Background: Global Software Engineering (GSE) extends geographical, temporal, and cultural boundaries in distributed environments. Over the past two decades, GSE research has evolved to manage software development for distributed teams. The COVID-19 pandemic highlights the need for comprehensive research, particularly during the software design phase, to support team collaboration in distributed development. Aim: This study systematically analyzes the evolution of research emphasis in the GSE field, specifically exploring on whether the research focuses increasing on software design due to the global pandemic. Method: We systematically analyzed the existing literature in two phases. In the first phase of our study, we mapped GSE research over the two decades leading to the pandemic (2000–2020). In the second phase, we used the forward snowballing approach to examine the literature on the software design phase published between 2020 and 2022. Results: The analysis of 592 research studies in the two phases reveals various trends in GSE research. Evaluation research is the most explored research type in methods and processes, and human aspects of development. Despite the paradigm shift caused by the COVID-19 pandemic that increased reliance on distributed teams, results show that while software organizations are extensively studied across all software engineering phases, the software design phase remains one of the least explored areas. Conclusion: This work highlights the evolving GSE research trends, emphasizing the rising significance of collaborative software design in distributed settings. Our findings address current research gaps and underscore the need for further research on software design activities. This contribution envisions a more collaborative, adaptable GSE field, guiding future research to support distributed teams.

Edge computing needs to deal with concerns such as load balancing, resource provisioning, and workload placement as optimization problems. Particle Swarm Optimization (PSO) is a nature-inspired stochastic optimization approach that aims at iteratively improving a solution of a problem over a given objective. Utilising PSO in a distributed edge setting would allow the transfer of resource-intensive computational tasks from a central cloud to the edge, this providing a more efficient use of existing resources. However, there are challenges to meet performance and fault tolerance targets caused by the resource-constrained edge environment with a higher probability of faults. We introduce here distributed synchronous and asynchronous variants of the PSO algorithm. These two forms specifically target the performance and fault tolerance requirements in an edge network. The PSO algorithms distribute the load across multiple nodes in order to effectively realize coarse-grained parallelism, resulting in a significant performance increase.

While entering the post-COVID-19 pandemic phase, to define a new normal way of working, some companies are transitioning toward a permanent WFX model, while others are combining WFX with colocated work (i.e., hybrid work). Therefore, fostering WFX skills (usually classified as soft skills) in early-career students becomes crucial; additionally, it can help reduce early school leaving. This work aims at understanding how business simulation projects foster the WFX skills deemed crucial by industries. To this end, we conducted two case studies involving high school students. The final questionnaire revealed that most participants evaluate their WFX as fair or higher. Moreover, they believe that business simulation projects help in developing WFX skills. Based on our results, we highlight recommendations for educational practice.

Blockchains are used in many software systems to deal with trusted storage. The selection of the appropriate software architecture stack in distributed systems is generally driven by scalability, security, and decentralization as central qualities. In the blockchain domain, these are known as the blockchain trilemma, as they oppose each other. We select the most popular blockchain platforms based on these trilemma properties and other indicators to provide a platform review. Specific metrics will be derived from the overall goals and applied to the platform options. This serves as a basis to create a Selection framework to facilitate the choice of the best possible platform for a given system architecture. The selection framework is evaluated through a use case.

In many Web and Internet-based systems, sharing Personally Identifiable Information (PII) to identify persons and other entities is common, but centralized systems such as central registries have limitations in terms of control of privacy and identity that a decentralized identity management architecture could address. This study aims to compare the current and potential systems, analyze protocols for decentralized identification and data exchange, propose a protocol selection method, and provide a simple code example. The goal is to assess the feasibility of decentralized processes in software-based business workflows. The methodology involves reviewing protocol materials, including white-papers, articles, and code docs, alongside ontological aspects of identification. Challenges to implementing Decentralized Identifiers (DIDs) include interoperability and the evolving Web/Internet landscape towards more decentralization, openness, and greater user utility.

Computation at the edge or within the Internet-of-Things (IoT) requires the use of controllers to make the management of resources in this setting self-adaptive. Controllers are software that observe a system, analyse its quality and recommend and enact decisions to maintain or improve quality. Today, often reinforcement learning (RL) that operates on a notion of reward is used to construct these controllers. Here, we investigate quality metrics and quality management processes for RL-constructed controllers for edge and IoT settings. We introduce RL and control principles and define a quality-oriented controller reference architecture. This forms the based for the central contribution, a quality analysis metrics framework, embedded into a quality management process.

Self-adaptive systems such as clouds and edge clouds are more and more using Machine Learning (ML) techniques if sufficient data is available to create respective ML models. Self-adaptive systems are built around a controller that, based on monitored system data as input, generate actions to maintain the system in question within expected quality ranges. Machine learning (ML) can help to create controllers for self-adaptive systems such as edge clouds. However, because ML-created controllers are created without a direct full control by expert software developers, quality needs to be specifically looked at, requiring a better understanding of the ML models. Here, we explore a quality-oriented management and governance architecture for self-adaptive edge controllers. The concrete objective here is the validation of a reference governance architecture for edge cloud systems that facilitates ML controller quality management in a feedback loop.

Background. Global Software Engineering (GSE) education is an established practice in academia. Several methods and tools support communication and programming activities, but earlier development stages, such as software design and modeling practices, are less explored. Aim. The goal of this work is to analyze the learners’ perspective during an online Software Engineering course. In particular, we focus on planning/organization activities and socio-technical challenges during the software design and modeling process. Method. We used a mixed-method approach to collect data from 30 undergraduate students enrolled in an online Software Engineering course. We combined questionnaires and interviews to analyze four GSE elements (i.e., communication practices, team collaboration, task allocation and distribution, and usage of collaboration tools). Moreover, we analyzed the socio-technical challenges faced by the teams. Results. Brainstorming is the most common practice used for planning so (...).

