Summary The current cloud market is dominated by a few providers, which offer cloud services in a take‐it‐or‐leave‐it manner. However, the dynamism and uncertainty of cloud environments may require the change over time of both application requirements and service capabilities. The current service‐level agreement (SLA) management solutions cannot easily guarantee a trustworthy, distributed SLA adaptation due to the centralized authority of the cloud provider who could also misbehave to pursue individual goals. To address the above issues, we propose a novel SLA management framework, which facilitates the specification and enforcement of dynamic SLAs that enable one to describe how, and under which conditions, the offered service level can change over time. The proposed framework relies on a two‐level blockchain architecture. At the first level, the smart SLA is transformed into a smart contract that dynamically guides service provisioning. At the second level, a permissioned blockchain is built through a federation of monitoring entities to generate objective measurements for the smart SLA/contract assessment. The scalability of this permissioned blockchain is also thoroughly evaluated. The proposed framework enables creating open distributed clouds, which offer manageable and dynamic services, and facilitates cost reduction for cloud consumers, while it increases flexibility in resource management and trust in the offered cloud services.

This paper advances the state-of-the-art by enhancing an existing provider-independent modelling language towards the complete specification of both serverless and hybrid multi-cloud applications. This extension has been validated by a use case developed in the context of the Functionizer project.

Security solutions for cloud applications usually exploit security tools as is by utilising their default configuration. On one hand, this can lead to a waste of resources. On the other hand, it can also lead to not properly protecting the different application components based on their diverse security requirements. To this end, this paper proposes a security solution for cross-cloud applications which is configurable according to the flexible configuration specification given by the devops. Such a specification conforms to a certain UML-based meta-model and is independent of the underlying security tools exploited. In this way, devops can enable to produce a varied security level per each application component that better suits its security requirements. We demonstrate the suitability of our solution through an evaluation showcasing that it can lead to reduced resource consumption without compromising the security of the components that it protects.

Currently, there is a move towards adopting multi-clouds due to their main benefits, including vendor lock-in avoidance and optimal application realisation via different cloud services. However, such multi-cloud applications face a new challenge related to the dynamicity and uncertainty that even a single cloud environment exhibits. As such, they cannot deliver a suitable service level to their customers, resulting in SLA penalty costs and application provider reputation reduction. To this end, we have previously proposed a cross-level and multi-cloud application adaptation architecture. Towards realising this architecture, this paper proposes two extensions of the CAMEL language allowing to specify advanced adaptation rules and histories. Such extensions not only enable to cover cross-level application adaptation by executing adaptation workflows but also to progress such an adaptation to address both the application and exploited cloud services evolution.

Simulations are the greatest means for evaluating systems and producing knowledge related to their optimal configuration for production. Simulation systems support the execution of simulations. These can be installed and executed internally to an organisation or can be offered as a service in the cloud. Current simulation-as-a-service (SimaaS) offerings rely on VM or container-based deployments which lead to additional costs due to the charging in an hourly basis. Further, such offerings cannot be easily adapted at runtime to still be able to sustain their promised service level. To resolve these issues, this paper proposes a novel SimaaS architecture and solution which exploits the serverless computing paradigm for reducing the simulation cost based on the actual usage of resources as well as accelerating the simulation time through the limitless, parallelised invocation of functions. Further, this solution relies on the MELODIC/Functionizer multi-cloud platform which enables adapting the simulation execution at runtime in order to sustain the right service level according to the user requirements and preferences. For the validation of our solution, a real business application provided by AI Investments has been used. It aims to optimise investment portfolio using the most advanced AI-based methods and requires heavy computational power to accomplish the respective tasks.

Cloud services operate in a highly dynamic environment. This means that they need to be assorted with dynamic SLAs which explicate how a rich set of QoS guarantees evolves over time. Only in this way, cloud users will trust and thus migrate their processes to the cloud. Research-wise, SLAs are assumed to include single states while they are managed mainly in a centralised manner. This paper proposes a framework to manage dynamic SLAs in a distributed manner by relying on a rich and dynamic SLA formalism which is transformed into a smart contract. This contract is then handled via the blockchain which exploits an oracle-based interface to retrieve the off-chain cloud service context sensed and enforce the right SLA management/modification functions. The proposed framework can change the current shape of the cloud market by catering for the notion of an open distributed cloud which offers manageable and dynamic services to cloud customers enabling them to reduce costs and increase the flexibility in resource management.

Effective and accurate service discovery and composition rely on complete specifications of service behaviour, containing inputs and preconditions that are required before service execution, outputs, effects and ramifications of a successful execution and explanations for unsuccessful executions. The previously defined Web Service Specification Language (WSSL) relies on the fluent calculus formalism to produce such rich specifications for atomic and composite services. In this work, we propose further extensions that focus on the specification of QoS profiles, as well as partially observable service states. Additionally, a design framework for service-based applications is implemented based on WSSL, advancing state of the art by being the first service framework to simultaneously provide several desirable capabilities, such as supporting ramifications and partial observability, as well as non-determinism in composition schemas using heuristic encodings; providing explanations for unexpected behaviour; and QoS-awareness through goal-based techniques. These capabilities are illustrated through a comparative evaluation against prominent state-of-the-art approaches based on a typical SBA design scenario.

