CPILS is a sustainable education system consisting of a private cloud core and peripherals for the interaction with users and the environment. It is aimed at upskilling students on societally relevant topics as well as technology to overcome uncertainties and achieve autarky. Currently, two instances of CPILS are deployed in universities as immersive learning spaces for mixed-discipline students. In this paper, we present the adaptive software design and technology decisions behind CPILS and demonstrate the resulting implementation.

WebAssembly is a lightweight binary format that achieves portable software across heterogeneous compute nodes in the computing continuum. One of its main drawbacks is the limited availability of pre-packaged software, in comparison to larger ecosystems, as it is instead the case with container registries or programming language package repositories. A transpilation approach might close this gap. However, it requires overcoming several technical issue dealing with native code execution. This work assesses the current transpilation technologies and the reduction of deployment friction. We report about the state of technology and contribute three advancements: (i) a compilation pipeline for Python applications using componentize-py and wasi-wheels, enabling native extension support; (ii) exploitation of hardware accelerators in Wasm via WASI-nn, and (iii) seamless cross-language composition using WebAssembly Interface Types (WIT). We demonstrate the effectiveness of our result with two real-world applications in a 3-zones continuum, highlighting faster development without compromising software dependability. We support experimental reproducibility and release our prototype and dataset as open-source.

Since 2015, as part of a Reproducibility Initiative, the International Conference for High Performance Computing, Networking, Storage, and Analysis (SC) has encouraged, and more recently required, all technical papers to include an Artifact Description (AD) Appendix. Each year, the conference selects one accepted paper to form the basis of a Reproducibility Challenge in the Student Cluster Competition (SCC) - a mainstay in the conference program - for the following year. In the SCC, teams of undergraduate students partner with their educational institution and vendors to build, benchmark and operate a small HPC system during the conference week. The Reproducibility Challenge component has them attempt to reproduce parts of the selected work on their own cluster. At SC22, teams worked to reproduce the SC21 paper “Productivity, Portability, Performance: Data-Centric Python” by Ziogas et al. This special section includes a selection of reports from student teams, along with an extended version of that paper, discussing the students’ results...

Societies and legislations are moving towards automated decision-making based on measured data in safety-critical environments. Over the next years, density and frequency of measurements will increase to generate more insights and get a more solid basis for decisions, including through redundant low-cost sensor deployments. The resulting data characteristics lead to large-scale system design in which small input data errors may lead to severe cascading problems including ultimately wrong decisions. To ensure internal data consistency to mitigate this risk in such IoT environments, fast-paced data fusion and consensus among redundant measurements need to be achieved. In this context, we introduce history-aware sensor fusion powered by accurate voting with clustering as a promising approach to achieve fast and informed consensus, which can converge to the output up to 4X faster than the state of the art history-based voting. Leveraging three case studies, we investigate different voting schemes and show how this approach can improve data accuracy by up to 30% and performance by up to 12% compared to state-of-the-art sensor fusion approaches. We furthermore contribute a specification format for easily deploying our methods in practice and use it to develop a pilot implementation.

Continuum computing promises the abstraction of physical node location and node platform stack in order to create a seamless application deployment and execution across edges and cloud data centres. For industrial IoT applications, the demand to generate data insights in conjunction with an installed base of increasingly capable edge devices is calling for appropriate continuum computing interfaces. Derived from a case study in industrial water flow monitoring and based on the industry’s de-facto standard Kubernetes to deploy complex containerised workloads, we present an appropriate continuum deployment mechanism based on custom Kubernetes controllers and CI/CD, called Kontinuum Controller. Through synthetic experiments and a holistic cross-provider deployment, we investigate its scalability with emphasis on reconciling adjusted configuration per application and per node, a critical requirement by industrial customers. Our findings convey that Kubernetes by default would enter undesirable oscillation already for modestly sized deployments. Thus, we also discuss possible solutions.

Autonomic decision-making based on rules and metrics is inevitably on the rise in distributed software systems. Often, the metrics are acquired from system observations such as static checks and runtime traces. To avoid bias propagation and hence reduce wrong decisions in increasingly autonomous systems due to poor observation data quality, multiple independent observers can exchange their findings and produce a majority-accepted, complete and outlier-cleaned ground truth in the form of consensus-supported metrics. In this work, we motivate the growing importance of metrics for informed and autonomic decisions in clouds and other distributed systems, present reasons for diverging observations, and describe a federated approach to produce ground truth with data-centric consensus voting for more reliable decision making processes. We validate the system design with experiments in the area of cloud software artefact observations and highlight benefits for reproducible distributed system behaviour.

Fully Individualised Programmable Exams (FIPEs) are physically or digitally written examinations in which the task descriptions are different for all students according to programmatically controlled variability. FIPEs reduce the risk of collaborative cheating while adhering to institutional equality policies by controlling how many differences are introduced. The individualisations are based on permutations, deviations, randomisations and sampling. They allow for meeting legal constraints and yet gaining the desired ability to further automate solutions checking. This paper contributes a software solution to address the need from exam specification to distribution. It introduces a categorisation for FIPEs, presents a working implementation to generate and disseminate exam documents and delivers an experience report in two curriculums, computer science and engineering sciences.

