A Review on Software Performance Analysis for Early Detection of Latent Faults in Design Models

Organizations and society could face major breakdown if IT strategies do not comply with performance requirements. This is more so in the era of globalization and emergence of technologies caused more issues. Software design models might have latent and potential issues that affect performance of software. Often performance is the neglected area in the industry. Identifying performance issues in the design phase can save time, money and effort. Software engineers need to know the performance requirements so as to ensure quality software to be developed. Software performance engineering a quantitative approach for building software systems that can meet performance requirements. There are many design models based on UML, Petri Nets and Product-Forms. These models can be used to derive performance models that make use of LQN, MSC, QNM and so on. The design models are to be mapped to performance models in order to predict performance of system early and render valuable feedback for improving quality of the system. Due to emerging distributed technologies such as EJB, CORBA, DCOM and SOA applications became very complex with collaboration with other software. The component based software systems, software systems that are embedded, distributed likely need more systematic performance models that can leverage the quality of such systems. Towards this end many techniques came into existence. This paper throws light into software performance analysis and its present state-of-the-art. It reviews different design models and performance models that provide valuable insights to make well informed decisions

Taguchi approach for performance evaluation of service-oriented software systems.

Service-oriented software systems are becoming increasingly common in the world today as big companies such as Microsoft and IBM advocate approaches focusing on assembly of system from distributed services. Although performance of such systems is a big problem, there is surprisingly an obvious lack of attention for evaluating the performance of enterprise-scale, service-oriented software systems. This thesis investigates the application of statistical tools in performance engineering domain for total quality management. In particular, the Taguchi approach is used as an efficient and systematic way to optimize designs for performance, quality, and cost. The aim is to improve the performance of software systems and to reduce application development cost by assembling services from known vendors or intranet services. The focus of this thesis is on the response time of service-oriented systems. Nevertheless, the developed methodology also applies to other performance issues, such as memory management and caching. The interaction problems of those issues are preserved for future work.Dept. of Computer Science. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2004 .L585. Source: Masters Abstracts International, Volume: 43-01, page: 0240. Adviser: Xiaobu Yuan. Thesis (M.Sc.)--University of Windsor (Canada), 2004

Evaluation of software architecture using fuzzy colored Petri nets

Software Architecture (SA) is one of the most important artifacts for life cycle of a software system because it incorporates some important decisions and principles for the system development. On the other hand, developing the systems based on uncertain and ambiguous requirement has been increased, significantly. Therefore, there have been significant attentions on SA requirements. In this paper, we present a new method for evaluation of performance characteristics based on a use case, response time, and queue length of SA. Since there are some ambiguities associated with considered systems, we use the idea of Fuzzy UML (F-UML) diagrams. In addition, these diagrams have been enriched with performance annotations using proposed Fuzzy-SPT sub profile, the extended version of SPT profile proposed by OMG. Then, these diagrams are mapped into an executable model based on Fuzzy Colored Petri Nets (FCPN) and finally the performance metrics are calculated using the proposed algorithms. We have implemented CPN-Tools for creating and evaluating the FCPN model

Learning Queuing Networks by Recurrent Neural Networks

It is well known that building analytical performance models in practice is difficult because it requires a considerable degree of proficiency in the underlying mathematics. In this paper, we propose a machine-learning approach to derive performance models from data. We focus on queuing networks, and crucially exploit a deterministic approximation of their average dynamics in terms of a compact system of ordinary differential equations. We encode these equations into a recurrent neural network whose weights can be directly related to model parameters. This allows for an interpretable structure of the neural network, which can be trained from system measurements to yield a white-box parameterized model that can be used for prediction purposes such as what-if analyses and capacity planning. Using synthetic models as well as a real case study of a load-balancing system, we show the effectiveness of our technique in yielding models with high predictive power

Software Performance Engineering for Cloud Applications – A Survey

Cloud computing enables application service providers to lease their computing capabilities for deploying applications depending on user QoS (Quality of Service) requirements.Cloud applications have different composition, configuration and deployment requirements.Quantifying the performance of applications in Cloud computing environments is a challenging task. Software performance engineering(SPE) techniques enable us to assess performance requirements of software applications at the early stages of development. This assessment helps the developers to fine tune their design needs so that the targeted performance goals can be met. In this paper, we try to analyseperformance related issues of cloud applications and identify any SPE techniques currently available for cloud applications

Scaling Size and Parameter Spaces in Variability-Aware Software Performance Models (T)

