Advances in Service-Oriented and Cloud Computing

We propose a performance engineering technique for self-adaptive container cluster management, often used in cloud environments now. We focus here on an abstract model that can be used by simulation tools to identify an optimal configuration for such a system, capable of providing reliable performance to service consumers. The aim of the model-based tool is to identify and analyse a set of rules capable of balancing resource demands for this platform. We present an executable model for a simulation environment that allows container cluster architectures to be studied. We have selected the Kubernetes cluster management platform as the target. Our models reflect the current Kubernetes platform, but we also introduce an advanced controller model going beyond current Kubernetes capabilities. We use the Palladio Eclipse plugin as the simulation environment. The outcome is a working simulator, that applied to a concrete container-based cluster architecture could be used by developers to understand and configure self-adaptive system behavior.

After introducing Docker containers in a nutshell, we discuss the benefits that can be obtained by supporting enhanced descriptions of multi-container Docker applications. We illustrate how such applications can be naturally modelled in TOSCA, and how this permits automating their management and reducing the time and cost needed to develop such applications (e.g., by facilitating the reuse of existing solutions, and by permitting to analyse and validate applications at design-time).

For various motives such as routing around scheduled downtimes or escaping price surges, operations engineers of cloud applications are occasionally conducting zero-downtime live migrations. For monolithic virtual machine-based applications, this process has been studied extensively. In contrast, for composite microservice applications new challenges arise due to the need for a transactional migration of all constituent microservice implementations such as platform-specific light-weight containers and volumes. This paper outlines the challenges in the general heterogeneous case and solves them partially for a specialised inhomogeneous case based on the OpenShift and Kubernetes application models. Specifically, the paper describes our contributions in terms of tangible application models, tool designs, and migration evaluation. From the results, we reason about possible solutions for the general heterogeneous case.

Assessing security of application deployments in the Fog is a non-trivial task, having to deal with highly heterogeneous infrastructures containing many resource-constrained devices. In this paper, we introduce: (i) a declarative way of specifying security capabilities of Fog infrastructures and security requirements of Fog applications, and (ii) a (probabilistic) reasoning strategy to determine application deployments and to quantitatively assess their security level, considering the trust degree of application operators in different Cloud/Fog providers. A lifelike example is used to showcase a first proof-of-concept implementation and to illustrate how it can be used in synergy with other predictive tools to optimise the deployment of Fog applications.

Rapid growth of various types of cloud services is creating new opportunities for innovative enterprise applications. As a result, enterprise applications are increasingly reliant on externally provided cloud services. It can be argued that traditional systems development methods and tools are not adequate in the context of cloud services and that new methods and frameworks that support these methods are needed for management of lifecycle of cloud services. In this paper, we describe the implementation of a Service Consumer Framework (SCF) – a framework for the management of design-time and runtime activities throughout the lifecycle of enterprise applications that use externally provided cloud services. The SCF framework has been evaluated during the implementation of a large-scale project and is being continuously improved to incorporate additional types of cloud services.

Deadlock-analysis of concurrent service-oriented systems is often done by P/T-net-based approaches. We show that there is a concurrent service-oriented system with synchronous (stack behavior) and asynchronous procedure (concurrent behavior) calls with a deadlock that is not discovered by classical P/T-net-based approaches. Hence, P/T-net-based approaches lead to false statements on absence of deadlocks. We propose an approach based on Mayr’s Process Rewrite Systems to model both, concurrent and stack behavior while the deadlock problem remains decidable.

This paper proposes a decentralized billing and subcontracting system for regional cloud service providers. Based on blockchain technology, this system allows, on the one hand side, to collectively offer services in a distributed environment in a strict or ad-hoc federation and, on the other, to bill each user of such a services individually without a respective central service. In order to do so, it uses a blockchain-based transaction process which uses specialized tokens in order to enable a fair and secure distribution of requested cloud services. It maintains the ability to achieve consensus by validating the respective blockchain (part). In result, the proposed system is not bound to a specific technology, but rather open to any blockchain that allows arbitrary data or modeling of custom transactions.

