Performance Engineering Approach Research Articles

Pegasus: Performance Engineering for Software Applications Targeting HPC Systems

Developing and optimizing software applications for high performance and energy efficiency is a very challenging task, even when considering a single target machine. For instance, optimizing for multicore-based computing systems requires in-depth knowledge about programming languages, application programming interfaces, compilers, performance tuning tools, and computer architecture and organization. Many of the tasks of performance engineering methodologies require manual efforts and the use of different tools not always part of an integrated toolchain. This paper presents Pegasus, a performance engineering approach supported by a framework that consists of a source-to-source compiler, controlled and guided by strategies programmed in a Domain-Specific Language, and an autotuner. Pegasus is a holistic and versatile approach spanning various decision layers composing the software stack, and exploiting the system capabilities and workloads effectively through the use of runtime autotuning. The Pegasus approach helps developers by automating tasks regarding the efficient implementation of software applications in multicore computing systems. These tasks focus on application analysis, profiling, code transformations, and the integration of runtime autotuning. Pegasus allows developers to program their strategies or to automatically apply existing strategies to software applications in order to ensure the compliance of non-functional requirements, such as performance and energy efficiency. We show how to apply Pegasus and demonstrate its applicability and effectiveness in a complex case study, which includes tasks from a smart navigation system.

FOCloud: Feature Model Guided Performance Prediction and Explanation for Deployment Configurable Cloud Applications

The increasing heterogeneity of the VM offerings on public IaaS clouds gives rise to a very large number of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">deployment options</i> for constructing distributed, multi-component cloud applications. However, selecting an appropriate <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">deployment variant</i> , i.e., a valid combination of deployment options, to meet required performance levels is non-trivial. The combinatorial explosion of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">deployment space</i> makes it infeasible to measure the performance of all deployment variants to build a comprehensive empirical performance model. To address this problem, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Feature-Oriented Cloud</i> (FOCloud), a performance engineering approach for deployment configurable cloud applications. FOCloud (i) uses feature modeling to structure and constrain the valid deployment space by modeling the commonalities and variations in the different deployment options and their inter-dependencies, (ii) uses sampling and machine learning to incrementally and cost-effectively build a performance prediction model whose input variables are the deployment options, and the output variable is the performance of the resulting deployment variant, and (iii) uses Explainable AI techniques to provide explanations for the prediction outcomes of valid deployment variants in terms of the deployment options. We demonstrate the practicality and feasibility of FOCloud by applying it to an extension of the RuBiS benchmark application deployed on Google Cloud.

Cloud Function Performance: a component modeling approach

Cloud Functions are a trend in cloud computing in which developers are allowed to install code in a Function-as-a-Service (FaaS) platform able to manage provisioning, execution, monitoring and automatic scaling. The underlying infrastructure in FaaS platforms is hidden from the developers and designers and, since the inuence of the infrastructure is unknown, this makes it di_cult to apply software performance engineering approaches on cloud functions, which could lead to wrong or inaccurate performance estimations. In this study, we explore the use of component-based modeling and simulation in order to generate performance estimations of an exemplar cloud function which was exercised using a variety of workloads. A cloud function was both implemented and instrumented to record performance datain a log _le, associated with its invocations; using the log _le as an input, we extracted a performance model in a Palladio Component Model format suitable for running simulations to validate whether the generated model could explain the runtime behavior of the function. Using this approach and further tunings in the model, we were able to validate that the simulations could explain more than 95% of the function's behavior and that component-based modeling and simulation can be considered a serious option when trying to explain the behavior of a cloud function.

An Appraisal of Web Applications vs. Web Services with respect to Performance Engineering using Software Performance Engineering Approach

An Appraisal of Web Applications vs. Web Services with respect to Performance Engineering using Software Performance Engineering Approach

Dynamic Replication Algorithm for Data Replication to Improve System Availability: A Performance Engineering Approach

ABSTRACTInformation technology systems deployed by enterprises should not only fulfil their business functionalities but also cater to Quality of Service concerns such as Availability, Scalability, and Performance. To enhance the system performance, the system availability is an important factor and to improve the system availability, one of the strategies is replicating the frequently accessed data to multiple suitable locations which is a practical choice as the users can access the data from a nearby site. This is, however, not the case for replicas which must have a preset number of copies on several locations. How to decide a sensible number and right location for replicas have become an important issue in cloud computing. In this paper, we show a dynamic data replication strategy to enhance the performance of software system. To identify the suitable file to replicate and to decide respective number of replicas, we calculate popularity degree and replica factor. We use the fuzzy logic system to identify the system to place the replicas and we use the round robin method to place the replicas in the identified systems. We compare the performance of our technique with the existing technique.

