engineering Timeline and Research Frontiers – Prognostics and Health Management – Physics of Failure – Data-driven Approaches in PHM – Hybrid Methods – Conclusions New Era in Reliability Sciences and Engineering • Started as an afterthought analysis – In enduing years dismissed as a legitimate field of science and engineering – Worked with small data • Three advances transformed reliability into a legitimate science: – 1. Availability of inexpensive sensors and information systems – 2. Ability to better described physics of damage, degradation, and failure time using empirical and theoretical sciences – 3. Access to big data and PHM techniques for diagnosing faults and incipient failures • Today we can predict abnormalities, offer just-in-time remedies to avert failures, and making systems robust and resilient to failures Seventy Years of Reliability Engineering – Reliability Engineering Initiatives in 1950’s • Weakest link • Exponential life model • Reliability Block Diagrams (RBDs) – Beyond Exp. Dist. & Birth of System Reliability in 1960’s • Birth of Physics of Failure (POF) • Uses of more proper distributions (Weibull, etc.) • Reliability growth • Life testing • Failure Mode and Effect Analysis -
Collection System Manager
CITY OF DALY CITY JOB SPECIFICATION EXEMPT POSITION COLLECTION SYSTEM MANAGER DEFINITION Under the general direction of the Director of Water and Wastewater Resources or authorized representative, supervises all activities of the Collection System Maintenance Division, including wastewater collection and recycled water systems, and performs other duties as assigned. EXAMPLES OF DUTIES Manage, supervise, schedule, and review the work of maintenance personnel in the maintenance, inspection and repair of wastewater collection and recycled water distribution systems in the Collection System Maintenance Division of the Department of Water and Wastewater Resources; ensure safe, efficient and effective compliance with local, state and federal laws, rules and regulations, and industrial practices. Implement City, Department and Division Rules and Regulations, policies, procedures and practices. Ensure that required records are accurately maintained. Oversee a comprehensive preventive maintenance program of facilities. Ensure that supervisors conduct regular safety training and maintain a safe work environment. Develop and maintain training programs for employees; ensure that employees maintain required certification. Formally evaluate or ensure the evaluation and satisfactory job performance of assigned employees. Manage crews in support of City departments in maintenance operations, such as the storm drain collection system maintenance and construction program. Work to complete Department and Division goals and objectives. Prepare and manage the Division budget; propose, develop and manage capital projects and manage contracts for the Division. Prepare reports, and make presentations to the City Manager, City Council or others as required. When required, maintain 24-hour availability to address Division emergencies. Develop and maintain good working relationships with supervisors, coworkers, elected officials, professional peer groups and the public. -
Reliability: Software Software Vs
Reliability Theory SENG 521 Re lia bility th eory d evel oped apart f rom th e mainstream of probability and statistics, and Software Reliability & was usedid primar ily as a tool to h hlelp Software Quality nineteenth century maritime and life iifiblinsurance companies compute profitable rates Chapter 5: Overview of Software to charge their customers. Even today, the Reliability Engineering terms “failure rate” and “hazard rate” are often used interchangeably. Department of Electrical & Computer Engineering, University of Calgary Probability of survival of merchandize after B.H. Far ([email protected]) 1 http://www. enel.ucalgary . ca/People/far/Lectures/SENG521/ ooene MTTF is R e 0.37 From Engineering Statistics Handbook [email protected] 1 [email protected] 2 Reliability: Natural System Reliability: Hardware Natural system Hardware life life cycle. cycle. Aging effect: Useful life span Life span of a of a hardware natural system is system is limited limited by the by the age (wear maximum out) of the system. reproduction rate of the cells. Figure from Pressman’s book Figure from Pressman’s book [email protected] 3 [email protected] 4 Reliability: Software Software vs. Hardware So ftware life cyc le. Software reliability doesn’t decrease with Software systems time, i.e., software doesn’t wear out. are changed (updated) many Hardware faults are mostly physical faults, times during their e. g., fatigue. life cycle. Each update adds to Software faults are mostly design faults the structural which are harder to measure, model, detect deterioration of the and correct. software system. Figure from Pressman’s book [email protected] 5 [email protected] 6 Software vs.