Causes Of Performance Problems Research Articles

Internal fault probability-based time domain differential protection applied to transmission lines connecting battery energy storage stations

Battery energy storage station (BESS) presents disparate fault current characteristics in charge and discharge states. Classic and recently proposed differential protection algorithms, including phasor-based and time-domain algorithms, are heavily influenced by operating state of BESS. Problems such as low sensitivity and ineffectiveness may arise, especially in charge state. In response to this issue, this paper applies Bayes' Theorem to calculate the probability of internal fault occurrence, named as internal fault probability. In this article, fault current characteristics of BESS under different operating states are described, and then the causes of performance problems of classic and recently proposed differential protection algorithms are analyzed. Subsequently, virtual operating current vector is developed, and the difference of virtual operating current vector in time-domain waveform under various conditions is described. On this basis, internal fault probability-based differential protection (IFP-DP) is presented. Besides, its operating performances under internal and external faults are analyzed. The advantageous performance of IFP-DP is fully validated by changing fault location, type, resistance, operating state and considering outliers, current transformer (CT) saturation, and loss of data. Proposed IFP-DP exhibits gratifying performance in operating time, sensitivity and security in the presence of BESS.

Inferring Unobserved Events in Systems with Shared Resources and Queues

To identify the causes of performance problems or to predict process behavior, it is essential to have correct and complete event data. This is particularly important for distributed systems with shared resources, e.g., one case can block another case competing for the same machine, leading to inter-case dependencies in performance. However, due to a variety of reasons, real-life systems often record only a subset of all events taking place. To understand and analyze the behavior and performance of processes with shared resources, we aim to reconstruct bounds for timestamps of events in a case that must have happened but were not recorded by inference over events in other cases in the system. We formulate and solve the problem by systematically introducing multi-entity concepts in event logs and process models. We introduce a partial-order based model of a multi-entity event log and a corresponding compositional model for multi-entity processes. We define PQR-systems as a special class of multi-entity processes with shared resources and queues. We then study the problem of inferring from an incomplete event log unobserved events and their timestamps that are globally consistent with a PQR-system. We solve the problem by reconstructing unobserved traces of resources and queues according to the PQR-model and derive bounds for their timestamps using a linear program. While the problem is illustrated for material handling systems like baggage handling systems in airports, the approach can be applied to other settings where recording is incomplete. The ideas have been implemented in ProM and were evaluated using both synthetic and real-life event logs.

AngularJS Performance: A Survey Study

AngularJS is a popular JavaScript MVC-based framework to construct single-page web applications. In this paper, we report the results of a survey with 95 professional developers about performance issues of AngularJS applications. We report common practices followed by developers to avoid performance problems (e.g., use of third-party or custom components), the general causes of performance problems in AngularJS applications (e.g., inadequate architecture decisions taken by AngularJS users), and the technical and specific causes of performance problems (e.g., unnecessary processing included in the digest cycle, which is the internal computation that automatically updates the view with changes detected in the model).

InvarNet-X: A Black-Box Invariant-Based Approach to Diagnosing Big Data Systems

As big data spreads rapidly, performance problems in these systems become common concerns. As the first line of defending these problems, performance diagnosis plays an essential role in big data systems. It is notoriously difficult to conduct performance diagnosis in large distributed systems. Previous work either pinpoint the root causes by instrumenting the applications or runtime systems in a white-box way, which leads to a considerable overhead, or just provide some hints to the hidden root causes in a black-box way. Very few works focus on pinpointing the real root causes in a black-box way. To address this problem, this paper proposes a black-box invariant-based diagnosing approach and implements a proof-of-concept system named InvarNet-X . In this paper, performance diagnosis is formalized as a pattern recognition problem, meaning that each performance problem is identified by a specific pattern. The rationale of InvarNet-X is that the unobservable root causes of performance problems always expose themselves through the violations of the associations among directly observable performance metrics. Such observable associations are called likely invariants calculated by the maximal information criterion, and each performance problem is signified by a sparse distributed representation. A problem signature database is constructed by training multiple real performance problems in advance. Once a performance anomaly is detected, the diagnosing procedure is triggered. The root cause is pinpointed by retrieving similar signatures in the signature database. The experimental evaluations in a controlled big data system show that InvarNet-X can achieve a high accuracy in diagnosing some real performance problems reported in software bug repositories, which is superior to several state-of-the-art approaches. Moreover, the light-weight property makes InvarNet-X easily facilitated in large-scale big data systems in real time.

