The development of smart grids requires the active participation of end users through demand response mechanisms to provide technical benefits to the distribution network and receive economic savings. Integrating advanced machine learning tools makes it possible to optimise the network and manage the mechanism to maximise the benefits. This paper proceeds by forecasting consumption for the next 24 h using a recurrent neural network and by processing these data using a reinforcement learning-based optimisation model to identify the best demand response policy. The model is tested in a real environment: a portion of the Terni electrical distribution network. Several scenarios were identified, considering users' participation at different levels and limiting the potential with various constraints.

AbstractThe specification of deployment topologies for complex applications distributed across multiple heterogeneous infrastructures is a difficult process that encompasses multiple modeling tasks, engaging several actors, including application ops experts, resource experts on the specification of the target infrastructure resources, quality experts on the application optimization, and application administrators on the deployment governance. SODALITE proposes a novel infrastructure as a code (IaC) modeling framework that provides a model driven engineering approach for the authoring of application- and infrastructure-level specifications, realizing an instantiation of an infrastructure as a code (IaC) modeling framework. This chapter introduces the SODALITE IDE and the IaC services. The IDE enables SODALITE expert roles to model (conforming to the SODALITE DSMLs) and generate IaC artefacts facilitating the app deployment. Experts are assisted in the modeling phase by the semantic knowledge inference and validation capabilities of a Knowledge Base (KB), which is populated with IaC descriptions for resources semi-automatically discovered from target heterogeneous infrastructures. The IDE leverages the SODALITE IaC services for automatic target image preparation and IaC artifacts generation upon deployment.

Cloud computing is a mature paradigm that has evolved to accommodate ever-increasing complex applications such as in the AI and HPC domain. If applications are complex, infrastructure can be even more, spanning over hybrid architectures. As such, producing a less error-prone deployment while offering high performance requires application and infrastructure awareness, and also deep knowledge of the IaC languages. In this paper, we present the SODALITE IDE, a suite that assists the users in the authoring of application deployment topology and infrastructure models for IaC. With focus on quality and performance, the IDE enables the faster and simpler development of IaC by offering features such as in-sync multiple model viewpoints , smart context-aware content assistance and semantic validation, powered by a Knowledge Base.

Complex applications, which include microservices, computationally intensive batch jobs, and sophisticated interaction with the external environment, demand for heterogeneous computational infrastructures that range from cloud to HPC and edge computing. In this context, a crucial problem is to facilitate the work of DevOps teams in i. the conception of the right operational architecture for the application, ii. its transformation into infrastructural code that automates its deployment, taking into account the peculiarities of each of the diverse infrastructures involved in this, and iii. its operation. The SODALITE framework aims at addressing this scenario. This paper presents the main features offered by the first version of the framework, currently focusing on managing cloud and HPC clusters, and shows them in practice through a relevant case study.

In this paper, we overview our experiences of developing large set of open source tools in ECSEL JU European project called MegaM@Rt2 whose main objective is to propose a scalable model-based framework incorporating methods and tools for the continuous development and runtime support of complex software-intensive Cyber-Physical Systems (CPSs). We briefly present the MegaM@Rt2 concepts, discuss our approach for open source, enumerate tools and give an example of a tools selection for a specific industrial context. Our goal is to introduce the reader with open source tools for the model-based engineering of CPSs suitable for diverse industrial applications.

Successful software evolution heavily depends on the selection of the right features to be included in the next release. Such selection is difficult, and companies often report bad experiences about user acceptance. To overcome this challenge, there is an increasing number of approaches that propose intensive use of data to drive evolution. This trend has motivated the SUPERSEDE method, which proposes the collection and analysis of user feedback and monitoring data as the baseline to elicit and prioritize requirements, which are then used to plan the next release. However, every company may be interested in tailoring this method depending on factors like project size, scope, etc. In order to provide a systematic approach, we propose the use of Situational Method Engineering to describe SUPERSEDE and guide its tailoring to a particular context.

Dynamic reconfiguration of software systems can be achieved by exploiting variability models of such systems, combined with mechanisms for selecting and instantiating the appropriate system configuration. We developed a model-driven approach for dynamic software reconfiguration, which uses a component profile-oriented feature model for representing different configurations of a software system, and a grammar based genetic programming tool that, at run-time, automatically generates an optimal system configuration. The resulting feature configuration is transformed to a target format such as JSON, SQL or other specification that allows us to instantiate the new system configuration. In this paper we focus on the run-time reasoning and propagation aspect, and discuss our experience on applying the approach to a use case.

Software Product Lines (SPLs) capture commonalities and variability of product families, typically represented by means of feature models. The selection of a set of suitable features when a software product is configured is typically made by exploring the space of treadoffs along different attributes of interest, for instance cost and value. In this paper, we present an approach for optimal product configuration by exploiting feature models and grammar guided genetic programming. In particular, we propose a novel encoding of candidate solutions, based on grammar representation of feature models, which ensures that relations imposed in the feature model are respected by the candidate solutions.