Programming education has become a mandatory element of many engineering curriculums, covering skills from digitally controlled mechanical processes to intelligent traffic and aviation systems. Many of these disciplines require the interaction with physical devices as programming interfaces. Higher education institutions focusing on quality presence labs with the need for occasional online teaching are thus looking for blended and multimodal solutions in which the physical interaction can be carried over as much as possible into the digital channels. In these solutions, various touch points between the physical and digital worlds should be exploited. This paper contributes such a solution. It introduces a cyber-physical educational support structure called EPOSS aimed at programming ’things’, including robots and derivative stationary and mobile units, that works in flexible lab and online teaching combinations. The system integrates domain-specific scenarios and open data sources for realistic autoprogramming simulations and is made available as open source prototype to foster adoption. The usefulness of the support system is demonstrated with traffic engineering education scenarios.

Tags and labels are annotations on resources in many commercial public cloud models. Little is known about the extent of tagging in commercially relevant settings and there is an absence of automated software to handle tags. We show that by introducing an extensible tag management middleware based on cloud functions, tags can be turned into a powerful declarative means of cloud management. Our universal connector middleware is demonstrated by a typical deployment administration scenario involving both AWS and Google Cloud Platform services.

Modern software applications following cloud-native design principles and architecture guidelines have inherent advantages in fulfilling current user requirements when executed in complex scheduled environments. Engineers responsible for software applications therefore have an intrinsic interest to migrate to cloud-native architectures. Existing methodologies for transforming legacy applications do not yet consider migration from partly cloud-enabled and cloud-aware applications under continuous development. This work thus introduces a co-transformation methodology and validates it through the migration of a prototypical music identification and royalty collection application. Experimental results demonstrate that the proposed methodology is capable to effectively guide a transformation process, resulting in elastic and resilient cloud-native applications. Findings include the necessity to maintain application self-management even on modern cloud platforms.

With the growth and evolution of cloud applications, more and more architectures use hybrid cloud bindings to optimally use virtual resources regarding pricing policies and performance. This process has led to the creation of multi-cloud management platforms as well as abstraction libraries. At the moment, many (multi-)cloud management platforms (CMPs) are designed to cover the functional requirements. Along with growing adoption and industrial impact of such solutions, there is a need for a comparison and test environment which automatically assesses and compares existing platforms and helps in choosing the optimal one. This paper focuses on the creation of a suitable testbed concept and an actual extensible software prototype which makes multi-cloud experiments repeatable and reusable by other researchers. The work is evaluated by an exemplary comparison of 4 CMPs bound to AWS, showcasing standardised output formats and evaluation criteria.

The cloud computing paradigm is one of the most promising of its kind. The demand and supply of online delivered software have been continuously growing. However, reports have shown that various Software as a service solutions have not succeeded in reaching cloud user expectations. Hence, users need to be satisfied with the service quality which is determined by the fulfillment of both functional and non-functional requirements. Currently, the evaluation of the cloud service quality consider-ing individual requirements is up to the user. Unfortunately, the amount of non-functional quality criteria is very high and some are laborious to determine. We believe that an information system could support and enable users without specific knowledge to evaluate comprehensively and rapidly the quality of their cloud. In this paper we first present a quality model for SaaS. In addition, we demonstrate how the quality information should be structured and visualized in a user-friendly way. With the provided transparency users are able to establish quality awareness in the long term.

We introduce and demonstrate NubiVis, a web-based tool for exploring and managing personal files stored in arbitrary locations with a unified view. NubiVis supports views on files partitioned and dispersed across several storage services when running atop the NubiSave cloud storage controller, as we

Distributed data storage is a topic of growing importance due to the mounting pressure to find the right balance between capacity, cost, privacy and other non-functional properties. Compared to central storage on physical media, on the network or in a cloud storage service, advanced data distribution techniques offer additional safety, security and performance. On the downside, these advantages come with a much higher complexity regarding the choice and configuration of where to store which parts of the data, and subsequent verification of where which data had been stored. Often, the storage targets must be configured individually while a centrally and locally accessible configuration interface with an appropriate propagation and verification mechanism would be more suitable. The complexity is further increased by additional data pre-processing tasks which are selectively applied to some of the targets. Compression, encryption and deduplication are typically present in pre-processing. With Storage Flows, we propose a new concept to manage distributed storage flows through systematic orchestration. The flows connect clients flexibly with intermediate data pre-processing tasks and finally the storage targets. We show that Storage Flows can be formalised and demonstrate their practical usefulness with implemented configuration and verification tools.

Service level agreements (SLAs) are used to manifest guarantees about certain functional and non-functional aspects of service execution. Service providers are confronted with a hard problem when trying to estimate reasonable QoS levels and other default settings for SLA templates. The insufficient use of formal service behavior descriptions, varying resource demands and a choice of configuration options expected by users contribute to this issue. We present our solution of gathering monitoring data at runtime and feeding it back into the service registry to adjust descriptions and make contract template derivation a more realistic process. In addition, we show how to extend SOA building blocks such as service discoveries and SLA managers with the adjustment mechanism.