Cultural Differences and Improving Performance: How Values and Beliefs Influence Organizational Performance

One of the most significant and yet largely overlooked factors influencing performance and workplace problem solving in many large organizations is that of national culture. Managers, and the organizations for which they work, need to be able to understand the influence of cultural values and beliefs on performance in order to identify appropriate solutions; strategies appropriate in one part of the world may be ineffective or even counter-productive in another. Bryan Hopkins' ground breaking book relates the concept of cultural dimensions, as developed by writers such as Hofstede and Trompenaars, to the performance engineering approaches of Gilbert and Mager and Pipe, to show how strategies for solving workplace performance problems need to consider the cultural composition of the workforce. It then provides a practical structure for problem solving within the context of an international, multi-cultural environment. This is a book for both managers working in an international setting or for those in national organizations who are dealing with the challenge of culturally diverse workforces. It's also a book for governments seeking to understand the potential implications of national culture on civilian or even military interventions.

Framework for hybrid performance prediction process model

The dynamic behavior of distributed systems requires that their performance characteristics be determined rigorously, preferably in the early stages of software engineering process. Evaluation of the performance at the end of software development leads to increase in the cost of design change. To compare design alternatives or to identify system bottlenecks, quantitative system analysis must be carried out from the early stages of the software development life cycle. In this paper we describe a process model, Hybrid Performance Prediction Process Model that allows modeling and evaluating distributed systems with the explicit goal of assessing performance of the software system during feasibility study. The use case performance engineering approach proposed in this paper exploits use case model and provides flexibility to integrate the software performance prediction process with software engineering process. We use an e-parking application to demonstrate various elements in our framework. The performance metrics are obtained and analyzed by considering two software architectures. Sensitivity analysis on the behavior of resources is carried out. This analysis helps to determine the capacity of the execution environment to obtain the defined performance objectives.

Nanotechnology and orthopedics: a personal perspective

Bone is a nanocomposite material comprised of hierarchically arranged collagen fibrils, hydroxyapatite and proteoglycans in the nanometer scale. Cells are accustomed to interact with nanostructures, thus providing the cells with a natural bone-like environment that potentially enhance bone tissue regeneration/repair. In this direction, nanotechnology provides opportunities to fabricate as well as explore novel properties and phenomena of functional materials, devices, and systems at the nanometer-length scale. Recent studies have provided significant insights into the influence of topographical features in regulating cell behavior. Topographical features provide essential chemical and physical cues that cells can recognize and elicit desired cellular functions including preferential adhesion, migration, proliferation, and expression of specific cell phenotype to bring desired effects. The current article will address some of the nanotechnology implications in addressing issues related to orthopedic implants performance and tissue engineering approach to bone repair/regeneration.

Migrating to Web services: a performance engineering approach

AbstractIn this paper we look at several performance pitfalls that Web services are facing today and at the performance penalties that have to be paid when exposing a legacy application as a Web service. We investigate two performance metrics of Web services, latency and scalability, and compare them with those of legacy middleware. The goal of the paper is to show how the performance penalties can be mitigated by following the principles and methods of performance engineering. Performance models can help the migration decisions, especially when new architectures or new deployment topologies are sought. The paper shows the mechanisms of building and solving a performance model involving Web services. An example is presented throughout the paper. Copyright © 2004 John Wiley &amp; Sons, Ltd.

Performance Modelling — What, Why, When and How

Management of performance risk is achieved by using performance engineering techniques. A major weapon in the performance engineering armoury is performance modelling. Modelling means prediction — estimating the performance of a new system, estimating the impact of change on an existing system or estimating the impact of a change of workload on an existing system. This paper describes how IT systems performance modelling fits into the overall performance engineering approach and its role in performance prediction and risk management. It then focuses on a recent case study illustrating why a particular modelling technique was used, how it was used, and the outcome of the modelling exercise.