As APIs proliferate, managing the constantly growing and evolving API landscapes inside and across organizations becomes a challenge. Part of the management challenge is for APIs to be able to describe themselves, so that users and tooling can use descriptions for finding and filtering APIs. A standardized labeling scheme can help to cover some of the cases where API self-description allows API landscapes to become more usable and scalable. In this paper we present the vision for standardized API labels, which summarize and represent critical aspects of APIs. These aspect allow consumers to more easily become aware of the kind of dependency they are going to establish with the service provider when choosing to use them. API labels not only summarize critical coupling factors, but also can include claims that require to be validated by trusted third parties.

While there are several theoretical comparisons of Object Orientation (OO) and Service Orientation (SO), little empirical research on the maintainability of the two paradigms exists. To provide support for a generalizable comparison, we conducted a study with four related parts. Two functionally equivalent systems (one OO and one SO version) were analyzed with coupling and cohesion metrics as well as via a controlled experiment, where participants had to extend the systems. We also conducted a survey with 32 software professionals and interviewed 8 industry experts on the topic. Results indicate that the SO version of our system possesses a higher degree of cohesion, a lower degree of coupling, and could be extended faster. Survey and interview results suggest that industry sees systems built with SO as more loosely coupled, modifiable, and reusable. OO systems, however, were described as less complex and easier to test.

To enable software professionals to design and evolve long-living Service-Based Systems (SBSs) in sustainable fashion, we are developing a continuous assurance method to identify and remediate potential evolvability-related issues. With the rational of broad applicability within service-based architectural styles, we focus on the commonalities of Service-Oriented Architecture (SOA) and Microservices. The method is based on structural service-oriented metrics (e.g. coupling or cohesion), service evolution scenarios, as well as service-oriented design patterns to increase modifiability. Tool support should enable convenient usage and adoption of the method for practitioners. The final evaluation is planned as an industry case study in combination with action research.

Deploying and managing multi-component IoT applications in Fog computing scenarios is challenging due to the heterogeneity, scale and dynamicity of Fog infrastructures, as well as due to the complexity of modern software systems. When deciding where/how to (re-)allocate application components over the continuum from the IoT to the Cloud, application administrators need to find the “best” deployment, satisfying all application (hardware, software, QoS, IoT) requirements over the contextually available resources, also fulfilling non-functional desiderata (e.g., financial costs, security).

Nowadays, Information and Communication Technologies (ICT) are widely adopted in hospitals. Increasingly often medical devices are computer-assisted. Hospital Information Systems (HISs) are not designed to manage the huge amount of data produced by these devices. New paradigms, such as Cloud Computing, by means of its features represents a valid tool to handle this kind of problem. Cloud Computing is very powerful, but it arises issues concerning data privacy. For this reason, clinical operators are reluctant to adopt it in HISs. In this paper, considering two real use-cases coming from the IRCCS “Bonino Pulejo”, a clinical and research center in Messina, we discuss a Cloud Computing architecture able to manage amounts vast of medical data. From a technical point of view, the proposed solution is based on microservices each of them realized for performing a specified task, such as the anonymizer. A microservice that is able to obfuscate users’ sensitive data in order to assure data privacy and to make the system compliant with GDPR.

Distributed software systems composed of two or more self-adaptive components require the presence of a coordination mechanism to ensure the fulfilment of overall system objectives over time. Exploiting elasticity, for example, is one important objective for operating software systems in the cloud. The recently adopted architectural style of independent Microservices, each with its autoscaling mechanism, creates a class of software systems that are composed of several self-adaptive components which provision and release resources in an autonomous manner. Manually evaluating the impact of coordinating actions among autoscaling mechanisms can be complicated because of their large configuration space. So, to aid software engineers in designing and evaluating coordinating actions for achieving overall elastic applications, we propose the Slingshot approach which leverages model-driven quality prediction and search-based software engineering techniques. The approach has three facets: (1) the decomposition of a software architecture into elastic layers where the impact of adaptations propagates in a top-down order; (2) the extension of self-adaptive performance modelling approaches to allow engineers to specify and analyze dependencies between layers; and (3) finding the optimal adaptation strategy of a lower layer for a fixed upper layer context. In this paper, we present a road-map consisting of research objectives and expected benefits of the proposed approach.

Microservices propose to develop applications as suites of small independent services communicating via lightweight mechanisms. Microservices are usually packaged into containers created ad-hoc, which help in deploying applications onto cloud platforms. Even if microservices and containers are already pervading enterprise IT, how to design, refactor and deploy microservice-based applications are key open problems. This paper illustrates our research project aimed at addressing two main research challenges deriving from the above mentioned problems, i.e., (i) analysing and (ii) deploying microservice-based applications.

