Deployment and Operation of Complex Software in Heterogeneous Execution Environments

This open access book provides an overview of the work developed within the SODALITE project, which aims at facilitating the deployment and operation of distributed software on top of heterogeneous infrastructures, including cloud, HPC and edge resources. The experts participating in the project describe how SODALITE works and how it can be exploited by end users. While multiple languages and tools are available in the literature to support DevOps teams in the automation of deployment and operation steps, still these activities require specific know-how and skills that cannot be found in average teams. The SODALITE framework tackles this problem by offering modelling and smart editing features to allow those we call Application Ops Experts to work without knowing low level details about the adopted, potentially heterogeneous, infrastructures. The framework offers also mechanisms to verify the quality of the defined models, generate the corresponding executable infrastructural code, automatically wrap application components within proper execution containers, orchestrate all activities concerned with deployment and operation of all system components, and support on-the-fly self-adaptation and refactoring

Continuous Process Auditing (CPA): an Audit Rule Ontology Approach to Compliance and Operational Audits

Continuous Auditing (CA) has been investigated over time and it is, somewhat, in practice within nancial and transactional auditing as a part of continuous assurance and monitoring. Enterprise Information Systems (EIS) that run their activities in the form of processes require continuous auditing of a process that invokes the action(s) speci ed in the policies and rules in a continuous manner and/or sometimes in real-time. This leads to the question: How much could continuous auditing mimic the actual auditing procedures performed by auditing professionals? We investigate some of these questions through Continuous Process Auditing (CPA) relying on heterogeneous activities of processes in the EIS, as well as detecting exceptions and evidence in current and historic databases to provide audit assurance

SODALITE@RT: Orchestrating Applications on Cloud-Edge Infrastructures

AbstractIoT-based applications need to be dynamically orchestrated on cloud-edge infrastructures for reasons such as performance, regulations, or cost. In this context, a crucial problem is facilitating the work of DevOps teams in deploying, monitoring, and managing such applications by providing necessary tools and platforms. The SODALITE@RT open-source framework aims at addressing this scenario. In this paper, we present the main features of the SODALITE@RT: modeling of cloud-edge resources and applications using open standards and infrastructural code, and automated deployment, monitoring, and management of the applications in the target infrastructures based on such models. The capabilities of the SODALITE@RT are demonstrated through a relevant case study

