Automated extraction of palladio component models from running enterprise Java applications

Nowadays, software systems have to fulfill increasingly strin-gent requirements for performance and scalability. To ensure that a system meets its performance requirements during op-eration, the ability to predict its performance under different configurations and workloads is essential. Most performance analysis tools currently used in industry focus on monitoring the current system state. They provide low-level monitoring data without any performance prediction capabilities. For performance prediction, performance models are normally required. However, building predictive performance models manually requires a lot of time and effort. In this paper, we present a method for automated extraction of perfor-mance models of Java EE applications, based on monitor-ing data collected during operation. We extract instances of the Palladio Component Model (PCM)- a performance meta-model targeted at component-based systems. We eval-uate the model extraction method in the context of a case study with a real-world enterprise application. Even though the extraction requires some manual intervention, the case study demonstrates that the existing gap between low-level monitoring data and high-level performance models can be closed. 1

Parameter dependencies for reusable performance specifications of software components

To avoid design-related per­for­mance problems, model-driven performance prediction methods analyse the response times, throughputs, and re­source utilizations of software architectures before and during implementation. This thesis proposes new modeling languages and according model transformations, which allow a reusable description of usage profile dependencies to the performance of software components. Predictions based on this new methods can support performance-related design decisions

Architecture-Level Software Performance Models for Online Performance Prediction

Proactive performance and resource management of modern IT infrastructures requires the ability to predict at run-time, how the performance of running services would be affected if the workload or the system changes. In this thesis, modeling and prediction facilities that enable online performance prediction during system operation are presented. Analyses about the impact of reconfigurations and workload trends can be conducted on the model level, without executing expensive performance tests

On Design and Realization of New Generation Misson-critial Application Systems

Mission-critical system typically refers to a project or system for which the success is vital to the mission of the underlying organization. The failure or delayed completion of the tasks in mission-critical systems may cause severe financial loss, even human casualties. For example, failure of an accurate and timely forecast of Hurricane Rita in September 2005 caused enormous financial loss and several deaths. As such, real-time guarantee and reliability have always been two key foci of mission-critical system design. Many factors affect real-time guarantee and reliability. From the software design perspective, which is the focus of this paper, three aspects are most important. The first of these is how to design a single application to effectively support real-time requirement and improve reliability, the second is how to integrate different applications in a cluster environment to guarantee real-time requirement and improve reliability, and the third is how to effectively coordinate distributed applications to support real-time requirements and improve reliability. Following these three aspects, this dissertation proposes and implements three novel methodologies: real-time component based single node application development, real-time workflow-based cluster application integration, and real-time distributed admission control. For ease of understanding, we introduce these three methodologies and implementations in three real-world mission-critical application systems: single node mission-critical system, cluster environment mission-critical system, and wide-area network mission-critical system. We study full-scale design and implementation of these mission-critical systems, more specifically: 1) For the single node system, we introduce a real-time component based application model, a novel design methodology, and based on the model and methodology, we implement a real-time component based Enterprise JavaBean (EJB) System. Through component based design, efficient resource management and scheduling, we show that our model and design methodology can effectively improve system reliability and guarantee real-time requirement. 2) For the system in a cluster environment, we introduce a new application model, a real-time workflow-based application integration methodology, and based on the model and methodology, we implement a data center management system for the Southeastern Universities Research Association (SURA) project. We show that our methodology can greatly simplify the design of such a system and make it easier to meet deadline requirements, while improving system reliability through the reuse of fully tested legacy models. 3) For the system in a wide area network, we narrow our focus to a representative VoIP system and introduce a general distributed real-time VoIP system model, a novel system design methodology, and an implementation. We show that with our new model and architectural design mechanism, we can provide effective real-time requirement for Voice over Internet Protocol (VoIP)

Automatic performance optimisation of component-based enterprise systems via redundancy

Component technologies, such as J2EE and .NET have been extensively adopted for building complex enterprise applications. These technologies help address complex functionality and flexibility problems and reduce development and maintenance costs. Nonetheless, current component technologies provide little support for predicting and controlling the emerging performance of software systems that are assembled from distinct components. Static component testing and tuning procedures provide insufficient performance guarantees for components deployed and run in diverse assemblies, under unpredictable workloads and on different platforms. Often, there is no single component implementation or deployment configuration that can yield optimal performance in all possible conditions under which a component may run. Manually optimising and adapting complex applications to changes in their running environment is a costly and error-prone management task. The thesis presents a solution for automatically optimising the performance of component-based enterprise systems. The proposed approach is based on the alternate usage of multiple component variants with equivalent functional characteristics, each one optimized for a different execution environment. A management framework automatically administers the available redundant variants and adapts the system to external changes. The framework uses runtime monitoring data to detect performance anomalies and significant variations in the application's execution environment. It automatically adapts the application so as to use the optimal component configuration under the current running conditions. An automatic clustering mechanism analyses monitoring data and infers information on the components' performance characteristics. System administrators use decision policies to state high-level performance goals and configure system management processes. A framework prototype has been implemented and tested for automatically managing a J2EE application. Obtained results prove the framework's capability to successfully manage a software system without human intervention. The management overhead induced during normal system execution and through management operations indicate the framework's feasibility