The world of work-from-home (WFH) and work-from-anywhere (abbreviated WFX or WFA) grew more than ever during the COVID-19 pandemic. Many companies are now planning on permanently allowing WFX, with subsequent demand for specific skills. It is crucial to foster these skills in high schools, as students may enter the job market without attending University. Up-to-date analysis of the current situation is missing to align supply and demand by enabling educators to set goals and strategies to train students so that they will be able to mesh into a WFX setting. To address this issue, we collected complementary data using a case study (23 students), a questionnaire (616 students), and interviews with professionals and practitioners of the software development sector. Results suggest that information management, communication skills, autonomy, and resourcefulness are key competencies that enable professionals to succeed in a WFX environment. However, students feel less prepared in terms of communication skills; moreover, they lack time management and autonomy skills. Based on our results, we highlight recommendations for educational practice that educators can use in curriculum building to fill the gaps that emerged in this study to assure the effective development of the skillset demanded by current and future WFX conditions.

Cooperative Maneuvering is a well-known application in the field of Cooperative, Connected, and Automated Mobility (CCAM). However, extensions are desired to offer more efficient, reliable, and safer solutions. With the introduction of the latest 5G technology, such features can be finally supported with the backup of real-time measurements of multiple vehicles on the road. Projects like the European 5G-CARMEN, address such challenges by designing and implementing edge and cloud based services that take advantage of 5G's high performance and reliability. This paper addresses the simulation of a Cooperative Maneuvering function in the context of the 5G-CARMEN project. Cooperative Maneuvering allows safer and more efficient navigation among multiple vehicles by applying optimal driving strategies. The simulation focuses on the scenario where a vehicle is on the entry lane of a highway and it needs to perform a lane change. Therefore, vehicles depending on their distance and location are advised to apply appropriate actions to make this lane change possible. Additionally, to find the best simulator for a Cooperative Maneuvering implementation a systematic survey of four simulators presents.

Data-as-a-Service (DaaS) solutions make raw source data accessible in the form of processable information. Machine learning (ML) allows to produce meaningful information and knowledge based on raw data. Thus, quality is a major concern that applies to raw data as well as to information provided by ML-generated models. At the core of the solution is a conceptual framework that links input data quality and the machine learned data service quality, specifically inferring raw data problems as root causes from observed data service deficiency symptoms. This allows to deduce the hidden origins of quality problems observable by users of DaaS offerings. We analyse the quality framework through an extensive case study from an edge cloud and Internet-of-Things-based traffic application. We determine quality assessment mechanisms for symptom and cause analysis in different quality dimensions.

Dependability Engineering is a critical importance for the now huge number of software systems that are directly linked to people (and can damage them - be that physically or through security and data protection issues) and that operate in open and distributed environments that expose them to reliability problems. We present here an integrated dependability analysis and design methods for reliability, safety and security that targets beginners and that can be applied without specific tools support. We introduce this analysis and design method here in a concrete educational setting that is made up of beginners and works without tools. We will look at this specifically in the context of Internet-of-Things data with realistic application cases.

Summary In cloud computing, resizing component resources is often limited by the available resources offered by a provider. After reaching a resource limit, a component cannot acquire more resources, which can badly affect the load situation. This article presents multiple predictable recovery actions of a self‐healing model for an identified anomalous behavior (eg, overload, underload) to auto‐scale compute resources in a containerized cluster environment according to various workload conditions. The efficacy of the model is demonstrated through an evaluation with different auto‐scaling strategies based on the number of created/terminated containers, container migration, resource utilization, and response time. The results show that the proposed model provides promising overall performance under dynamic workloads compared to other auto‐scaling strategies.

Internet of Things (IoT) is composed of physical devices, communication networks, and services provided by edge systems and over-the-top applications. IoT connects billions of devices that collect data from the physical environment, which are pre-processed at the edge and then forwarded to processing services at the core of the infrastructure, on top of which cloud-based applications are built and provided to mobile end users. IoT comes with important advantages in terms of applications and added value for its users, making their world smarter and simpler. These advantages, however, are mitigated by the difficulty of guaranteeing IoT trustworthiness, which is still in its infancy. IoT trustworthiness is a must especially in critical domains (e.g., health, transportation) where humans become new components of an IoT system and their life is put at risk by system malfunctioning or breaches. In this article, we put forward the idea that trust in IoT can be boosted if and only if its automation and adaptation processes are based on trustworthy data. We therefore depart from a scenario that considers the quality of a single decision as the main goal of an IoT system and consider the trustworthiness of collected data as a fundamental requirement at the basis of a trustworthy IoT environment. We therefore define a methodology for data collection that filters untrusted data out according to trust rules evaluating the status of the devices collecting data and the collected data themselves. Our approach is based on blockchain and smart contracts and collects data whose trustworthiness and integrity are proven over time. The methodology balances trustworthiness and privacy and is experimentally evaluated in real-world and simulated scenarios using Hyperledger fabric blockchain.

