Liam O'Brien

Quality Attributes for Service-Oriented Architectures

The SOA approach is a very popular choice today for the implementation of distributed systems. The use of SOA or more specifically the Web services technology is an important architecture decision. An architect should understand how different quality attributes for a system are impacted by that decision. While there are significant benefits with respect to interoperability and modifiability, other qualities such as performance, security and testability are concerns. This paper discusses how the different quality attributes of a system can be positively or negatively affected by the use of such technology. It describes the factors related to each attribute, as well as possible tradeoffs and existing efforts to achieve that quality. The paper also discusses open issues in service level agreements that are used to contract the level of service quality between service providers and users.

Supporting Migration to Services using Software Architecture Reconstruction

There are many good reasons why organizations should perform software architecture reconstructions. However, few organizations are willing to pay for the effort. Software architecture reconstruction must be viewed not as an effort on its own but as a contribution in a broader technical context, such as the streamlining of products into a product line or the modernization of systems that hit their architectural borders, that is require major restructuring. In this paper we propose the use of architecture reconstruction to support system modernization through the identification and reuse of legacy components as services in a service-oriented architecture (SOA). A case study showing how architecture reconstruction was used on a system to support an organizations decision-making process is presented

A Framework for Scope, Cost and Effort Estimation for Service Oriented Architecture (SOA) Projects

Determining the scope, size, cost and effort of a Service Oriented Architecture (SOA) project is important to managing the risk of cost blowout happening during the project, building the business case and securing the funding for the project. Little work has been published on examining the various aspects of SOA projects and in having a systematic approach to determine the scope, size, cost and effort for such projects. This paper examines in detail various types of SOA projects and proposes the SMAT-AUS framework for capturing and using details about various aspects of SOA projects to determine the scope and estimate cost and effort. We describe the framework in detail and show the various dimensions of the framework that will impact on the scope, cost and effort including the technical and social/cultural/ organizational aspects, as well as the maturity of the organization in terms of its experience in undertaking SOA projects. We also give details of a case study using the framework.

Business transformation to SOA: aspects of the migration and performance and QoS issues

Organizations face challenges to be more adaptable and transform to meet new customer demands with fewer resources and streamlining of its business activities. There is a growing move to introduce SOAs with their promise of cost-efficiency, agility, adaptability and legacy leverage. However there are many aspects of transforming an organization to use SOA and many obstacles and issues that the organization has to address when introducing SOAs. In this paper we outline some of the major aspects of SOA introduction and focus on some of the open issues that still need to be tackled. In the discussion on the various aspects of SOA introduction we focus in on performance and QoS which are major pieces to get right if the SOA implementation is to be successful. We outline some of the work that we are doing in this area and some problem areas where further research is needed.

Model-driven derivation of product architectures

Product Derivation is one of the central activities in Software Product Lines (SPL). One of the main challenges of the process of product derivation is dealing with complexity, which is caused by the large number of artifacts and dependencies between them. Another major challenge is maximizing development efficiency and reducing time-to-market, while at the same time producing high quality products. One approach to overcome these challenges is to automate the derivation process. To this end, this paper focuses on one particular activity of the derivation process; the derivation of the product-specific architecture and describes how this activity can be automated using a model-driven approach. The approach derives the product-specific architecture by selectively copying elements from the product-line architecture. The decision, which elements are included in the derived architecture, is based on a product-specific feature configuration. We present a prototype that implements the derivation as a model transformation described in the Atlas Transformation Language (ATL). We conclude with a short overview of related work and directions for future research

Effort-Oriented Classification Matrix of Web Service Composition

Within the service-oriented computing domain, Web service composition is an effective realization to satisfy the rapidly changing requirements of business. Therefore, the research into Web service composition has unfolded broadly. Since examining all of the related work in this area becomes a mission next to impossible, the classification of composition approaches can be used to facilitate multiple research tasks. However, the current attempts to classify Web service composition do not have clear objectives. Furthermore, the contexts and technologies of composition approaches are confused in the existing classifications. This paper proposes an effort-oriented classification matrix for Web service composition, which distinguishes between the context and technology dimension. The context dimension is aimed at analyzing the environment influence on the effort of Web service composition, while the technology dimension focuses on the technique influence on the effort. Consequently, besides the traditional classification benefits, this matrix can be used to build the basis of cost estimation for Web service composition in future research.

A factor framework for experimental design for performance evaluation of commercial cloud services

Given the diversity of commercial Cloud services, performance evaluations of candidate services would be crucial and beneficial for both service customers (e.g. cost-benefit analysis) and providers (e.g. direction of service improvement). Before an evaluation implementation, the selection of suitable factors (also called parameters or variables) plays a prerequisite role in designing evaluation experiments. However, there seems a lack of systematic approaches to factor selection for Cloud services performance evaluation. In other words, evaluators randomly and intuitively concerned experimental factors in most of the existing evaluation studies. Based on our previous taxonomy and modeling work, this paper proposes a factor framework for experimental design for performance evaluation of commercial Cloud services. This framework capsules the state-of-the-practice of performance evaluation factors that people currently take into account in the Cloud Computing domain, and in turn can help facilitate designing new experiments for evaluating Cloud services.

Performance modelling power consumption and carbon emissions for Server Virtualization of Service Oriented Architectures (SOAs)

Server Virtualization is driven by the goal of reducing the total number of physical servers in an organisation by consolidating multiple applications on shared servers. Expected benefits include more efficient server utilisation, and a decrease in green house gas emissions. However, Service Oriented Architectures combined with Server Virtualization may significantly increase risks such as saturation and Service Level Agreement (SLA) violations.

Early Observations on Performance of Google Compute Engine for Scientific Computing

Although Cloud computing emerged for business applications in industry, public Cloud services have been widely accepted and encouraged for scientific computing in academia. The recently available Google Compute Engine (GCE) is claimed to support high-performance and computationally intensive tasks, while little evaluation studies can be found to reveal GCEs scientific capabilities. Considering that fundamental performance benchmarking is the strategy of early-stage evaluation of new Cloud services, we followed the Cloud Evaluation Experiment Methodology (CEEM) to benchmark GCE and also compare it with Amazon EC2, to help understand the elementary capability of GCE for dealing with scientific problems. The experimental results and analyses show both potential advantages of, and possible threats to applying GCE to scientific computing. For example, compared to Amazons EC2 service, GCE may better suit applications that require frequent disk operations, while it may not be ready yet for single VM-based parallel computing. Following the same evaluation methodology, different evaluators can replicate and/or supplement this fundamental evaluation of GCE. Based on the fundamental evaluation results, suitable GCE environments can be further established for case studies of solving real science problems.

Boosting Metrics for Cloud Services Evaluation -- The Last Mile of Using Benchmark Suites

Benchmark suites are significant for evaluating various aspects of Cloud services from a holistic view. However, there is still a gap between using benchmark suites and achieving holistic impression of the evaluated Cloud services. Most Cloud service evaluation work intended to report individual benchmarking results without delivering summary measures. As a result, it could be still hard for customers with such evaluation reports to understand an evaluated Cloud service from a global perspective. Inspired by the boosting approaches to machine learning, we proposed the concept Boosting Metrics to represent all the potential approaches that are able to integrate a suite of benchmarking results. This paper introduces two types of preliminary boosting metrics, and demonstrates how the boosting metrics can be used to supplement primary measures of individual Cloud service features. In particular, boosting metrics can play a summary Response role in applying experimental design to Cloud services evaluation. Although the concept Boosting Metrics was refined based on our work in the Cloud Computing domain, we believe it can be easily adapted to the evaluation work of other computing paradigms.