Knowledge-centric autonomic systems

Autonomic computing revolutionised the commonplace understanding of proactiveness in the digital world by introducing self-managing systems. Built on top of IBM’s structural and functional recommendations for implementing intelligent control, autonomic systems are meant to pursue high level goals, while adequately responding to changes in the environment, with a minimum amount of human intervention. One of the lead challenges related to implementing this type of behaviour in practical situations stems from the way autonomic systems manage their inner representation of the world. Specifically, all the components involved in the control loop have shared access to the system’s knowledge, which, for a seamless cooperation, needs to be kept consistent at all times.A possible solution lies with another popular technology of the 21st century, the Semantic Web,and the knowledge representation media it fosters, ontologies. These formal yet flexible descriptions of the problem domain are equipped with reasoners, inference tools that, among other functions, check knowledge consistency. The immediate application of reasoners in an autonomic context is to ensure that all components share and operate on a logically correct and coherent “view” of the world. At the same time, ontology change management is a difficult task to complete with semantic technologies alone, especially if little to no human supervision is available. This invites the idea of delegating change management to an autonomic manager, as the intelligent control loop it implements is engineered specifically for that purpose.Despite the inherent compatibility between autonomic computing and semantic technologies,their integration is non-trivial and insufficiently investigated in the literature. This gap represents the main motivation for this thesis. Moreover, existing attempts at provisioning autonomic architectures with semantic engines represent bespoke solutions for specific problems (load balancing in autonomic networking, deconflicting high level policies, informing the process of correlating diverse enterprise data are just a few examples). The main drawback of these efforts is that they only provide limited scope for reuse and cross-domain analysis (design guidelines, useful architectural models that would scale well across different applications and modular components that could be integrated in other systems seem to be poorly represented). This work proposes KAS (Knowledge-centric Autonomic System), a hybrid architecture combining semantic tools such as: • an ontology to capture domain knowledge,• a reasoner to maintain domain knowledge consistent as well as infer new knowledge, • a semantic querying engine,• a tool for semantic annotation analysis with a customised autonomic control loop featuring: • a novel algorithm for extracting knowledge authored by the domain expert, • “software sensors” to monitor user requests and environment changes, • a new algorithm for analysing the monitored changes, matching them against known patterns and producing plans for taking the necessary actions, • “software effectors” to implement the planned changes and modify the ontology accordingly. The purpose of KAS is to act as a blueprint for the implementation of autonomic systems harvesting semantic power to improve self-management. To this end, two KAS instances were built and deployed in two different problem domains, namely self-adaptive document rendering and autonomic decision2support for career management. The former case study is intended as a desktop application, whereas the latter is a large scale, web-based system built to capture and manage knowledge sourced by an entire (relevant) community. The two problems are representative for their own application classes –namely desktop tools required to respond in real time and, respectively, online decision support platforms expected to process large volumes of data undergoing continuous transformation – therefore, they were selected to demonstrate the cross-domain applicability (that state of the art approaches tend to lack) of the proposed architecture. Moreover, analysing KAS behaviour in these two applications enabled the distillation of design guidelines and of lessons learnt from practical implementation experience while building on and adapting state of the art tools and methodologies from both fields.KAS is described and analysed from design through to implementation. The design is evaluated using ATAM (Architecture Trade off Analysis Method) whereas the performance of the two practical realisations is measured both globally as well as deconstructed in an attempt to isolate the impact of each autonomic and semantic component. This last type of evaluation employs state of the art metrics for each of the two domains. The experimental findings show that both instances of the proposed hybrid architecture successfully meet the prescribed high-level goals and that the semantic components have a positive influence on the system’s autonomic behaviour

Business rules based legacy system evolution towards service-oriented architecture.

Enterprises can be empowered to live up to the potential of becoming dynamic, agile and real-time. Service orientation is emerging from the amalgamation of a number of key business, technology and cultural developments. Three essential trends in particular are coming together to create a new revolutionary breed of enterprise, the service-oriented enterprise (SOE): (1) the continuous performance management of the enterprise; (2) the emergence of business process management; and (3) advances in the standards-based service-oriented infrastructures. This thesis focuses on this emerging three-layered architecture that builds on a service-oriented architecture framework, with a process layer that brings technology and business together, and a corporate performance layer that continually monitors and improves the performance indicators of global enterprises provides a novel framework for the business context in which to apply the important technical idea of service orientation and moves it from being an interesting tool for engineers to a vehicle for business managers to fundamentally improve their businesses

Using ontology and semantic web services to support modeling in systems biology

This thesis addresses the problem of collaboration among experimental biologists and modelers in the study of systems biology by using ontology and Semantic Web Services techniques. Modeling in systems biology is concerned with using experimental information and mathematical methods to build quantitative models across different biological scales. This requires interoperation among various knowledge sources and services. Ontology and Semantic Web Services potentially provide an infrastructure to meet this requirement. In our study, we propose an ontology-centered framework within the Semantic Web infrastructure that aims at standardizing various areas of knowledge involved in the biological modeling processes. In this framework, first we specify an ontology-based meta-model for building biological models. This meta-model supports using shared biological ontologies to annotate biological entities in the models, allows semantic queries and automatic discoveries, enables easy model reuse and composition, and serves as a basis to embed external knowledge. We also develop means of transforming biological data sources and data analysis methods into Web Services. These Web Services can then be composed together to perform parameterization in biological modeling. The knowledge of decision-making and workflow of parameterization processes are then recorded by the semantic descriptions of these Web Services, and embedded in model instances built on our proposed meta-model. We use three cases of biological modeling to evaluate our framework. By examining our ontology-centered framework in practice, we conclude that by using ontology to represent biological models and using Semantic Web Services to standardize knowledge components in modeling processes, greater capabilities of knowledge sharing, reuse and collaboration can be achieved. We also conclude that ontology-based biological models with formal semantics are essential to standardize knowledge in compliance with the Semantic Web vision