Performance and dependability levels of cloud-based computations are difficult to guarantee by-design due to segregation of visibility and control between applications, data owners, and cloud providers. Lack of predictability increases users' uncertainty about the service levels they will actually achieve. Cloud tenants compete for shared resources/services at all layers of the cloud stack, and pose heterogeneous and conflicting non-functional requirements over them. These requirements have implications for platform and infrastructure layers, which have to be configured to satisfy inter-tenants requirements. We argue that adaptation techniques can play a crucial role in providing a reliable cloud, supporting definite behavior of applications and stable quality of service. Existing adaptation techniques however are unsuitable for cloud use, since they mostly focus on single tenancy, performance requirements, and are based on unverifiable evidence, which is collected in an untrusted way. In this paper, we propose a multi-tenant, general-purpose adaptation technique for the cloud, based on evidence collected by means of a trustworthy certification process. We depart from traditional heavy and comprehensive certification processes, such as ISO/IEC 27017, and consider a flexible and lightweight certification process for the cloud. It is based on authentic evidence and provides accountable validation on the compliance of a cloud-based system. Our approach adapts the cloud at all layers to maintain stable non-functional properties in certificates over time, by continuously verifying certificate validity. We assess the performance and quality of our adaptation approach in a wide range of settings.

Edge computing extends cloud computing capabilities to the edge of the network, allowing for instance Internet-of-Things (IoT) applications to process computation more locally and thus more efficiently. We aim to minimize latency and delay in edge architectures. We focus on an advanced architectural setting that takes communication and processing delays into account in addition to an actual request execution time in a performance engineering scenario. Our architecture is based on multi-cluster edge layer with local independent edge node clusters. We argue that particle swarm optimisation as a bio-inspired optimisation approach is an ideal candidate for distributed load processing in semi-autonomous edge clusters for IoT management. By designing a controller and using a particle swarm optimization algorithm, we can demonstrate that processing and propagation delay and the end-to-end latency (i.e., total response time) can be optimized.

Edge clusters consisting of small and affordable single-board devices are used in a range of different applications such as microcontrollers regulating an industrial process or controllers monitoring and managing traffic roadside. We call this wider context of computational infrastructure between the sensor and Internet-of-Things world and centralised cloud data centres the edge or edge computing. Despite the growing hardware capabilities of edge devices, resources are often still limited and need to be used intelligently. This can achieved by providing a self-adaptive scaling component in these clusters that is capable of scaling individual parts of the application running in the cluster. We propose an auto-scalable container-based cluster architecture for lightweight edge devices. A serverless architecture is at the core of the management solution. Our auto-scaler as the key component of this architecture is based on fuzzy logic in order to address challenges arising from an uncertain environment. In this context, it is crucial to evaluate the capabilities and limitations of the application in a real-world context. Our results show that the proposed platform architecture, the implemented application and the scaling functionality meet the set requirements and offer a basis for lightweight edge computing.

Clusters consisting of lightweight single-board devices are used in a variety of use cases: from microcon-trollers regulating the production process of an assembly line to roadside controllers monitoring and managing traffic. Often, data that is accumulated on the devices has to be sent to remote cloud data centers for processing. However, with hardware capabilities of controllers continuously increasing and the need for better performance and security through local processing, directly processing data on a remote cluster, known as Edge Computing, is a favourable solution. Recent trends such as microservices, containerization and server-less technology provide solutions for interconnecting the nodes and deploying software across the created cluster. This paper aims at proposing a serverless architecture for clustered container applications. The architecture relies on the MQTT protocol for communication, Prometheus for monitoring and Docker swarm in conjunction with openFaas for deploying services across a cluster. Using the proposed architecture as a foundation, a concrete traffic management application is implemented as a proof-of-concept. Results show that the proposed architecture meets performance requirements. However, the network setup as well as the network capabilities of the used devices were identified as potential bottlenecks.

[Context] Serverless is a recent technology that enables companies to reduce the overhead for provisioning, scaling and in general managing the infrastructure. Companies are increasingly adopting Serverless, by migrating existing applications to this new paradigm. Different practitioners proposed patterns for composing and managing serverless functions. However, some of these patterns offer different solutions to solve the same problem, which makes it hard to select the most suitable solution for each problem. [Goal] In this work, we aim at supporting practitioners in understanding the different patterns, by classifying them and reporting possible benefits and issues. [Method]We adopted a multivocal literature review process, surveying peer-reviewed and grey literature and classifying patterns (common solutions to solve common problems), together with benefits and issues. [Results] Among 24 selected works, we identified 32 patterns that we classified as orchestration, aggregation, event-management, availability, communication, and authorization. [Conclusion] Practitioners proposed a list of fairly consistent patterns, even if a small number of patterns proposed different solutions to similar problems. Some patterns emerged to circumvent some serverless limitations, while others for some classical technical problems (e.g. publisher/subscriber).

In the current labor market, a large number of people engage in programming activities, even without being trained developers. Fostering Software Engineering (SE) principles at the K-12 level can increase the quality of the code that students will write in their future careers. Middle school students usually learn Computer Science (CS) as part of other disciplines; thus, the challenge is achieving the CS learning objectives and foster SE principles while fulfilling the curricular objectives. In this work, we describe a didactic module and its assessment framework; moreover, we report the results of a classroom experience that shows the effectiveness of the proposed approach. This work provides educators with a practical example of how to cover several areas of technology in a way for middle school students to be engaged and to spark future interest. The results encourage us to work on the development of the next modules dedicated to middle schools.