In software performance engineering, what-if scenarios, architecture optimization, capacity planning, run-time adaptation, and uncertainty management of realistic models typically require the evaluation of many instances. Effective analysis is however hindered by two orthogonal sources of complexity. The first is the infamous problem of state space explosion — the analysis of a single model becomes intractable with its size. The second is due to massive parameter spaces to be explored, but such that computations cannot be reused across model instances. In this paper, we efficiently analyze many queuing models with the distinctive feature of more accurately capturing variability and uncertainty of execution rates by incorporating general (i.e., non-exponential) distributions. Applying product-line engineering methods, we consider a family of models generated by a core that evolves into concrete instances by applying simple delta operations affecting both the topology and the model's parameters. State explosion is tackled by turning to a scalable approximation based on ordinary differential equations. The entire model space is analyzed in a family-based fashion, i.e., at once using an efficient symbolic solution of a super-model that subsumes every concrete instance. Extensive numerical tests show that this is orders of magnitude faster than a naive instance-by-instance analysis

Performance requirements verification during software systems development

Requirements verification refers to the assurance that the implemented system reflects the specified requirements. Requirement verification is a process that continues through the life cycle of the software system. When the software crisis hit in 1960, a great deal of attention was placed on the verification of functional requirements, which were considered to be of crucial importance. Over the last decade, researchers have addressed the importance of integrating non-functional requirement in the verification process. An important non-functional requirement for software is performance. Performance requirement verification is known as Software Performance Evaluation. This thesis will look at performance evaluation of software systems. The performance evaluation of software systems is a hugely valuable task, especially in the early stages of a software project development. Many methods for integrating performance analysis into the software development process have been proposed. These methodologies work by utilising the software architectural models known in the software engineering field by transforming these into performance models, which can be analysed to gain the expected performance characteristics of the projected system. This thesis aims to bridge the knowledge gap between performance and software engineering domains by introducing semi-automated transformation methodologies. These are designed to be generic in order for them to be integrated into any software engineering development process. The goal of these methodologies is to provide performance related design guidance during the system development. This thesis introduces two model transformation methodologies. These are the improved state marking methodology and the UML-EQN methodology. It will also introduce the UML-JMT tool which was built to realise the UML-EQN methodology. With the help of automatic design models to performance model algorithms introduced in the UML-EQN methodology, a software engineer with basic knowledge of performance modelling paradigm can conduct a performance study on a software system design. This was proved in a qualitative study where the methodology and the tool deploying this methodology were tested by software engineers with varying levels of background, experience and from different sectors of the software development industry. The study results showed an acceptance for this methodology and the UML-JMT tool. As performance verification is a part of any software engineering methodology, we have to define frame works that would deploy performance requirements validation in the context of software engineering. Agile development paradigm was the result of changes in the overall environment of the IT and business worlds. These techniques are based on iterative development, where requirements, designs and developed programmes evolve continually. At present, the majority of literature discussing the role of requirements engineering in agile development processes seems to indicate that non-functional requirements verification is an unchartered territory. CPASA (Continuous Performance Assessment of Software Architecture) was designed to work in software projects where the performance can be affected by changes in the requirements and matches the main practices of agile modelling and development. The UML-JMT tool was designed to deploy the CPASA Performance evaluation tests

Architectural patterns for Parallel Programming: models for performance estimation.

Parallel Programming relies on the coordination of computing resources, so that they simultaneously work towards a common objective. Achieving this requires extra effort from the software designer, because of the increased complexity involved. Furthermore, as Parallel Programming is considered a means to improve performance, the software designer has to consider sophisticated and cost-effective practices and techniques for performance measurement and analysis. In particular, it is of great interest to obtain performance information during design stages and before implementation, since this enables the software developer to select the organisation of computations and communications between components. The Architectural Performance Modelling Method is presented as a criteria for selecting the organisation of a parallel program based on estimating its probable per formance. By considering a parallel program as an instance of a software architecture, it can be described in terms of interacting software components. Such components can be classified depending on their particular objective and their rate of change, for example, as components associated with the hardware and software environment (or Platform), components representing the fundamental structural organisation for execution and communication (or Coordination), and so on. The performance of a parallel program can be estimated as the result of the contribution of each one of those kinds of components. An Architectural Performance Model is based on selecting from the Architectural Patterns for Parallel Programming (descriptions of coodinations commonly used in Parallel Programming), a component simulator (representing a simulation of a processing component's behaviour), and a performance analysis of parallel applications (in which the information on system performance is examined). Parallel programs simulated using the Architectural Performance Modelling Method range from a complete parallel pro gram to a partially implemented program design. The simulation of parallel systems, using the information about the problem to be solved, the available resources, and architectural patterns describing overall coordinations of the parallel programs, makes it possible to identify the best performing architectural solution for the system being built