Cloud-Edge application deployment involves placing multiple software components on infrastructural topologies of heterogeneous nodes, ranging from Cloud servers to Internet-of-Things (IoT) edge devices. When multiple versions (or “ flavours ”) of a component are available, application managers must select a flavour for each deployed component, and assign these components to specific nodes, all while considering constraints such as dependencies, quality of service (QoS), budget, operational costs, and carbon emissions. In complex scenarios, finding the optimal deployment is often infeasible for human operators without automated tools to systematically explore the solution space. To address this challenge, we introduce FREEDA, a first constraint optimisation approach for deploying constrained and multi-flavoured applications on Cloud-Edge infrastructure topologies. We demonstrate the practical feasibility of FREEDA through experiments on a variety of realistic Cloud-Edge infrastructural topologies and component architectures. Furthermore, we benchmark FREEDA against Zephyrus, a comparable tool employing the same underlying solving technology. Empirical results show that FREEDA achieves strong scalability across a broad spectrum of realistic configurations and consistently outperforms Zephyrus.

Data products are emerging as architectural elements of data mesh with the aim of improving data management within organizations. Among the main aspects in the design of a data product is the need to define the policies that regulate its access. These policies concern not only security aspects but, more generally, compliance with regulations (e.g., GDPR, HIPAA) or organizational regulations. Taking advantage of similarities with microservice-based solutions, policy-as-code is also used in the data mesh to regulate access to data products. Following the proposed models for the service mesh which regulates a microservice-based environment, the performance of the Policy Decision Point (PDP) becomes crucial to ensure efficient access to data managed by data products. Since this may require a replication of PDPs, in order to achieve true scalability, each of them will have to take into account only the policies associated with the data products under their responsibilities. This work provides a solution to automate the replication of PDPs and the distribution of policies to be controlled according to an approach that balances policy consistency and system scalability. The work has been validated through an extension of the Kubernetes orchestrator that, based on the information collected from the monitoring system, it is able to define which is the right number of PDPs and which data product, and related policies, should be assigned to each of them. Notably, the policy management is based on a Open Policy Agent (OPA) implementation.

Monitoring business processes within complex supply chains demands efficient data collection and analytics tailored to diverse phenomena. Traditional centralized solutions face limitations in adapting to the dynamic nature of supply chains. This calls for distributed solutions which break the usual architectural assumption to have a central entity in charge of collecting, integrating and offering tools for the analysis. This project, embedded in a larger initiative called MICS, proposes an inno-vative distributed monitoring solution integrating blockchain for a trustworthy and efficient data analytics strategy that preserves data sovereignty in complex collaborative environments. Leveraging the cloud -edge continuum, the solution aims to ensure secure data exchange, adherence to agreements, and real-time analytics. Expected outcomes include an innovative federated architecture, 5G slice management solutions, an adversarial analysis of supply chain security, and a proof-of-concept implementation of the blockchain-based data flow tracking system. These developments aim to enhance the reliability, security, and efficiency of supply chain monitoring in dynamic industrial environments.

Data as a Service (DaaS) offers an effective provisioning model able to exploit the advantages of cloud computing in terms of accessibility and scalability when data providers need to make their data available to different data consumers. Nevertheless, in settings where data are generated at the edge and they need to be propagated (e.g., Industry 4.0, Smart Cities), DaaS model suffers of some limitations: data transfer from the edge to the cloud – and viceversa – could require a significant time and privacy issues could hamper the possibility to move the data. Goal of this paper is to propose a DaaS model based on the Fog Computing paradigm, which combines the advantages of both cloud and edge computing. The proposed solution implements an adaptive multi-agent system where each agent autonomously manages the placement of data in the most convenient location considering the quality of service requirements of the user that it is serving. To guarantee the collaboration of the agents without imposing a centralized control, a reinforcement learning algorithm will be enacted to balance between the local optimum for the single data consumers and the satisfaction of the global requirements of all consumers.