Cooperative, Connected, Automated and Autonomous Mobility (CCAM) has a significant effect on improving traffic, lowering pollution and, most significantly, has enormous potential for safer roads. Mobile Edge Computing (MEC) is placing part of the cloud infrastructure at the edge of the network to boost performance and provide context-conscious services. Many of the drawbacks to today's technology could be overcome in tandem with the expected high capacity 5G. Service continuity (SC) across organizational and territorial borders in a highway scenario is one of the main challenges of the EU 5G-CARMEN project. The project tackles this problem by designing and implementing MEC-based services that act as bridges between various realms while taking advantage of 5G's high performance and reliability. This paper addressed the continued introduction of SC frameworks in the context of the 5G-CARMEN project. New high-level prediction algorithms and methods to boost SC have also been proposed.

With telecommunication technology development now moving to a different generation of wireless communication from 4G to 5G, user connections become faster and more reliable. However, from the view of application developers, there is still a testbed environment lacking to evaluate their systems reliability and performance. In this paper, we present an extension for the LTE module collaboration via roaming based on NS-3 simulator, which will provide an ecosystem for on-car service testing as a sample context. We also evaluate the model with specific metrics to check whether the model is adaptable to real-time processes.

The constant increase of the amount of data generated by Internet of Things (IoT) devices creates challenges for the supporting cloud infrastructure, which is often used to process and store the data. This work focuses on an alternative approach, based on the edge cloud computing model, i.e., processing and filtering data before transferring it to a backing cloud infrastructure. We describe the implementation of a low-power and low-cost cluster of single board computers (SBC) for this context, applying models and technologies from the Big Data domain with the aim of reducing the amount of data which has to be transferred elsewhere. To implement the system, a cluster of Raspberry Pis was built, relying on Docker to containerize and deploy an Apache Hadoop and Apache Spark cluster, on which a test application is then executed. A monitoring stack based on Prometheus, a popular monitoring and alerting tool in the cloud-native industry, is used to gather system metrics and analyze the performance of the setup. We evaluate the complexity of the system, showing that by means of containerization increased fault tolerance and ease of maintenance can be achieved, which makes the proposed solution suitable for an industrial environment. Furthermore, an analysis of the overall performance, which takes into account the resource usage of the proposed solution with regards to the constraints imposed by the devices, is presented in order to discuss the capabilities and limitations of proposed architecture.

Many activities, such as computational thinking courses, are nowadays proposed in K-12 to prepare students for the current labour market, where being able to creatively use technology to solve problems is becoming increasingly important, and where more and more people are engaged in programming activities. Thus, there is a need to equip students with the necessary means to improve software quality, including non-vocational schools, where the challenge is leveraging existing curricular, non-programming activities to this end. This work explores the possibility of fostering software engineering principles in non-vocational high schools through curricular, non-programming activities. We describe two didactic modules and report the results of a classroom experience (involving 16 high school first-year students) that has been carried out to understand the effectiveness of the proposed approach. During the didactic modules, the participants achieved the objectives of the curricular activity, and at the same time learned how to organize their work by applying software en- gineering principles. These results allow us to formulate hypotheses for further work, such as extending our approach to other activities and observe if and when students will develop a “software engineering mindset”.

Load balancing and workload distribution cause challenges for the management of IoT and distributed systems in the edge computing environment. Swarm intelligence is a technology suitable for the management of distributed systems, networks, communication and routing protocols. Swarm intelligence-based PSO algorithms (particle swarm optimization) can be applied for load balancing and task scheduling in cloud computing environments operating through a broker agent. In distributed cloud environments, data is collected and then processed at the center of the cloud, rather than making decision at edge nodes closer to IoT infrastructures. Here, we develop an automated orchestration technique for clustered cloud architectures. An Autonomous Particle Swarm Optimization, called the A-PSO algorithm, is implemented that enables an edge node, such as a remote storage, to work as part of a decentralized, self-adaptive intelligent task scheduling and load balancing agant between resources in distributed systems. Using Multi Objective Optimization (MOO), complementing the A-PSO algorithm, we also include metrics such as Actual Round-Trip Time (ARTT) of tasks assignments to the remote storage to reduce the execution cost. Our A-PSO algorithm can orchestrate the distribution of large volumes of data to remote storage and back in cluster, i.e., coordinated distributed cloud environments.

Containers as a lightweight technology to virtualise applications have recently been successful, particularly to manage applications in the cloud. Often, the management of clusters of containers becomes essential and the orchestration of the construction and deployment becomes a central problem. This emerging topic has been taken up by researchers, but there is currently no secondary study to consolidate this research. We aim to identify, taxonomically classify and systematically compare the existing research body on containers and their orchestration and specifically the application of this technology in the cloud. We have conducted a systematic mapping study of 46 selected studies. We classified and compared the selected studies based on a characterisation framework. This results in a discussion of agreed and emerging concerns in the container orchestration space, positioning it within the cloud context, but also moving it closer to current concerns in cloud platforms, microservices and continuous development.

Cloud migration is concerned with moving an on-premise software system into the cloud. In this paper we focus on software producers adopting the cloud to provide their solutions to enterprise customers. Their challenge is to migrate a software product, developed in-house and traditionally delivered on-premise, to an Infrastructure-as-a-Service or Platform-as-a-Service solution, while also mapping an existing traditional licensing model on to a cloud monetization model. The analysis of relevant cost types and factors of cloud computing generate relevant information for the software producers when deciding to adopt cloud computing, and defining software pricing. We present an integrated framework for informing cloud monetization based on operational cost factors for migrating to the cloud and test it in a real-life case study. Differences between basic virtualization of the software product and using fully cloud-native platform services for re-architecting the product in question are discussed.