Distance Based Root Cause Analysis and Change Impact Analysis of Performance Regressions

Performance regression testing is applied to uncover both performance and functional problems of software releases. A performance problem revealed by performance testing can be high response time, low throughput, or even being out of service. Mature performance testing process helps systematically detect software performance problems. However, it is difficult to identify the root cause and evaluate the potential change impact. In this paper, we present an approach leveraging server side logs for identifying root causes of performance problems. Firstly, server side logs are used to recover call tree of each business transaction. We define a novel distance based metric computed from call trees for root cause analysis and apply inverted index from methods to business transactions for change impact analysis. Empirical studies show that our approach can effectively and efficiently help developers diagnose root cause of performance problems.

Going Forward: A Lean Way to Design

That's not what I meant. Generally speaking, this phrase does not mean the end of the world, and the affected parties can quickly come to terms and remedy the misunderstanding. In the world of design, how- ever, such a phrase can imply disaster for the designer and client, if not resolved early enough in the process of a building project. And in the history of design, this phrase is probably heard more often than many designers are willing to admit.As with many areas where inspiration evolves organically with the passing of time, the world of design and build is undergoing a renaissance. New tools and approaches, including patient-centered design, computer simulation and itera- tive data-driven design are being applied in the healthcare setting, and this issue of HERD is dedicated to examining them. In each of these thought-provoking articles, we challenge conventional approaches to design-and conventional wis- dom-and offer new techniques that promise to create more effective environ- ments in which to practice and receive care.The idea that conventional wisdom must be challenged in the world of design grew from the very successful application of Lean and Six Sigma principles in hospital operations, dating back over a decade. In 1999, nobody in healthcare had heard of these methods, and it might not be unusual to hear in the halls of the hospital is Sigma and why is she sick? Today, two separate studies, one by the American College of Healthcare Executives and the other by the Ameri- can Society for Quality, indicate that over 40% of hospitals have applied Lean and Six Sigma to improve quality and reduce costs.My own experience with these methods has been consistent with industry. In 2000, I earned a Blackbelt in Six Sigma and went about helping hospitals to realize their full potential in operational, workflow and quality changes. Unfor- tunately, even the smallest amount of data can be intoxicating to managers, who are often quick to conclude that they understand the root cause of performance problems. Executives were absolutely convinced they knew how to improve pro- cesses and many initiated performance improvement efforts with a solution in mind. The industry became replete with examples of performance improvement efforts that actually had a deleterious effect because they were based on flawed data. It would take another several years before the industry realized that for data-driven design to work, it must demand accurate data, patience, and the ability to surrender to the process of discovery before rushing to solutions.Fortunately, such patience has paid off. In a study conducted by Creative Health- care including over 150 projects conducted by 10 healthcare systems over the past 10 years, Six Sigma projects were shown to yield an average return on invest- ment (ROI) of 7:1. With this compelling evidence to embolden us, it's time to take these methods as far as they can go.How does one begin the process of undertaking a Lean or Six Sigma journey in an environment where such concepts are potentially new and unproven? A research paper offered by Maartje van Reedt Dortland et al. in this issue of HERD discusses the findings from an exploratory survey with options for con- struction that facilitate understanding of the contemporary view of available options, while another research paper by Vanessa Okken et al. …

A study of determining factors affecting the performance of nurses based on the achieve model in selected hospital of Isfahan (Iran)

Background:The achieve uses 7 variables: ability; clarity; help; incentive; evaluation; validity; and environment, to provide leaders and staffs with the necessary tools to improve performance, and help managers to determine cause of performance problem. And create change strategy for solving those problems. We try to determine factors affecting the performance of nurses, base on this model in selected hospital of Isfahan (Iran).Materials and Methods:This was a descriptive-correlation survey. The population of study was 85 nurses of 9 hospitals in Esfahan. Data gathering was done via achieve questionnaire. Reliability was calculated by Cronbach's alpha (r = 0.85). Data analysis was done by using SPSS 16.Result:The ability and help variables were the most important factors in improving performance of nurses and validity and evaluation were less important.Conclution:Today, staff effectiveness is one of the important problems in health care organization. In fact, performance improvement is the most important step for organization improvement.

Sports in America from Colonial times to the twenty-first century: an encyclopedia

Here's practical help for the day-to-day concerns that keep managers awake at night. Written in an informal, first-person style, this useful book fills the gap between the legal and policy issues that are the mainstay of human resources and supervision courses and the real-world needs of managers as they attempt to cope with the human side of their jobs. author is a noted scholar in both cognitive psychology and organizational studies, and has drawn from extensive personal experience as well as careful observation of good and bad managers. The Human Element is organized around six fundamental commitments that good employee managers make in order to succeed. It is filled with practical examples and step-by-step guidelines for performing important tasks and dealing with common problems - everything from how to conduct a meeting, to how to write a code of conduct, to how to diagnose the cause of performance problems. The Human Element is designed to reduce the stress of management by providing insight into why employees do what they do, and what to do about it. It is an ideal supplement for any course in people management, including supervision, HRM, and applied OB courses.