Current PaaS platforms enable single or hybrid cloud deployments. However, such deployment types cannot best cover the user application requirements as they do not consider the great variety of services offered by different cloud providers and the effects of vendor lock-in. On the other hand, multi-cloud deployment enables selecting the best possible service among equivalent ones providing the best trade-off between performance and cost. In addition, it avoids cases of service level deterioration due to service under-performance as main effects of vendor lock-in. While many multi-cloud application deployment research prototypes have been proposed, such prototypes do not examine the effect that deployment decisions have on application performance. As such, they blindly attempt to satisfy low-level hardware requirements by neglecting the impact of allocation decisions on higher-level requirements at the component or application level. To this end, this paper proposes a new IaaS selection algorithm which, apart from being able to satisfy both low and high level requirements of different types, it also exploits deployment knowledge offered via reasoning over previous application execution histories to take the best possible allocation decisions. The experimental evaluation clearly shows that by considering this extra knowledge, more optimal deployment solutions are derived, able to maintain the service levels requested by users, in less solving time.

This paper presents an extension to OWL-Q, a prominent semantic quality-based service description language, called Q-SLA, enabling to specify SLAs. This extension advances the state-of-the-art by covering all possible information aspects needed to enable proper and automatic support to all service management activities. A particular use-case is also provided highlighting Q-SLA's main benefits.

While various platforms are offering facilities for single-cloud application design, deployment and provisioning, there is a need to move to multiple clouds in order to achieve cost-effectiveness and avoid vendor lock-in. Apart from not supporting multi-cloud application management, many platforms usually focus on the deployment and provisioning phases of the cloud-based application lifecycle by neglecting the design phase. However, the design selection of the best possible cloud service composition affects the provisioning phase, as the more distant from optimality is the selected solution, the more adaptation actions will be enacted. To this end, there is a high need for cloud application design tools and methods which can select the best possible cloud service composition based on user requirements. This paper satisfies this need by proposing a cloud service composition approach able to optimally compose different types of cloud services by simultaneously satisfying various types of user requirements. These types, not concurrently supported by any cloud application design tool, include quality, deployment, security, placement and cost requirements. Moreover, the proposed approach addresses a particular design choice type not currently considered in literature.

The benefits of cloud computing have led to a proliferation of infrastructures and platforms covering the provisioning and deployment requirements of many cloud-based applications. However, the requirements of an application may change during its life cycle. Therefore, its provisioning and deployment should be adapted so that the application can deliver its target quality of service throughout its entire life cycle. Existing solutions typically support only simple adaptation scenarios, whereby scalability rules map conditions on fixed metrics to a single scaling action targeting a single cloud environment (e.g., Scale out an application component). However, these solutions fail to support complex adaptation scenarios, whereby scalability rules could map conditions on custom metrics to multiple scaling actions targeting multi-cloud environments. In this paper, we propose the Scalability Rule Language (SRL), a language for specifying scalability rules that support such complex adaptation scenarios of multi-cloud applications. SRL provides Eclipse-based tool support, thus allowing modellers not only to specify scalability rules but also to syntactically and semantically validate them. Moreover, SRL is well integrated with the Cloud Modelling Language (Cloud ML), thus allowing modellers to associate their scalability rules with the components and virtual machines of provisioning and deployment models.

The Service-Oriented Computing (SOC) paradigm is currently being adopted by many developers, as it promises the construction of applications through reuse of existing Web Services (WSs). However, current SOC tools produce applications that interact with users in a limited way. This limitation is overcome by model-based Human-Computer Interaction (HCI) approaches that support the development of applications whose functionality is realized with WSs and whose User Interface (UI) is adapted to the user's context. Typically, such approaches do not consider various functional issues, such as the applications' semantics and their syntactic robustness in terms of the WSs selected to implement their functionality and the automation of the service discovery and selection processes. To this end, we propose a model-driven design method for interactive service-based applications that is able to consider the functional issues and their implications for the UI. This method is realized by a semiautomatic environment that can be integrated into current model-based HCI tools to complete the development of interactive service front-ends. The proposed method takes as input an HCI task model, which includes the user's view of the interactive system, and produces a concrete service model that describes how existing services can be combined to realize the application's functionality. To achieve its goal, our method first transforms system tasks into semantic service queries by mapping the task objects onto domain ontology concepts; then it sends each resulting query to a semantic service engine so as to discover the corresponding services. In the end, only one service from those associated with a system task is selected, through the execution of a novel service concretization algorithm that ensures message compatibility between the selected services.