QoS control of E-business systems through performance modelling and estimation

E-business systems provide the infrastructure whereby parties interact electronically via business transactions. At peak loads, these systems are susceptible to large volumes of transactions and concurrent users and yet they are expected to maintain adequate performance levels. Over provisioning is an expensive solution. A good alternative is the adaptation of the system, managing and controlling its resources. We address these concerns by presenting a model that allows fast evaluation of performance metrics in terms of measurable or controllable parameters. The model can be used in order to (a) predict the performance of a system under given or assumed loading conditions and (b) to choose the optimal configuration set-up for certain controllable parameters with respect to specified performance measures. Firstly, we analyze the characteristics of E-business systems. This analysis leads to the analytical model, which is sufficiently general to capture the behaviour of a large class of commonly encountered architectures. We propose an approximate solution which is numerically efficient and fast. By mean of simulation, we prove that its accuracy is acceptable over a wide range of system configurations and different load levels. We further evaluate the approximate solution by comparing it to a real-life E-business system. A J2EE application of non-trivial size and complexity is deployed on a 2-tier system composed of the JBoss application server and a database server. We implement an infrastructure fully integrated on the application server, capable of monitoring the E-business system and controlling its configuration parameters. Finally, we use this infrastructure to quantify both the static parameters of the model and the observed performance. The latter are then compared with the metrics predicted by the model, showing that the approximate solution is almost exact in predicting performance and that it assesses the optimal system configuration very accurately.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Predicting software performance in symmetric multi-core and multiprocessor Environments

With today\u27s rise of multi-core processors, concurrency becomes a ubiquitous challenge in software development.Performance prediction methods have to reflect the influence of multiprocessing environments on software performance in order to help software architects to find potential performance problems during early development phases. In this thesis, we address the influence of the operating system scheduler on software performance in symmetric multiprocessing environments

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

A Quality-Driven Approach to Enable Decision-Making in Self-Adaptive Software

Self-adaptive software systems are increasingly in demand. The driving forces are changes in the software “self” and “context”, particularly in distributed and pervasive applications. These systems provide self-* properties in order to keep requirements satisfied in different situations. Engineering self-adaptive software normally involves building the adaptable software and the adaptation manager. This PhD thesis focuses on the latter, especially on the design and implementation of the deciding process in an adaptation manager. For this purpose, a Quality-driven Framework for Engineering an Adaptation Manager (QFeam) is proposed, in which quality requirements play a key role as adaptation goals. Two major phases of QFeam are building the runtime adaptation model and designing the adaptation mechanism. The modeling phase investigates eliciting and specifying key entities of the adaptation problem space including goals, attributes, and actions. Three composition patterns are discussed to link these entities to build the adaptation model, namely: goal-centric, attribute-action-coupling, and hybrid patterns. In the second phase, the adaptation mechanism is designed according to the adopted pattern in the model. Therefore, three categories of mechanisms are discussed, in which the novel goal-ensemble mechanism is introduced. A concrete model and mechanism, the Goal-Attribute-Action Model (GAAM), is proposed based on the goal-centric pattern and the goal-ensemble mechanism. GAAM is implemented based on the StarMX framework for Java-based systems. Several considerations are taken into account in QFeam: i) the separation of adaptation knowledge from application knowledge, ii) highlighting the role of adaptation goals, and iii) modularity and reusability. Among these, emphasizing goals is the tenet of QFeam, especially in order to address the challenge of addressing several self- * properties in the adaptation manager. Furthermore, QFeam aims at embedding a model in the adaptation manager, particularly in the goal-centric and hybrid patterns. The proposed framework focuses on mission-critical systems including enterprise and service-oriented applications. Several empirical studies were conducted to put QFeam into practice, and also evaluate GAAM in comparison with other adaptation models and mechanisms. Three case studies were selected for this purpose: the TPC-W bookstore application, a news application, and the CC2 VoIP call controller. Several research questions were set for each case study, and findings indicate that the goal-ensemble mechanism and GAAM can outperform or work as well as a common rule-based approach. The notable difference is that the effort of building an adaptation manager based on a goal-centric pattern is less than building it using an attribute-action-coupling pattern. Moreover, representing goals explicitly leads to better scalability and understandability of the adaptation manager. Overall, the experience of working on these three systems show that QFeam improves the design and development process of the adaptation manager, particularly by highlighting the role of adaptation goals