MT-WAVE

Modern web applications consist of many distinct services that collaborate to provide the full application functionality. To improve application performance, developers need to be able to identify the root cause of performance problems; identifying and fixing performance problems in these distributed, heterogeneous applications can be very difficult. As web applications become more complicated, the number of systems involved will continue to grow and full-system performance tuning will become more difficult. We postulate that multi-tier profiling, starting at the web browser, is the appropriate way to solve this problem. Instrumenting from the web browser, as the user experiences it, ensures that we can tell what each service in the application is contributing to overall page-load time; thus, each tier must provide instrumentation data that developers can use to quickly identify the root cause of performance problems. We have built MT-WAVE, a system that integrates with the different tiers of a web application (including a browser extension) and collects light-weight instrumentation to a central location via X-Trace facilities. The collected data is presented with our visualization system that provides varying levels of detail. To validate our approach, we performed case studies of two applications, both showing performance insight. In particular, we identified and fixed a significant and unintuitive bottleneck in an open-source project management application and verified caching behaviour in a cloud-hosted commercial product. While specific technologies are used in our case study, we believe that most web technologies in common use today would require straightforward modifications to be able to utilize MT-WAVE tracing facilities. This tool is designed to be used by application developers and system administrators while testing new software, or after deployment when it becomes clear that existing performance is not meeting user needs.

Abstract only

Modern web applications consist of many distinct services that collaborate to provide the full application functionality. To improve application performance, developers need to be able to identify the root cause of performance problems; identifying and fixing performance problems in these distributed, heterogeneous applications can be very difficult. As web applications become more complicated, the number of systems involved will continue to grow and full-system performance tuning will become more difficult.

DIADS

Many enterprise environments have databases running on network-attached storage infrastructure (referred to as Storage Area Networks or SANs ). Both the database and the SAN are complex subsystems that are managed by separate teams of administrators. As often as not, database administrators have limited understanding of SAN configuration and behavior, and limited visibility into the SAN's run-time performance; and vice versa for the SAN administrators. Diagnosing the cause of performance problems is a challenging exercise in these environments. We propose to remedy the situation through a novel tool, called Diads, for database and SAN problem diagnosis. This demonstration proposal summarizes the technical innovations in Diads: (i) a powerful abstraction called Annotated Plan Graphs (APGs) that ties together the execution path of queries in the database and the SAN using low-overhead monitoring data, and (ii) a diagnosis workflow that combines domain-specific knowledge with machine-learning techniques. The scenarios presented in the demonstration are also described.

Abstract at the Least (Reviews of Data-Based Studies of a Whimsical Nature)

Performance technologists consider motivational, environmental, and knowledge deficiencies as they diagnose causes of performance problems. Environmental factors are assumed to include equipment, space, and ergonomic considerations to name a few. Andrew Smith and Miles extend our understanding of the effects on performance of an often overlooked environmental factor - lunch, in their study: “The Effects of Lunch on Cognitive Vigilance Tasks.”

A methodology for root cause analysis of poor performance in fixed-wireless data networks

In fixed-wireless data networks, poor performance experienced by users, such as excessive delays during file transfers, might be due to a heavily utilized base station or due to the location of the users relative to the base station. A principal component analysis based methodology that may be used by content providers for analyzing the root cause of performance problems is presented. The methodology requires use of only flow level Internet measurements collected at content provider's site and provides an effective diagnostic tool for network managers.

Continuous profiling

This article describes the Digital Continuous Profiling Infrastructure, a sampling-based profiling system designed to run continuously on production systems. The system supports multiprocessors, works on unmodified executables, and collects profiles for entire systems, including user programs, shared libraries, and the operating system kernel. Samples are collected at a high rate (over 5200 samples/sec. per 333MHz processor), yet with low overhead (1–3% slowdown for most workloads). Analysis tools supplied with the profiling system use the sample data to produce a precise and accurate accounting, down to the level of pipeline stalls incurred by individual instructions, of where time is bring spent. When instructions incur stalls, the tools identify possible reasons, such as cache misses, branch mispredictions, and functional unit contention. The fine-grained instruction-level analysis guides users and automated optimizers to the causes of performance problems and provides important insights for fixing them.