Microservices is an architectural style increasing in popularity. However, there is still a lack of understanding how to adopt a microservice-based architectural style.\nWe aim at characterizing different microservice architectural style patterns and the principles that guide their definition.\nWe conducted a systematic mapping study in order to identify reported usage of microservices and based on these use cases extract common patterns and principles.\nWe present two key contributions.\nFirstly, we identified several agreed microservice architecture patterns that seem widely adopted and reported in the case studies identified.\nSecondly, we presented these as a catalogue in a common template format including a summary of the advantages, disadvantages, and lessons learned for each pattern from the case studies.\nWe can conclude that different architecture patterns emerge for different migration, orchestration, storage and deployment settings for a set of agreed principles.

In recent years, an increasing number of people (called “end-users”) have started to perform a range of activities related to software development, such as coding with domain-specific languages. The research in the area of End-User Software Engineering (EUSE) aims at improving the quality of end-user-produced software by paying attention to the entire software life cycle. The increasing number of activities dedicated to the diffusion of coding in K-12 motivates the need of EUSE also in this environment. Students indeed will probably need to produce software in their future careers (even if not professionally), and the quality of their software may be crucial. In this work, we describe a didactic module in which the activities usually carried out in the existing study programme are exploited to introduce software engineering principles. The module does not shift the students’ attention from their main objectives and does not introduce additional lectures on software engineering topics. We describe the results of a first edition of the module that involved 17 students in a trilingual international high school. The results are promising and allow us to formulate hypotheses for further work, such as extending our approach to other activities and observe if and when students will develop a “software engineering mindset”, even without developing software.

The integration of Cloud and IoT (Internet-of-Things) resulting in so-called edge clouds has started. This requires the combination of data centre management technologies with much more constrained devices. Lightweight virtualisation solutions such as containerisation can be used to distribute, deploy and manage edge cloud applications on clusters. Leightweightness also applies to the devices, where we focus here on small-board devices such as Raspberry Pis in our concrete case. These small-board devices are particularly useful in situations where a mix of robustness due to environmental conditions and low costs is required. We discuss different architectural solutions for the distribution of computation to edge cloud devices based on containers and other management approaches and evaluate these in terms of cost, power consumption and performance.

Cloud service brokerage and related management and marketplace concepts have been identified as key concerns for future cloud technology development and research. Cloud service management is an important building block of cloud architectures that can be extended to act as a broker service layer between consumers and providers, and even to form marketplace services. We present a three-pronged classification and comparison framework for broker platforms and applications. A range of specific broker development concerns like architecture, programming and quality are investigated. Based on this framework, selected management, brokerage and marketplace solutions will be compared, not only to demonstrate the utility of the framework, but also to identify challenges and wider research objectives based on an identification of cloud broker architecture concerns and technical requirements for service brokerage solutions. We also discuss emerging cloud architecture concerns such as commoditisation and federation of integrated, vertical cloud stacks.

A cloud is a distributed Internet-based software system providing resources as tiered services. Through service-orientation and virtualization for resource provisioning, cloud applications can be deployed and managed dynamically. We discuss the building blocks of an architectural style for cloud-based software systems. We capture style-defining architectural principles and patterns for control-theoretic, model-based architectures for cloud software. While service orientation is agreed on in the form of service-oriented architecture and microservices, challenges resulting from multi-tiered, distributed and heterogeneous cloud architectures cause uncertainty that has not been sufficiently addressed. We define principles and patterns needed for effective development and operation of adaptive cloud-native systems.

A goal of cloud service management is to design self-adaptable auto-scaler to react to workload fluctuations and changing the resources assigned. The key problem is how and when to add/remove resources in order to meet agreed service-level agreements. Reducing application cost and guaranteeing service-level agreements (SLAs) are two critical factors of dynamic controller design. In this paper, we compare two dynamic learning strategies based on a fuzzy logic system, which learns and modifies fuzzy scaling rules at runtime. A self-adaptive fuzzy logic controller is combined with two reinforcement learning (RL) approaches: (i) Fuzzy SARSA learning FSL and (ii) Fuzzy Q-learning FQL. As an off-policy approach, Q-learning learns independent of the policy currently followed, whereas SARSA as an on-policy always incorporates the actual agent's behavior and leads to faster learning. Both approaches are implemented and compared in their advantages and disadvantages, here in the OpenStack cloud platform. We demonstrate that both auto-scaling approaches can handle various load traffic situations, sudden and periodic, and delivering resources on demand while reducing operating costs and preventing SLA violations. The experimental results demonstrate that FSL and FQL have acceptable performance in terms of adjusted number of virtual machine targeted to optimize SLA compliance and response time.

The domain-specific model-driven development requires effective and flexible techniques for implementing domain-specific rule generators. In this paper, we present a framework for rule generation through model translation with feature model, a high-level of the domain model to translate into low-level of rule language based on the paradigm of software reuse in terms of customisation and configuration with domain-specific rule strategies benefit mode-to-text translations. This framework is domain-specific where non-technical domain user can customise and configure the business process models. These compositions support two dimensional of translation modularity by using software product line engineering. The domain engineering is achieved by designing the domain and process model as a requirement space, it is also called template model, connecting with feature model through weaving model. The feature model is a high-level input model to customise the template model to an implementation. The application engineering is achieved by supporting the rule definition and configuring the generated rules. We discuss the development approach of the framework in a domain-specific environment; we present a case study in a Digital Content Technology (DCT) domain.