Abstract Being the blockchain and distributed ledger technologies particularly suitable to create trusted environments where participants do not trust each other, business process management represents a proper setting in which these technologies can be adopted. In this direction, current research work primarily focuses on blockchain-oriented business process design, or on execution engines able to enact processes through smart contracts. Conversely, less attention has been paid to study if and how blockchains can be beneficial to business process monitoring. This work aims to fill this gap by (1) providing a reference architecture for enabling the adoption of blockchain technologies in business process monitoring solutions, (2) defining a set of relevant research challenges derived from this adoption, and (3) discussing the current approaches to address the aforementioned challenges.

Modern cloud applications are required to be distributed, scalable, and reliable. The microservice architectural style enables developers to achieve this goal with reduced effort. Nonetheless, microservices deployment is not trivial due to the heterogeneity of the microservices in terms of both functional and non-functional requirements. This is also true when considering the monitoring requirements that are specific to each microservice and must be satisfied in order to enable the verification of the application objectives satisfaction. However, not all providers offer the same set of metrics with the same quality. The goal of this paper is to provide an approach for supporting the deployment of microservices in multi-cloud environments focusing on the Quality of Monitoring. Adopting a multi-objective mixed integer linear optimisation problem, our approach supports the application owner in finding the optimal deployment for satisfying all the constraints and maximising the quality of monitored data, while minimising the costs. To this end, a knowledge base is introduced to mediate between the perspectives of the cloud provider and the application owner, while a Bayesian Network is adopted to enhance the provider’s monitoring capabilities by estimating metrics requested by the application owners that the cloud provider is not able to monitor.

Fog computing is a continuum of resources between the cloud provider and the edge of the network. Such resources can be used to host data and computation that can be moved from the cloud provider to near the consumer in order to increase the quality of the services provided. This tutorial presents a method to select the best data or computation movement to be enacted. The tutorial is divided in three sessions. The first session introduces the fog computing architecture. The second session introduces data and computation movement: their challenges and the available technologies used to enact them. The third session presents the actual method. In particular, how the method uses a goal-based modelling language to define the requirements of consumers, the impact of data and computation movements on their requirements and how it permits to choose the best movement.

The increasing utilization of cloud resources raises several issues related to their environmental impact and, more in general, sustainability. Recently, most of the contributions have focused on energy efficiency achieved through a better physical and virtual resource management. The present paper considers instead the application level, extending the focus to the reduction of CO2emissions related to the execution of applications. We aim to exploit adaptivity through the design of an Application Controller that, enacting the right adaptation strategy for a given context, allows the improvement of the trade off between QoS and CO2emission reduction. The effectiveness of the approach has been shown running an HPC application in a federated cloud infrastructure.

With the increasing popularity of the cloud computing model and rapid proliferation of cloud infrastructures there are increasing concerns about energy consumption and consequent impact of cloud computing as a contributor to global CO2emissions. To date, little is known about how to incorporate energy consumption and CO2concerns into cloud application development and deployment decision models. In this respect, this paper describes an eco-aware approach that relies on the definition, monitoring and utilization of energy and CO2metrics combined with the use of innovative application scheduling and runtime adaptation techniques to optimize energy consumption and CO2footprint of cloud applications as well as the underlying infrastructure. The eco-aware approach involves measuring or quantifying the energy consumption and CO2at different levels of cloud computing, using that information to create scheduling and adaptation techniques that contribute towards reducing the energy consumption and CO2emissions, and finally testing and validating the developed solutions in a multi-site cloud environment with the help of challenging case study applications. The experimental and validation results show the potential of the eco-aware approach to significantly reduce the CO2footprint and consequent environmental impact of cloud applications.

