Model-Centric, Context-Aware Software Adaptation

Software must be constantly adapted to changing requirements. The time scale, abstraction level and granularity of adaptations may vary from short-term, fine-grained adaptation to long-term, coarse-grained evolution. Fine-grained, dynamic and context-dependent adaptations can be particularly difficult to realize in long-lived, large-scale software systems. We argue that, in order to effectively and efficiently deploy such changes, adaptive applications must be built on an infrastructure that is not just model-driven, but is both model-centric and context-aware. Specifically, this means that high-level, causally-connected models of the application and the software infrastructure itself should be available at run-time, and that changes may need to be scoped to the run-time execution context. We first review the dimensions of software adaptation and evolution, and then we show how model-centric design can address the adaptation needs of a variety of applications that span these dimensions. We demonstrate through concrete examples how model-centric and context-aware designs work at the level of application interface, programming language and runtime. We then propose a research agenda for a model-centric development environment that supports dynamic software adaptation and evolution.

Differential diagnosis of CT focal liver lesions using texture features, feature selection and ensemble driven classifiers

The aim of the present study is to define an optimally performing computer-aided diagnosis (CAD) architecture for the classification of liver tissue from non-enhanced computed tomography (CT) images into normal liver (C1), hepatic cyst (C2), hemangioma (C3), and hepatocellular carcinoma (C4). To this end, various CAD architectures, based on texture features and ensembles of classifiers (ECs), are comparatively assessed.

SLA-driven predictive orchestration for distributed cloud-based mobile services

We describe a system for performing SLA-driven management and orchestration of distributed infrastructures composed of services supporting mobile computing use cases. In particular, we focus on a Follow-Me Cloud scenario in which we consider mobile users accessing cloud-enable services. We combine a SLA-driven approach to infrastructure optimization, with forecast-based performance degradation preventive actions and pattern detection for supporting mobile cloud infrastructure management. We present our system's information model and architecture including the algorithmic support and the proposed scenarios for system evaluation.

Knowledge transfer in software-maintenance offshore outsourcing

Software-maintenance offshore outsourcing (SMOO) projects have been plagued by tedious knowledge transfer during the service transition to the vendor. Vendor engineers risk being over-strained by the high amounts of novel information, resulting in extra costs that may erode the business case behind offshoring. Although stakeholders may desire to avoid these extra costs by implementing appropriate knowledge transfer practices, little is known on how effective knowledge transfer can be designed and managed in light of the high cognitive loads in SMOO transitions. The dissertation at hand addresses this research gap by presenting and integrating four studies. The studies draw on cognitive load theory, attributional theory, and control theory and they apply qualitative, quantitative, and simulation methods to qualitative data from eight in-depth longitudinal cases. The results suggest that the choice of appropriate learning tasks may be more central to knowledge transfer than the amount of information shared with vendor engineers. Moreover, because vendor staff may not be able to and not dare to effectively self-manage learn-ing tasks during early transition, client-driven controls may be initially required and subsequently faded out. Collectively, the results call for people-based rather than codification-based knowledge management strategies in at least moderately specific and complex software environments.

Service Level Agreements-Driven Management of Distributed Applications in Cloud Computing Environments

Cloud Computing enables provisioning and distribution of highly scalable services in a reliable, on-demand and sustainable manner. However, objectives of managing enterprise distributed applications in cloud environments under Service Level Agreement (SLA) constraints lead to challenges for maintaining optimal resource control. Furthermore, conflicting objectives in management of cloud infrastructure and distributed applications might lead to violations of SLAs and inefficient use of hardware and software resources. This dissertation focusses on how SLAs can be used as an input to the cloud management system, increasing the efficiency of allocating resources, as well as that of infrastructure scaling. First, we present an extended SLA semantic model for modelling complex service-dependencies in distributed applications, and for enabling automated cloud infrastructure management operations. Second, we describe a multi-objective VM allocation algorithm for optimised resource allocation in infrastructure clouds. Third, we describe a method of discovering relations between the performance indicators of services belonging to distributed applications and then using these relations for building scaling rules that a CMS can use for automated management of VMs. Fourth, we introduce two novel VM-scaling algorithms, which optimally scale systems composed of VMs, based on given SLA performance constraints. All presented research works were implemented and tested using enterprise distributed applications.

Explaining Endogenous Control Change in Outsourced Software Projects

Given the centrality of control for achieving success in outsourced software projects, past research has identified key exogenous factors that determine the choice of controls. This view of exogenously driven control choice is based on a number of assumptions; particularly, clients and vendors are seen as separate cognitive entities that combat opportunistic threats under environmental uncertainty by one-off choices or infrequent revisions of controls. In this paper we complement this perspective by acknowledging that an outsourced software project may be characterized as a collective, evolving process faced with the challenge of coping with cognitive limitations of both client and vendor through a continuous process of learning. We argue that if viewed in this way, controls are less subject of a deliberate choice but rather are subject of endogenously driven change, i.e. controls evolve in close interaction with the evolving software project. Accordingly, we suggest a complementary model of endogenous control, where controls mediate individual and collective learning processes. Our research contributes to a better understanding of the dynamics in outsourced software projects. It also spells out methodological implications that may help improve cross-section control research.