The business environment changes rapidly and needs to adapt to the enterprise business systems must be considered for new types of requirements to accept changes in the business strategies and processes. This raises new challenges that the traditional development approaches cannot always provide a complete solution in an efficient way. However, most of the current proposals for automatic generation are not devised to cope with rapid integration of the changes in the business requirement of end user (stakeholder’s and customer’s) resource. Domain-specific Rules constitute a key element for domain specific enterprise application, allowing configuration of changes, and management of the domain constraint within a domain. In this paper, we propose an approach to the development of an automatic generation of the domain-specific rules by using variability feature model and ontology definition of domain model concepts coming from Software product line engineering and Model Driven Architecture. We provide a process approach to generate a domain-specific rule based on the end user requirement.

Context: Processes are central to the operation of many systems or organizations. Process-centric systems, ranging from enterprise workflow systems to open distributed service compositions, have significantly increased in number and complexity. Objective: Designers of process-centric systems can benefit from process abstractions (including patterns) capturing and allowing the reuse of designs for frequent operational problems. Existing process patterns detection techniques have efficiency problems and difficulties to identify partial and inexact pattern instances. Method: We propose a process pattern detection technique based on a family of subgraph matching algorithms. The algorithms implement surjective graph morphism detection and a mechanism to incorporate semantic similarity computation for types and attributes of process graph elements. Results: Efficiency is addressed using simplified data structures, reducing the search space and its exploration. Match accuracy and time-complexity are demonstrated in an experimental study. Conclusions: Using process patterns allows business and technical processes to be provided as sharable service resources. Patterns can help to manage processes as configurable resources where a pattern can define a family of concrete customizable processes.

Abstract Cloud systems provide elastic execution environments of resources that link application and infrastructure/platform components, which are both exposed to uncertainties and change. Change appears in 2 forms: the evolution of architectural components under changing requirements and the adaptation of the infrastructure running applications. Cloud architecture continuity refers to the ability of a cloud system to change its architecture and maintain the validity of the goals that determine the architecture. Goal validity implies the satisfaction of goals in adapting or evolving systems. Architecture continuity aids technical sustainability, that is, the longevity of information, systems, and infrastructure and their adequate evolution with changing conditions. In a cloud setting that requires both steady alignment with technological evolution and availability, architecture continuity directly impacts economic sustainability. We investigate change models and change rules for managing change to support cloud architecture continuity. These models and rules define transformations of architectures to maintain system goals: Evolution is about unanticipated change of structural aspects of architectures, and adaptation is about anticipated change of architecture configurations. Both are driven by quality and cost, and both represent multidimensional decision problems under uncertainty. We have applied the models and rules for adaptation and evolution in research and industry consultancy projects.

Summary Many organizations migrate on‐premise software applications to the cloud. However, current coarse‐grained cloud migration solutions have made such migrations a non transparent task, an endeavor based on trial‐and‐error. This paper presents Variability‐based, Pattern‐driven Architecture Migration (V‐PAM), a migration method based on (i) a catalogue of fine‐grained service‐based cloud architecture migration patterns that target multi‐cloud, (ii) a situational migration process framework to guide pattern selection and composition, and (iii) a variability model to structure system migration into a coherent framework. The proposed migration patterns are based on empirical evidence from several migration projects, best practice for cloud architectures and a systematic literature review of existing research. Variability‐based, Pattern‐driven Architecture Migration allows an organization to (i) select appropriate migration patterns, (ii) compose them to define a migration plan, and (iii) extend them based on the identification of new patterns in new contexts. The patterns are at the core of our solution, embedded into a process model, with their selection governed by a variability model. Copyright © 2016 John Wiley & Sons, Ltd.

Requirements ontology offers a mechanism to map requirements for cloud computing services to cloud computing resources. Multiple stakeholders can capture and map knowledge in a flexible and efficient manner. The major contribution of the paper is the definition and development of an ontology for cloud computing requirements. The approach views each user requirement as a semantic intelligence task that maps and delivers it as cloud services. Requirements are modelled as tasks designed to meet specific requirements, problem domains that the requirements exist in, and problem-solving methods which are generic mechanisms to solve problems. A meta-ontology for cloud computing is developed and populated with ontology fragments on to which cloud computing requirements can be mapped. A critical analysis of the usage of ontologies in the requirements process is made and a case study is described that demonstrates the approach in a real-world application. The conclusion is that problem-solving ontologies provide a useful mechanism for the specification and reuse of requirements in the cloud computing environment.

Microservices have recently emerged as an architectural style, addressing how to build, manage, and evolve architectures out of small, self-contained units. Particularly in the cloud, the microservices architecture approach seems to be an ideal complementation of container technology at the PaaS level However, there is currently no secondary study to consolidate this research. We aim here to identify, taxonomically classify and systematically compare the existing research body on microservices and their application in the cloud. We have conducted a systematic mapping study of 21 selected studies, published over the last two years until end of 2015 since the emergence of the microservices pattern. We classified and compared the selected studies based on a characterization framework. This results in a discussion of the agreed and emerging concerns within the microservices architectural style, positioning it within a continuous development context, but also moving it closer to cloud and container technology.

Integration and mediation are two core functions that a cloud service broker needs to perform. The description\nof services involved plays a central role in this endeavour to enable services to be considered as commoditised\nutilities. We propose a conceptual framework for a cloud service broker based on two parts: a reference\narchitecture for cloud brokers and a service description template that describes the mediated and integrated\ncloud services. Structural aspects of that template will be identified, formalised in an ontology and mapped\nonto a set of sublanguages that can be aligned to the cloud development and deployment process.