Over the past decade, cloud services emerged as one of the most promising technologies in IT. Since cloud computing allows improving the quality of software and, at the same time, aims at reducing costs of operating software and hardware, more and more software is delivered as a service in the cloud. However , moving existing software applications to the cloud and making them behave as software as a service is still a major challenge. In fact, in addition to technical aspects, business aspects also need to be considered. The ARTIST EU project (FP7) proposes a comprehensive model-based modernization approach, covering both business and technical aspects, to cloudify already existing software. In particular , ARTIST employs MDE techniques to automate the reverse engineering and forward engineering phases in a way that modernized software truly benefits from targeted cloud environments. In this paper we describe the overall ARTIST approach and present several lessons learned.

Nowadays Cloud Computing is considered as the ideal environment for engineering, hosting and provisioning applications. A continuously increasing set of cloud-based solutions is available to application owners and developers to tailor their applications exploiting the advanced features of this paradigm for elasticity, high availability and performance. Although these offerings provide many benefits to new applications, they also incorporate constrains to the modernization and migration of legacy applications by obliging the use of specific technologies and explicit architectural design approaches. The modernization and adaptation of legacy applications to cloud environments is a great challenge for all involved stakeholders, not only from the technical perspective, but also in business level with the need to adapt the business processes and models of the modernized application that will be offered from now on, as a service. In this paper we present a novel model-driven approach for the migration of legacy applications in modern cloud environments which covers all aspects and phases of the migration process, as well as an integrated framework that supports all migration process.

Cloud computing has leveraged new software development and provisioning approaches by changing the way computing, storage and networking resources are purchased and consumed. The variety of cloud offerings on both technical and business level has considerably advanced the development process and established new business models and value chains for applications and services. However, the modernization and cloudification of legacy software so as to be offered as a service still encounters many challenges. In this work, we present a complete methodology and a methodology instantiation framework for the effective migration of legacy software to modern cloud environments.

Modernizing an IT system is a long, complex journey. The pre-migration phase is the starting point of each migration project where the decision to transform the legacy rather than to rewrite it has to be taken. In order to support this decision making, the ARTIST European project [1] proposes a technical feasibility analysis to as much technical information as possible about the legacy application itself and about the required technical tasks to migrate its components. This paper presents a technical feasibility analysis which relies on Cloud Migration Point approach to estimate the cost of the migration (in terms of required effort) and incorporates techniques such as Model Driven Reverse Engineering, software complexity metrics or Domain Specific Languagebased heuristics to automate this process as much as possible, although leaving to the user the knowledge and control all over

As cloud computing allows improving the quality of software and aims at reducing costs of operating software, more and more software is delivered as a service. However, moving from a software as a product strategy to delivering software as a service hosted in cloud environments is very ambitious. This is due to the fact that managing software modernization is still a major challenge, especially when paradigm shifts, such as moving to cloud environments, are targeted that imply fundamental changes to how software is modernized, delivered, and sold. Thus, in addition to technical aspects, business aspects need also to be considered. ARTIST proposes a comprehensive software modernization approach covering business and technical aspects. In particular, ARTIST employs Model-Driven Engineering (MDE) techniques to automate the reverse engineering of legacy software and forward engineering of cloud-based software in a way that modernized software truly benefits from targeted cloud environments. Therewith, ARTIST aims at reducing the risks, time, and costs of software modernization and lowers the barriers to exploit cloud computing capabilities and new business models.

Many software companies have in mind jumping into the Cloud in order to take advantage of this technical paradigm as well as the innovative business models associated (such as SaaS). However, taking this leap becomes a hard task since it implies a high uncertainty and a raised risk without knowing when or even if the investment will be recovered. This paper presents the solution proposed by the ARTIST project to assess companies which are considering the migration of their products to Cloud, and provide estimations of the costs, ROI, efforts and migration tasks that this process could imply. The envisioned solution comprises a pre-migration phase through which 1) the maturity (to migrate to Cloud) of the application and the company will be measured, 2) a feasibility analysis both at technical and business level will be performed. In addition to the general approach of the proposed solution, this paper presents the first results obtained by means of a theoretical exercise conducted with the PetStore Java application.

This paper shows the ontology development for the second version of the prototype Iuriservice II, an i-FAQ for judicial use containing a repository of professional judicial knowledge. We define the epistemological and ontological levels as separate issues. The domain and middle-out ontology will be connected to the SEKT top and upper level ontology PROTON. In the second part of the paper, we introduce a refinement of the architectural design, especially conceived to improve the user/system interaction by using searching, scoring and matching algorithms for a multidisciplinary ontological processing.