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.

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.

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.

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.

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 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).

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.

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.

In 2013, ACM recognized Computational Thinking (CT) as “one of the fundamental skills desired of all graduates”. This means that, especially in liberal education environments, one of the challenges of CT courses is to motivate students who are discouraged upfront as they perceive programming as a difficult task. Applications that have tangible results typically stimulate students’ interests. For instance, Educational Robotics (ER) is recognized as a tool to enhance higher order thinking skills and to facilitate teamwork. In this paper, we describe a course that has been designed to use ER (i.e., programming a maze-solving robot) to foster CT. Each activity of the course has been designed to foster specific CT skills and to contribute to CT assessment, which remains a challenge in CT research. We report the results of an experiment, in a liberal education environment, with a total of 13 ninth graders (15.4% M, 84.6% F).

Computational Thinking (CT) has been recognized as one of the fundamental skills that all graduates should acquire. For this reason, motivational concerns need to be addressed at an early age of a child, and reaching students who do not consider themselves candidates for science, technology, engineering, and mathematics disciplines is important as well if the broadest audience possible is to be engaged. This article describes a framework for teaching and assessing CT in the context of K-12 education. The framework is based on Agile software engineering methods, which rely on a set of principles and practices that can be mapped to the activities of CT. The article presents as well the results of an experiment applying this framework in two sixth-grade classes, with 42 participants in total. The results show that Agile software engineering methods are effective at teaching CT in middle schools, after the addition of some tasks to allow students to explore, project, and experience the potential product before using the software tools at hand. Moreover, according to the teachers’ feedback, the students reached all the educational objectives of the topics involved in the multidisciplinary activities. This result can be taken as an indicator that it is possible to use computing as a medium for teaching other subjects, besides computer science.

The capabilities and applications of new drones require a high demand of power that traditional batteries cannot sustain, triggering the need of developing strategies to promote efficient energy usage on drone platforms.However, to reduce the energy consumption it is required to have reliable means to measure the device's behavior and its relationship to the battery discharge.This paper introduces Green Flight, a software system that acquires data during a drone mission, featuring an online battery discharge analyzer and a real-time suggestions mode to help to optimize the overall power consumption of the drone, allowing for longer, more autonomous missions.

In today's ever increasing demand on loosely coupled systems, software providers need to produce solutions that are flexible enough to be configured at runtime, yet maintaining high quality. One way to address the quality of such systems is through testing. Software testing provides software providers and their clients with techniques to characterize the internal and external quality of their systems, by specifying test cases to check how systems react under different circumstances. In the context of web services the situation changes, software providers become service providers. In other words, they do not provide a physical software to their clients, instead they allow them to invoke remote code from their system. This change affects the way we look at testing itself. In the web service paradigm clients do not own services, instead they invoke them. Different approaches for testing web services have been reported in the literature, with different techniques and using different input information to generate test cases. Considering the topic of web services testing, as a subject for this systematic review is a challenging undertaking, mainly due to the extensive amount of literature in the subject. To narrow it, we have focused on one single particularity that affects dramatically the testing process. That is the specifications used to generate test cases. To this end, in this paper we present a systematic review of the specifications used for testing web services. The outcome of this study answers key questions involved in the advantages and disadvantages of the existing specifications as well as the potential improvements that could lead to more robust web services testing process.

Web Services (WS) are software components accessible over the Internet through a well-defined set of standards. When consumers invoke a service, they expect to receive a valid response. However, the problem is to determine the structure of a valid request [21]. WS specifications are used to solve this problem since they are considered the primary piece of information for building service requests. Unfortunately, existing specifications do not provide enough support for this type information (e.g., WSDL) or there is little support on the client side (e.g., OWL-S). In this paper we address this issue by implementing a technique to reduce the number of faulty requests. We specifically propose an approach for extending WSDL with service input parameters rules that help consumers and integrators to verify their calls on the client side.

In modern software solutions vendors are racing to keep pace with their customers' needs for more and more distributed systems enable to enhance collaboration and reduce their time to market. Service Oriented Architecture (SOA) is a promising software methodology that addresses this problem. SOA advocates the integration of loosely coupled systems across platforms. SOA adds a new level of abstraction to the existing stack of technologies and development methodologies, however, it adds also new challenges. The most popular way to implement SOA is using Web Services (WS). WS is an XML technology to integrate loosely coupled systems. The challenge facing WS is testing. From the client prospective, a Web Service is a black box that needs to be tested before being used, mainly to confirm the claims of services' providers about the quality of their services. By calling a WS, we delegate part of our business logic to an external provider to do it for us. Thus, we have no control of what could happen during the execution of that part of the business logic. To this end, many testing approaches and techniques have been proposed in the literature to address various aspects related to WS testing. In our effort to improve WS testing infrastructures, we propose a framework for service integrators to collaborate during Web Services testing by making test suites open to the public and share testing results.

The advance in web services (WS) and service oriented architecture (SOA) applications has led to the development of new types of systems in which different heterogeneous components and platforms can connect and collaborate to solve a business problem. This advancement adds a new level of abstraction to the existing stack of technologies and development methodologies, but it adds also new challenges. One such a big challenge is testing. From the client prospective, a Web Service is a black box that needs to be tested before being used, mainly to confirm the claims of services' providers about the quality of their services. By calling a WS, we delegate part of our business logic to an external provider to do it for us. Thus, we have no control of what could happen during the execution of that part of the business logic. To this end, many testing approaches and techniques have been proposed in the literature to address various aspects related to WS testing. In our effort to improve WS testing infrastructures, we propose a framework for service integrators to collaborate during Web Services testing by making test suites open to the public and share testing results.