Services are increasingly being hosted on cloud nodes to enhance their performance and increase their availability. The virtually unlimited availability of cloud resources enables service owners to consume resources without quantitative restrictions, paying only for what they use. To avoid cost overruns, resource consumption must be controlled and capped when necessary. We present a distributed tree-based protocol for managing quotas in clouds that minimizes communication overheads and reduces the time required to determine whether a quota has been exhausted. Experimental evaluation shows that our protocol reduces communication costs by 42% relative to a distributed baseline solution and is up to 15 times faster.

ABSTRACT Context Architecture‐centric software evolution (ACSE) enables changes in system's structure and behaviour while maintaining a global view of the software to address evolution‐centric trade‐offs. The existing research and practices for ACSE primarily focus ondesign‐time evolutionandruntime adaptationsto accommodate changing requirements in existing architectures. Objectives We aim toidentify, taxonomicallyclassifyand systematicallycomparethe existing research focused on enabling or enhancing change reuse to support ACSE. Method We conducted a systematic literature review of 32 qualitatively selected studies and taxonomically classified these studies based on solutions that enable (i)empirical acquisitionand (ii)systematic applicationof architecture evolution reuse knowledge (AERK) to guide ACSE. Results We identified six distinct research themes that support acquisition and application of AERK. We investigated (i)howevolution reuse knowledge is defined, classified and represented in the existing research to support ACSE and (ii)whatare the existing methods, techniques and solutions to support empirical acquisition and systematic application of AERK. Conclusions Change patterns(34% of selected studies) represent a predominant solution, followed byevolution styles(25%) andadaptation strategies and policies(22%) to enable application of reuse knowledge. Empirical methods for acquisition of reuse knowledge represent 19% includingpattern discovery,configuration analysis,evolution and maintenance predictiontechniques (approximately 6% each). A lack of focus on empirical acquisition of reuse knowledge suggests the need of solutions witharchitecture change miningas a complementary and integrated phase forarchitecture change execution. Copyright © 2014 John Wiley & Sons, Ltd.

Background--By leveraging cloud services, organizations can deploy their software systems over a pool of resources. However, organizations heavily depend on their business-critical systems, which have been developed over long periods. These legacy applications are usually deployed on-premise. In recent years, research in cloud migration has been carried out. However, there is no secondary study to consolidate this research. Objective--This paper aims to identify, taxonomically classify, and systematically compare existing research on cloud migration. Method--We conducted a systematic literature review (SLR) of 23 selected studies, published from 2010 to 2013. We classified and compared the selected studies based on a characterization framework that we also introduce in this paper. Results--The research synthesis results in a knowledge base of current solutions for legacy-to-cloud migration. This review also identifies research gaps and directions for future research. Conclusion--This review reveals that cloud migration research is still in early stages of maturity, but is advancing. It identifies the needs for a migration framework to help improving the maturity level and consequently trust into cloud migration. This review shows a lack of tool support to automate migration tasks. This study also identifies needs for architectural adaptation and self-adaptive cloud-enabled systems.

Software systems are subject to change. To embrace change, the systems should be equipped with automated mechanisms. Business process and software architecture models are two artifacts that are subject to change in an interrelated manner that requires them co-evolve. As opposed to the traditional batch-based model transformation, we propose a comprehensive set of structural and behavioral evolution patterns that enable to incrementally reflect the impact of change of business processes to their associated architecture models by applying reusable patterns. A basis for automation is provided through a graph-based formalism.

Service-based architectures implement business processes as technical software services to develop enterprise software.As a consequence of frequent business and technical change cycles, the architect requires a reusecentered approach to systematically accommodate recurring changes in existing software.Our 'Pat-Evol' project aims at supporting pattern-driven reuse in architecture-centric evolution for service software.We propose architecture change mining as a complementary phase to a systematic architecture change execution.Therefore, we investigate the 'history' of sequential changes -exploiting change logs -to discover patterns of change that occur during evolution.To foster reuse, a pattern catalogue maintains an updated collection with once-off specification for identified pattern instances.This allows us to exploit change pattern as a generic, first class abstractions (that can be operationalised and parameterised) to support reuse in architecture-centric software evolution.The notion of 'build-once, use-often' empowers the role of an architect to model and execute generic and potentially reusable solution to recurring architecture evolution problems.

Autonomic, i.e. dynamic and fault-tolerant Web service composition is a requirement resulting from recent developments such as on-demand services. In the context of planning-based service composition, multiagent planning and dynamic error handling are still unresolved problems. Recently, business rule and constraint management has been looked at for enterprise SOA to add business flexibility. This paper proposes a constraint integration and violation handling technique for dynamic service composition. Higher degrees of reliability and fault-tolerance, but also performance for autonomously composed WS-BPEL processes are the objectives.

Service-oriented architecture (SOA) is currently the predominant software integration framework. Web services provide the predominant platform for SOA. SOA as an integration architecture solution supports a range of application scenarios. We present a solution for the integration of business information systems based on SOA and Web services. We discuss maintainability requirements in relation to information-specific integration and architecture aspects. A template-based approach based on modular and declarative transformation rules and architectural styles and patterns defines our solution to the maintainability problem of integration architectures.

Active learning and training is a particularly effective form of education. In various domains, skills are equally important to knowledge. We present an automated learning and skills training system for a database programming environment that promotes procedural knowledge acquisition and skills training. The system provides meaningful knowledge-level feedback such as correction of student solutions and personalized guidance through recommendations. Specifically, we address automated synchronous feedback and recommendations based on personalized performance assessment. At the core of the tutoring system is a pattern-based error classification and correction component that analyzes student input in order to provide immediate feedback and in order to diagnose student weaknesses and suggest further study material. A syntax-driven approach based on grammars and syntax trees provides the solution for a semantic analysis technique. Syntax tree abstractions and comparison techniques based on equivalence rules and pattern matching are specific approaches.