The development and maintenance of cloud sites are often characterized by energy waste and high CO2 emissions. Energy efficiency and the decrease of the CO2 emissions in cloud-based systems can be only obtained by adopting suitable actions and techniques (e.g., utilization of green energy sources, reduction of the number of physical and virtual machines, usage of the greener machines). In order to evaluate the suitability of these different actions, it is necessary to define a measure for greenness of the whole system. For this reason, this paper defines a set of metrics to assess the greenness of a cloud infrastructure. In order to provide a detailed view of the behaviour of the system and to facilitate the identification of the causes of the energy waste, metrics have been defined at different layers of the system (i.e., application, virtualization, infrastructure layers). The monitoring infrastructure that is necessary to retrieve all the data required for the assessment of the identified set of metrics is also described.

SUMMARY Mobile devices with their more and more powerful resources allow the development of mobile information systems in which services are not only provided by traditional systems but also autonomously executed and controlled in the mobile devices themselves. Services distributed on autonomous mobile devices allow both the development of cooperative applications without a back‐end infrastructure and the development of applications blending distributed and centralized services. In this paper, we propose MicroMAIS: an integrated platform for supporting the execution of Web service‐based applications natively on a mobile device. The MicroMAIS platform is composed of mAS and μ‐BPEL. The former allows the execution of a single Web service, whereas the latter permits the orchestration of several Web services according to the WS‐BPEL standard. Copyright © 2011 John Wiley & Sons, Ltd.

Service retrieval holds a central role during the development of Web services and Service-Based Applications (SBAs). The higher the number of available services, the more complex it becomes to locate the service closer to the developer needs. The complexity increases further with the number of available service versions that could also be suitable for this purpose. Existing approaches on service retrieval use a similarity measure between service interfaces to identify potentially relevant services. In this work we focus on introducing information about the compatibility of services while calculating their similarity as the means for providing more suitable results. For this purpose we update and extend an existing Web services matchmaker called UDDI Registry by Example (URBE).

In this work, we present UDDI registry by example (Urbe), a novel approach for Web service retrieval based on the evaluation of similarity between Web service interfaces. Our approach assumes that the Web service interfaces are defined with Web service description language (WSDL) and the algorithm combines the analysis of their structures and the analysis of the terms used inside them. The higher the similarity, the less are the differences among their interfaces. As a consequence, Urbe is useful when we need to find a Web service suitable to replace an existing one that fails. Especially in autonomic systems, this situation is very common since we need to ensure the self-management, the self-configuration, the self-optimization, the self-healing, and the self-protection of the application that is based on the failed Web service. A semantic-oriented variant of the approach is also proposed, where we take advantage of annotations semantically enriching WSDL specifications. Semantic Annotation for WSDL (SAWSDL) is adopted as a language to annotate a WSDL description. The Urbe approach has been implemented in a prototype that extends a universal description, discovery and integration (UDDI) compliant Web service registry.

Abstract Web service composition is emerging as an interesting approach to integrate business applications and create intra‐organizational business processes. Single Web services are combined to create a complex Web service that will realize the process business logic. Once the process is created, it is executed by an orchestration engine that invokes individual Web services in the correct order. However, Web services composing the workflow sometimes become unavailable during the run‐time phase, blocking process execution. This paper describes an architecture that allows the flexible orchestration of business processes. With this approach, Web services composing the process can be automatically substituted with other compatible Web services during process execution. A methodology is defined to evaluate Web service compatibility based on interface matching, in order to select substitutable Web services. Copyright © 2005 John Wiley & Sons, Ltd.

The Web services platform is gaining popularity as the technology of choice for integrating applications in diverse and heterogeneous distributed environments, such as the Internet. It is widely recognized that one of the barriers preventing widespread adoption of this technology is a lack of products that support non-functional features of applications, such as security, transactionality and reliability. Supporting such features in a service-oriented environment is more complex than traditional distributed computing environments since such behaviors cannot be assumed by applications, but some persistent representation of the behavior has to be discovered dynamically. WS-Policy has been touted as a possible future standard way to specify these features and associate them with services, but the multitude of related proposals resemble a poorly manufactured jigsaw puzzle more than a coherent vision.