Service-orientation paves the way for the Internet of Services (IoS), where millions of services will be available to realize the everyday user applications or tasks. Consequently, as a great number of functionally equivalent services will be available for a specific user task, the service nonfunctional aspect should be considered for filtering and selecting among these services. The state-of-the-art approaches in nonfunctional service discovery exploit constraint solving techniques to optimize the matchmaking time between a service offer and demand pair. However, they do not scale well, as matchmaking time is proportional to the offer number, so they are not yet suitable for the IoS. To this end, this article proposes three novel alternative techniques that intelligently organize the service offer space to improve the overall matchmaking time. These techniques are theoretically and experimentally evaluated. The results show that all techniques optimize the matchmaking time without sacrificing accuracy and that each technique is better in different circumstances.

Service-orientation paves the way for the Internet of Services (IoS), where millions of services will be available for building novel applications. As such, the service non-functional aspect should be considered for filtering and selecting among the great number of functionally-equivalent services that will be available for a specific user task. Until now, the state-of-the-art work in non-functional service discovery has exploited constraint solving techniques to optimize the matchmaking time between a non-functional service offer and demand pair. However, as matchmaking time is proportional to the offer number, this work does not scale well so it is not yet appropriate for the IoS. To this end, two alternative techniques are proposed to improve the overall matchmaking time. Both techniques were theoretically and experimentally evaluated. The results show that both techniques optimize the matchmaking time without sacrificing accuracy, while the second technique is quite scalable.

Services are becoming more and more widely used. When designing interactive applications based on services one important issue is how to identify those services most relevant for the application functionalities. The proposed approach takes as input a task model, which includes the user's view of the interactive system, and an ontology capturing the application domain, and automatically discovers a set of ordered service descriptions for each system task of the model. The discovered descriptions can be used in order to invoke a particular service operation that fulfils a task's required functionality. In this way, the whole application functionality can be realized by a set of service operations without writing a single line of code. As a result, the application development time is significantly reduced and it is possible to complete the development of interactive front-ends by integrating our solution in existing model-based HCI approaches.

QoS-based Web Service (WS) discovery has been recognized as the main solution for filtering and selecting between functionally equivalent WSs stored in registries or other types of repositories. There are two main techniques for QoS-based WS matchmaking (filtering): ontology-based and Constraint Programming (CP)-based. Unfortunately, the first technique is not efficient as it is based on the rather immature technology of ontology reasoning, while the second one is not accurate as it is based on syntactic QoS-based descriptions and faulty matchmaking metrics. In our previous work, we have developed an extensible and rich ontology language for QoS-based WS description. Moreover, we have devised a semantic alignment algorithm for aligning QoS-based WS descriptions so as to increase the accuracy of QoS-based WS matchmaking algorithms. Finally, we have developed two alternative CP-based QoS-based WS matchmaking algorithms: a unary-constrained and n-ary-constrained one. In this paper, we claim that Mixed-Integer Programming (MIP) should be used as a matchmaking technique instead of CP and we provide experimental results proving it. In addition, we analyze and experimentally evaluate our matchmaking algorithms against a competing techniques one in order to demonstrate their efficiency and accuracy.

The goal of service oriented architectures (SOAs) is to enable the creation of business applications through the automatic discovery and composition of independently developed and deployed (Web) services. Automatic discovery of Web services (WSs) can be achieved by incorporating semantics into a richer WS description model (WSDM) and by the use of semantic Web (SW) technologies in the WS matchmaking and selection (i.e., discovery) process. A sufficiently rich WSDM should encompass not only functional but also nonfunctional aspects like quality of service (QoS). QoS is a set of performance and domain-dependent attributes that has a substantial impact on WS requesters' expectations. Thus, it can be used for distinguishing between many functionally equivalent WSs that are available nowadays. This paper starts by defining QoS in the context of WSs. Its main contribution is the analysis of the requirements for a semantically rich QoS-based WSDM and an accurate, effective QoS-based WS Discovery (WSDi) process. In addition, a road map of extending current WS standard technologies for realizing semantic, functional, and QoS-based WSDi, respecting the above requirements, is presented.

The goal of service oriented architectures is to enable the creation of business applications through the automatic discovery and composition of independently developed and deployed (Web) services. Automatic discovery of Web Services (WSs) can be achieved by incorporating semantics into a richer WS description model (WSDM) and the use of Semantic Web (SW) technologies in the WS matchmaking and selection models. A sufficiently rich WSDM should encompass not only functional but also non-functional aspects like Quality of Service (QoS). QoS is a set of performance attributes that has a substantial impact on WS requesters' expectations. Thus, it can be used as a discriminating factor of functionally-equivalent WSs. The focus of this paper is twofold: to analyze the requirements of a semantically rich QoS-based WSDM and to provide SW and constrained- based mechanisms for enriching syntactic QoS-based WS Discovery (WSDi) algorithms. In addition, a roadmap of extending WS standard techniques for realizing semantic, functional and QoS-based WSDi is presented.