The process notion is central in computing. Business processes and workflow processes are essential elements of software systems implementations. Processes are connected to notions of interaction and composition. The Web Services Framework as a development and deployment platform for services is based on the assembly of interacting processes as the compositional paradigm. Service-based software development on and for the Web platform embracing the philosophy of discovering and using third-party services makes a shared knowledge representation framework necessary. We develop a semantical and ontological framework for service process composition. We propose a framework for the compositional definition of Web services based on the π-calculus to define protocol-like restrictions on service interactions and based on description logic and ontologies to guide the discovery and modelling of services and processes.

Enterprise systems are frequently built by combining a number of discrete Web services together, a process termed composition. There are a number of architectural configurations or distribution patterns, which express how a composed system is to be deployed. Previously, we presented a model driven architecture using UML 2.0, which took existing service interfaces as its input and generated an executable Web service composition, guided by a distribution pattern model. In this paper, we propose using Web service semantic descriptions in addition to Web service interfaces, to assist in the semi-automatic generation of the distribution pattern model. Web services described using semantic languages, such as OWLS, can be automatically assessed for compatibility and their input and output messages can be mapped to each other

The teaching/ learning of cognitive skills, such as problem-solving, is an important goal in most forms of education. In well-structured subject areas certain exercise problem types may be precisely described by means of machine-processable knowledge structures or ontologies. These ontologies can readily be used to generate individual problem examples for the student, where each problem consists of a question and its solution. An example is given from the subject domain of computer databases.

Web service compositions are often used to realise service-based enterpriseapplications. These enterprise systems are built from many existing discreteapplications, often legacy applications exposed using Web service interfaces.Acceptance of these systems is often constrained by non-functional aspects,such as Quality of Service (QoS). A number of factors affect the QoS of anenterprise system, including availability, scalability and performance. Thereare a number of architectural configurations or distribution patterns, whichexpress how a composed system is to be deployed. These distribution patternshave a direct impact upon the QoS of the composition. However, the amount ofcode required to realise these distribution patterns is considerable. Additionally, there is an increased deployment time associated with setting updifferent distribution patterns. We therefore propose a novel approach whichcombines a Model Driven Architecture using UML 2.0 for modeling andsubsequently generating Web service compositions, with a method for achieving dynamic decentralised interaction amongst services with reduced deployment overheads. These approaches combined provide for the generation of dynamic Web service compositions driven by a distribution pattern model.

Increasingly, distributed systems are being constructed by composing a number of components, often legacy applications exposed using Web service interfaces. There are a number of architectural configurations or distribution patterns, which express how such systems are to be deployed. However, the amount of code required to realise these distribution patterns is considerable. Here, we propose a novel model driven architecture using UML 2.0, which takes existing Web service interfaces as its input and generates an executable Web service composition, based on a distribution pattern chosen by the software architect

Universities are increasingly offering courses online. Feedback, assessment, and guidance are important features of this online courseware. Together, in the absence of a human tutor, they aid the student in the learning process. We present a programming training environment for a database course. It aims to offer a substitute for classroom based learning by providing synchronous automated feedback to the student, along with guidance based on a personalized assessment. The automated tutoring system should promote procedural knowledge acquisition and skills training. An automated tutoring feature is an integral part of this tutoring system.

The learning objectives of a database course include aspects from conceptual and theoretical knowledge to practical development and implementation skills. We present an interactive educational multimedia system based on the virtual apprenticeship model for the knowledge- and skills-oriented Web-based education of database course students. Combining knowledge learning and skills training in an integrated environment is a central aspect of our system. We show that tool-mediated independent learning and training in an authentic setting is an alternative to traditional classroom-based approaches.

Teaching database engineers involves a variety of learning activities. A strong focus is on practical problems that go beyond the acquisition of knowledge. Skills and experience are equally important. We propose a virtual apprenticeship model for the knowledge- and skills-oriented Web-based education of database students. We adapt the classical cognitive apprenticeship theory to the Web context utilising scaffolding and activity theory. The choice of educational media and the forms of student interaction with the media are central success criteria.

Interactivity is a central element in teaching and learning. The Web as a new educational platform enables new forms of teaching and learning. The consequence for the Web - a constantly evolving environment that provides less direct feedback - is the importance of evaluation. In particular the learners' interaction with the Web-based multimedia features is central. We propose Web usage mining, a technology that has already been used to analyse e-commerce Web sites, in conjunction with an analytic model as the evaluation approach.

Web Services are software services that can be advertised by providers and deployed by customers using Web technologies. This concept is currently carried further to address Web service choreography. Choreography refers to the composition of individual services to services processes that can communicate and interact with another.We propose a formal ontology framework for these Web service processes that supports the description, matching, and composition through logic reasoning techniques. The Semantic Web, based on a description logic-based knowledge representation and reasoning framework, provides the foundations. Integrating aspects from modal logics and process calculi into this framework will prove essential.

Web based virtual courses focussing on content delivery only, have turned out not to be as successful as expected. We investigate reasons and suggest some remedies, using our own virtual database course for illustration. Interactivity and integration are the two main remedies, which will lead towards improved, learning oriented virtual course environments. We introduce a formalism to support their development and analysis.

The prototyping language ProSET (Prototyping with Sets) for experimental and evolutionary prototyping is discussed, with emphasis on algorithm design. Its features include generative communication, flexible exception handling, and the integration of persistence. Issues pertaining to the compiler and the programming environment are discussed.>