Establishing performance requirements of computer based systems subject to uncertainty

An organized systems design approach is dictated by the increasing complexity of computer based systems. Computer based systems are unique in many respects but share many of the same problems that have plagued design engineers for decades. The design of complex systems is difficult at best, but as a design becomes intensively dependent on the computer processing of external and internal information, the design process quickly borders chaos. This situation is exacerbated with the requirement that these systems operate with a minimal quantity of information, generally corrupted by noise, regarding the current state of the system. Establishing performance requirements for such systems is particularly difficult. This paper briefly sketches a general systems design approach with emphasis on the design of computer based decision processing systems subject to parameter and environmental variation. The approach will be demonstrated with application to an on-board diagnostic (OBD) system for automotive emissions systems now mandated by the state of California and the Federal Clean Air Act. The emphasis is on an approach for establishing probabilistically based performance requirements for computer based systems

The aerospace plane design challenge: Credible computational fluid dynamics results

Computational fluid dynamics (CFD) is necessary in the design processes of all current aerospace plane programs. Single-stage-to-orbit (STTO) aerospace planes with air-breathing supersonic combustion are going to be largely designed by means of CFD. The challenge of the aerospace plane design is to provide credible CFD results to work from, to assess the risk associated with the use of those results, and to certify CFD codes that produce credible results. To establish the credibility of CFD results used in design, the following topics are discussed: CFD validation vis-a-vis measurable fluid dynamics (MFD) validation; responsibility for credibility; credibility requirement; and a guide for establishing credibility. Quantification of CFD uncertainties helps to assess success risk and safety risks, and the development of CFD as a design tool requires code certification. This challenge is managed by designing the designers to use CFD effectively, by ensuring quality control, and by balancing the design process. For designing the designers, the following topics are discussed: how CFD design technology is developed; the reasons Japanese companies, by and large, produce goods of higher quality than the U.S. counterparts; teamwork as a new way of doing business; and how ideas, quality, and teaming can be brought together. Quality control for reducing the loss imparted to the society begins with the quality of the CFD results used in the design process, and balancing the design process means using a judicious balance of CFD and MFD

Water Quality Trading and Agricultural Nonpoint Source Pollution: An Analysis of the Effectiveness and Fairness of EPA's Policy on Water Quality Trading

Water quality problems continue to plague our nation, even though Congress passed the Clean Water Act (CWA) to "restore and maintain the chemical, physical, and biological integrity of the Nation's waters"1 more than three decades ago. During the past thirty years, the dominant sources of water pollution have changed, requiring us to seek new approaches for cleaning up our waters. Water quality trading has been heralded as an approach that can integrate market mechanisms into the effort of cleaning up our water. This Article examines the Environmental Protection Agency's (EPA) policy on water quality trading and the prospects for water quality trading to help improve water quality.Part II briefly describes our water quality problems and causes. Part III examines the theoretical basis for trading and the EPA's Water Quality Trading Policy. Part IV discusses the potential impact of total maximum daily loads (TMDLs) on water quality trading, and Part V analyzes potential problems that water quality trading programs confront. Part VI addresses distributional and efficiency concerns that arise when considering trading and agricultural nonpoint source pollution. Part VII then examines issues relating to water quality trading and state laws before reaching conclusions and recommendations in Part VIII

An empirical learning-based validation procedure for simulation workflow

Simulation workflow is a top-level model for the design and control of simulation process. It connects multiple simulation components with time and interaction restrictions to form a complete simulation system. Before the construction and evaluation of the component models, the validation of upper-layer simulation workflow is of the most importance in a simulation system. However, the methods especially for validating simulation workflow is very limit. Many of the existing validation techniques are domain-dependent with cumbersome questionnaire design and expert scoring. Therefore, this paper present an empirical learning-based validation procedure to implement a semi-automated evaluation for simulation workflow. First, representative features of general simulation workflow and their relations with validation indices are proposed. The calculation process of workflow credibility based on Analytic Hierarchy Process (AHP) is then introduced. In order to make full use of the historical data and implement more efficient validation, four learning algorithms, including back propagation neural network (BPNN), extreme learning machine (ELM), evolving new-neuron (eNFN) and fast incremental gaussian mixture model (FIGMN), are introduced for constructing the empirical relation between the workflow credibility and its features. A case study on a landing-process simulation workflow is established to test the feasibility of the proposed procedure. The experimental results also provide some useful overview of the state-of-the-art learning algorithms on the credibility evaluation of simulation models

The State of Network Neutrality Regulation

The Network Neutrality (NN) debate refers to the battle over the design of a regulatory framework for preserving the Internet as a public network and open innovation platform. Fueled by concerns that broadband access service providers might abuse network management to discriminate against third party providers (e.g., content or application providers), policymakers have struggled with designing rules that would protect the Internet from unreasonable network management practices. In this article, we provide an overview of the history of the debate in the U.S. and the EU and highlight the challenges that will confront network engineers designing and operating networks as the debate continues to evolve.BMBF, 16DII111, Verbundprojekt: Weizenbaum-Institut für die vernetzte Gesellschaft - Das Deutsche Internet-Institut; Teilvorhaben: Wissenschaftszentrum Berlin für Sozialforschung (WZB)EC/H2020/679158/EU/Resolving the Tussle in the Internet: Mapping, Architecture, and Policy Making/ResolutioNe

Confidence: Its role in dependability cases for risk assessment

Society is increasingly requiring quantitative assessment of risk and associated dependability cases. Informally, a dependability case comprises some reasoning, based on assumptions and evidence, that supports a dependability claim at a particular level of confidence. In this paper we argue that a quantitative assessment of claim confidence is necessary for proper assessment of risk. We discuss the way in which confidence depends upon uncertainty about the underpinnings of the dependability case (truth of assumptions, correctness of reasoning, strength of evidence), and propose that probability is the appropriate measure of uncertainty. We discuss some of the obstacles to quantitative assessment of confidence (issues of composability of subsystem claims; of the multi-dimensional, multi-attribute nature of dependability claims; of the difficult role played by dependence between different kinds of evidence, assumptions, etc). We show that, even in simple cases, the confidence in a claim arising from a dependability case can be surprisingly low

Report from GI-Dagstuhl Seminar 16394: Software Performance Engineering in the DevOps World

This report documents the program and the outcomes of GI-Dagstuhl Seminar 16394 "Software Performance Engineering in the DevOps World". The seminar addressed the problem of performance-aware DevOps. Both, DevOps and performance engineering have been growing trends over the past one to two years, in no small part due to the rise in importance of identifying performance anomalies in the operations (Ops) of cloud and big data systems and feeding these back to the development (Dev). However, so far, the research community has treated software engineering, performance engineering, and cloud computing mostly as individual research areas. We aimed to identify cross-community collaboration, and to set the path for long-lasting collaborations towards performance-aware DevOps. The main goal of the seminar was to bring together young researchers (PhD students in a later stage of their PhD, as well as PostDocs or Junior Professors) in the areas of (i) software engineering, (ii) performance engineering, and (iii) cloud computing and big data to present their current research projects, to exchange experience and expertise, to discuss research challenges, and to develop ideas for future collaborations