Specification and simulation of queuing network models using Domain-Specific Languages

Queuing Network Models (QNMs) provide powerful notations and tools for modeling and analyzing the performance of many different kinds of systems. Although several powerful tools currently exist for solving QNMs, some of these tools define their own model representations, have been developed in platform-specific ways, and are normally difficult to extend for coping with new system properties, probability distributions or system behaviors. This paper shows how Domain Specific Languages (DSLs), when used in conjunction with Model-driven engineering techniques, provide a high-level and very flexible approach for the specification and analysis of QNMs. We build on top of an existing metamodel for QNMs (PMIF) to de ne a DSL and its associated tools (editor and simulation engine), able to provide a high-level notation for the specification of different kinds of QNMs, and easy to extend for dealing with other probability distributions or system properties, such as system reliability.Ministerio de Ciencia e Innovación TIN2011-2379

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

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

A Proactive Approach to Application Performance Analysis, Forecast and Fine-Tuning

A major challenge currently faced by the IT industry is the cost, time and resource associated with repetitive performance testing when existing applications undergo evolution. IT organizations are under pressure to reduce the cost of testing, especially given its high percentage of the overall costs of application portfolio management. Previously, to analyse application performance, researchers have proposed techniques requiring complex performance models, non-standard modelling formalisms, use of process algebras or complex mathematical analysis. In Continuous Performance Management (CPM), automated load testing is invoked during the Continuous Integration (CI) process after a build. CPM is reactive and raises alarms when performance metrics are violated. The CI process is repeated until performance is acceptable. Previous and current work is yet to address the need of an approach to allow software developers proactively target a specified performance level while modifying existing applications instead of reacting to the performance test results after code modification and build. There is thus a strong need for an approach which does not require repetitive performance testing, resource intensive application profilers, complex software performance models or additional quality assurance experts. We propose to fill this gap with an innovative relational model associating the operation‟s Performance with two novel concepts – the operation‟s Admittance and Load Potential. To address changes to a single type or multiple types of processing activities of an application operation, we present two bi-directional methods, both of which in turn use the relational model. From annotations of Delay Points within the code, the methods allow software developers to either fine-tune the operation‟s algorithm “targeting” a specified performance level in a bottom-up way or to predict the operation‟s performance due to code changes in a top-down way under a given workload. The methods do not need complex performance models or expensive performance testing of the whole application. We validate our model on a realistic experimentation framework. Our results indicate that it is possible to characterize an application Performance as a function of its Admittance and Load Potential and that the application Admittance can be characterized as a function of the latency of its Delay Points. Applying this method to complex large-scale systems has the potential to significantly reduce the cost of performance testing during system maintenance and evolution

Quantifying and Predicting the Influence of Execution Platform on Software Component Performance

The performance of software components depends on several factors, including the execution platform on which the software components run. To simplify cross-platform performance prediction in relocation and sizing scenarios, a novel approach is introduced in this thesis which separates the application performance profile from the platform performance profile. The approach is evaluated using transparent instrumentation of Java applications and with automated benchmarks for Java Virtual Machines

Quantifying and Predicting the Influence of Execution Platform on Software Component Performance

The performance of software components depends on several factors, including the execution platform on which the software components run. To simplify cross-platform performance prediction in relocation and sizing scenarios, a novel approach is introduced in this thesis which separates the application performance profile from the platform performance profile. The approach is evaluated using transparent instrumentation of Java applications and with automated benchmarks for Java Virtual Machines

Model-Driven Online Capacity Management for Component-Based Software Systems

Capacity management is a core activity when designing and operating distributed software systems. It comprises the provisioning of data center resources and the deployment of software components to these resources. The goal is to continuously provide adequate capacity, i.e., service level agreements should be satisfied while keeping investment and operating costs reasonably low. Traditional capacity management strategies are rather static and pessimistic: resources are provisioned for anticipated peak workload levels. Particularly, enterprise application systems are exposed to highly varying workloads, leading to unnecessarily high total cost of ownership due to poor resource usage efficiency caused by the aforementioned static capacity management approach. During the past years, technologies emerged that enable dynamic data center infrastructures, e. g., leveraged by cloud computing products. These technologies build the foundation for elastic online capacity management, i.e., adapting the provided capacity to workload demands based on a short-term horizon. Because manual online capacity management is not an option, automatic control approaches have been proposed. However, most of these approaches focus on coarse-grained adaptation actions and adaptation decisions are based on aggregated system-level measures. Architectural information about the controlled software system is rarely considered. This thesis introduces a model-driven online capacity management approach for distributed component-based software systems, called SLAstic. The core contributions of this approach are a) modeling languages to capture relevant architectural information about a controlled software system, b) an architecture-based online capacity management framework based on the common MAPE-K control loop architecture, c) model-driven techniques supporting the automation of the approach, d) architectural runtime reconfiguration operations for controlling a system’s capacity, e) as well as an integration of the Palladio Component Model. A qualitative and quantitative evaluation of the approach is performed by case studies, lab experiments, and simulation

Lean six sigma to reduce pharmacy medication errors in Thai hospitals : an action research study

Hospital medication errors are costly and contribute to patient mortality, morbidity and decreased health care quality. Although healthcare organizations have endeavoured to reduce medication errors by using several approaches, the errors remained, returned or could not be resolved. The use of CI methodologies, such as Lean Six Sigma, can enable healthcare practitioners to ascertain the problems in the medication process and identify and eliminate the root cause of such problems. However, the existing literature does not address the need for an LSS roadmap in reducing medication errors; therefore, healthcare practitioners do not have an LSS roadmap to follow to reduce medication errors. This study aims to develop an LSS implementation and sustainability roadmap that can guide healthcare practitioners in the implementation of LSS to reduce medication errors. A systematic review was conducted to understand the benefits, challenges, and success factors of LSS implementation in reducing medication errors in a global context. The action research methodology was used to illustrate the employment of Lean Six Sigma through collaboration between the researcher and participants in an inpatient pharmacy of two public hospitals in Thailand. This study was carried out through action research based on the following key phases: identification of problems, reflection, planning action, taking action, evaluation, reflection and specify lessons learnt. The key finding of the systematic literature review revealed that Lean Six Sigma can be very useful on reducing medication errros in a hospital setting and improving patient care. The action research findings clearly show that Lean Six Sigma application improved the inpatient pharmacy dispensing process and contributed to reduced dispensing errors and enhanced patient safety. This is the first study that has developed an LSS roadmap which healthcare practitioners can follow to reduce medication errors using, LSS methodology and, and to sustain LSS in their organizations. This study provides a greater awareness for senior managers and medical directors in hospitals about the role of LSS and its associated tools and techniques in tackling medication errors. Future research can apply the roadmap in other hospitals to ensure its practical validity and enhance the application of LSS in the healthcare setting