The release of a growing amount of open procurement data means that we are increasingly able, and even have the obligation, to scrutinize and analyse public spending for delivering better quality of public services, optimizing costs, preventing fraud and corruption, and building healthy and sustainable economies. The TheyBuyForYou project addresses this challenge by developing an integrated technology platform, with a cross-lingual and cross-border procurement knowledge graph, core services, open APIs, and online tools, and validating them in several business cases in public/corporate procurement in Slovenia, Spain and Italy. This paper gives an overview about the project’s goals and challenges.

EW-Shopp is an innovation project, the aim of which is to build a platform for support of data linking, integration, and analytics in companies from the e-commerce, retail, and marketing industries. The project consortium joins several business partners from different sectors of e-commerce including marketing, price comparison, and both web and brick-and-mortar stores. The project is developing several pilot services to test the platform and inform its further development.

The convergence of Internet of Things (IoT), Cloud, and Big Data, creates new challenges and opportunities for data analytics. Human- and machine-created data is being aggregated continuously, transforming our economy and society. To face these challenges, companies call upon expert analysts and consultants to assist them.

In this paper, we present I-BiDaaS, a European Union Horizon 2020 research and innovation project that proposes a self-service solution for Big Data analytics. The solution will be transformative for companies that aim to extract knowledge from big data. It will empower their employees with the right knowledge, and give the true decision-makers the insights they need to make the right decisions. It will shift the power balance within an organization, increase efficiency, reduce costs, improve employee empowerment, and increase profitability. I-BiDaaS aims to empower users to easily utilize and interact with Big Data technologies, by designing, building, and demonstrating, a unified solution that significantly increases the speed of data analysis while coping with the rate of data asset growth, and facilitates cross-domain data-flow towards a thriving data-driven EU economy.

We present the goals, development and results of the SmartSDK project, a FIWARE initiative that aims to create a sustainable FIWARE ecosystem between Europe and Mexico by leveraging on existing FIWARE outcomes and building reference standards for common challenges, providing ready-to-use bundles to simplify the creation of Smart Software Services.

The H2020 FIRST project addresses the virtual factories, which are digital abstractions of real factories. The exploitation of virtual factories enables interoperability between real components inside a factory as well as between different factories belonging to the same supply chain. Moreover, virtual factories can be exploited to manage and compose services inside a factory, defining dynamic adaptation of set of services depending on high-level goals.

In this paper we sketch the project results and its current state.

As systems get more complex testing has also increased not only in complexity but in the total IT cost, which is estimated to increase even more by 2020. Testing large complex distributed applications is hard, time consuming and lacks tooling. Given that the digitisation of business has proved to be a key aspect for improving the productivity of developers in the delivery of the service to end-users, in this paper we present early results showing how these capabilities can also be provided to testers of software and services, by adopting standard interfaces and leveraging the tools provided by an early research open-source platform, capable of efficiently testing large scale systems, ElasTest.

In order to meet complexity, scalability and quality of service requirements in modern network infrastructures, resources need to be allocated and/or optimized along a Cloud-to-Thing (C2T) continuum using new paradigms. To accommodate the dynamism and address the variability, network operators and cloud systems will need to be able to pre-emptively take reconfiguration and remediation actions in a fully automatic fashion across the C2T continuum. In this work, we present the simulator approach in RECAP, an EU-funded research project, to develop the next generation of distributed cloud, edge and fog computing systems. Initial simulation experiments demonstrate that the RECAP Simulation Framework can run large-scale simulations of virtual content delivery networks and inform network optimisation decisions.

This paper describes briefly DOSSIER-Cloud, an ongoing H2020 project called that implements a series of coordination and support actions, aiming at promoting research in the area of Software Engineering for Distributed Systems development. According to the call, two internationally recognized scientific groups from the Netherlands (University of Tilburg – UvT) and Italy (Politecnico di Milano – POLIMI) collaborate with the Cyprus University of Technology (CUT) to facilitate transfer of scientific knowledge and expertise, as well as of best research practices from UvT and POLIMI to CUT, and ultimately strengthen the research and scientific profile of the partners in the relevant area.