Measuring Process Modelling Success

Process-modelling has seen widespread acceptance, par ticularly on large IT-enabled Business Process Reengineering projects. It is applied, as a process design and management technique, across all life-cycle phases of a system. While there has been much research on aspects of process-modelling, little attention has focused on post-hoc evaluation of process-modelling success. This paper addresses this gap, and presents a process-modelling success measurement (PMS) framework, which includes the dimensions: process-model quality; model use; user satisfaction; and process modelling impact. Measurement items for each dimension are also suggested

Compositional Performance Modelling with the TIPPtool

Stochastic process algebras have been proposed as compositional specification formalisms for performance models. In this paper, we describe a tool which aims at realising all beneficial aspects of compositional performance modelling, the TIPPtool. It incorporates methods for compositional specification as well as solution, based on state-of-the-art techniques, and wrapped in a user-friendly graphical front end. Apart from highlighting the general benefits of the tool, we also discuss some lessons learned during development and application of the TIPPtool. A non-trivial model of a real life communication system serves as a case study to illustrate benefits and limitations

Towards automatic Markov reliability modeling of computer architectures

The analysis and evaluation of reliability measures using time-varying Markov models is required for Processor-Memory-Switch (PMS) structures that have competing processes such as standby redundancy and repair, or renewal processes such as transient or intermittent faults. The task of generating these models is tedious and prone to human error due to the large number of states and transitions involved in any reasonable system. Therefore model formulation is a major analysis bottleneck, and model verification is a major validation problem. The general unfamiliarity of computer architects with Markov modeling techniques further increases the necessity of automating the model formulation. This paper presents an overview of the Automated Reliability Modeling (ARM) program, under development at NASA Langley Research Center. ARM will accept as input a description of the PMS interconnection graph, the behavior of the PMS components, the fault-tolerant strategies, and the operational requirements. The output of ARM will be the reliability of availability Markov model formulated for direct use by evaluation programs. The advantages of such an approach are (a) utility to a large class of users, not necessarily expert in reliability analysis, and (b) a lower probability of human error in the computation

A Planning Approach to Migrating Domain-specific Legacy Systems into Service Oriented Architecture

The planning work prior to implementing an SOA migration project is very important for its success. Up to now, most of this kind of work has been manual work. An SOA migration planning approach based on intelligent information processing methods is addressed to semi-automate the manual work. This thesis will investigate the principle research question: “How can we obtain SOA migration planning schemas (semi-) automatically instead of by traditional manual work in order to determine if legacy software systems should be migrated to SOA computation environment?”. The controlled experiment research method has been adopted for directing research throughout the whole thesis. Data mining methods are used to analyse SOA migration source and migration targets. The mined information will be the supplementation of traditional analysis results. Text similarity measurement methods are used to measure the matching relationship between migration sources and migration targets. It implements the quantitative analysis of matching relationships instead of common qualitative analysis. Concretely, an association rule and sequence pattern mining algorithms are proposed to analyse legacy assets and domain logics for establishing a Service model and a Component model. These two algorithms can mine all motifs with any min-support number without assuming any ordering. It is better than the existing algorithms for establishing Service models and Component models in SOA migration situations. Two matching strategies based on keyword level and superficial semantic levels are described, which can calculate the degree of similarity between legacy components and domain services effectively. Two decision-making methods based on similarity matrix and hybrid information are investigated, which are for creating SOA migration planning schemas. Finally a simple evaluation method is depicted. Two case studies on migrating e-learning legacy systems to SOA have been explored. The results show the proposed approach is encouraging and applicable. Therefore, the SOA migration planning schemas can be created semi-automatically instead of by traditional manual work by using data mining and text similarity measurement methods

Incremental Calibration of Architectural Performance Models with Parametric Dependencies

Architecture-based Performance Prediction (AbPP) allows evaluation of the performance of systems and to answer what-if questions without measurements for all alternatives. A difficulty when creating models is that Performance Model Parameters (PMPs, such as resource demands, loop iteration numbers and branch probabilities) depend on various influencing factors like input data, used hardware and the applied workload. To enable a broad range of what-if questions, Performance Models (PMs) need to have predictive power beyond what has been measured to calibrate the models. Thus, PMPs need to be parametrized over the influencing factors that may vary. Existing approaches allow for the estimation of parametrized PMPs by measuring the complete system. Thus, they are too costly to be applied frequently, up to after each code change. They do not keep also manual changes to the model when recalibrating. In this work, we present the Continuous Integration of Performance Models (CIPM), which incrementally extracts and calibrates the performance model, including parametric dependencies. CIPM responds to source code changes by updating the PM and adaptively instrumenting the changed parts. To allow AbPP, CIPM estimates the parametrized PMPs using the measurements (generated by performance tests or executing the system in production) and statistical analysis, e.g., regression analysis and decision trees. Additionally, our approach responds to production changes (e.g., load or deployment changes) and calibrates the usage and deployment parts of PMs accordingly. For the evaluation, we used two case studies. Evaluation results show that we were able to calibrate the PM incrementally and accurately.Comment: Manar Mazkatli is supported by the German Academic Exchange Service (DAAD

Hierarchical architecture design and simulation environment

The Hierarchical Architectural design and Simulation Environment (HASE)is intended as a flexible tool for computer architects who wish to experiment with alternative architectural configurations and design parameters. HASE is both a design environment and a simulator. Architecture components are described by a hierarchical library of objects defined in terms of an object oriented simulation language. HASE instantiates these objects to simulate and animate the execution of a computer architecture. An event trace generated by the simulator therefore describes the interaction between architecture components, for example, fetch stages, address and data buses, sequencers, instruction buffers and register files. The objects can model physical components at different abstraction levels, eg. PMS (processor memory switch), ISP (instruction set processor) and RTL (register transfer level). HASE applies the concepts of inheritance, encapsulation and polymorphism associated with object orientation, to simplify the design and implementation of an architecture simulation that models component operations at different abstraction levels. For example, HASE can probe the performance of a processor's floating point unit, executing a multiplication operation, at a lower level of abstraction, i.e. the RTL, whilst simulating remaining architecture components at a PMS level of abstraction. By adopting this approach, HASE returns a more meaningful and relevant event trace from an architecture simulation. Furthermore, an animator visualises the simulation's event trace to clarify the collaborations and interactions between architecture components. The prototype version of HASE is based on GSS (Graphical Support System), and DEMOS (Discrete Event Modelling On Simula)