Context-Aware Digital Twins to Support Software Management at the Edge

With millions of connected edge gateways, there is a pressing challenge of remote maintenance of containerised software components after the initial release. To support remote update operations, edge software providers have been increasingly adopting digital twin-based device management platforms for run-time monitoring and interaction. A common limitation of these solutions is the lack of support for modelling the multi-dimensional context of edge devices deployed in the field, which hinders the software management in a tailored and context-aware manner. This paper aims to address this lack of context-awareness in digital twins required for edge software assignment by introducing two modelling principles, which allow focusing on the device fleet as a whole and capturing the diverse cyber-physical-social context of individual devices. As part of proof of concept, these principles were incorporated in an existing digital twin platform. This prototype implementation demonstrates the viability of the proposed modelling principles via a running example in the context of a telemedicine application system.acceptedVersio

EXCLAIM framework: a monitoring and analysis framework to support self-governance in Cloud Application Platforms

The Platform-as-a-Service segment of Cloud Computing has been steadily growing over the past several years, with more and more software developers opting for cloud platforms as convenient ecosystems for developing, deploying, testing and maintaining their software. Such cloud platforms also play an important role in delivering an easily-accessible Internet of Services. They provide rich support for software development, and, following the principles of Service-Oriented Computing, offer their subscribers a wide selection of pre-existing, reliable and reusable basic services, available through a common platform marketplace and ready to be seamlessly integrated into users' applications. Such cloud ecosystems are becoming increasingly dynamic and complex, and one of the major challenges faced by cloud providers is to develop appropriate scalable and extensible mechanisms for governance and control based on run-time monitoring and analysis of (extreme amounts of) raw heterogeneous data. In this thesis we address this important research question -- \textbf{how can we support self-governance in cloud platforms delivering the Internet of Services in the presence of large amounts of heterogeneous and rapidly changing data?} To address this research question and demonstrate our approach, we have created the Extensible Cloud Monitoring and Analysis (EXCLAIM) framework for service-based cloud platforms. The main idea underpinning our approach is to encode monitored heterogeneous data using Semantic Web languages, which then enables us to integrate these semantically enriched observation streams with static ontological knowledge and to apply intelligent reasoning. This has allowed us to create an extensible, modular, and declaratively defined architecture for performing run-time data monitoring and analysis with a view to detecting critical situations within cloud platforms. By addressing the main research question, our approach contributes to the domain of Cloud Computing, and in particular to the area of autonomic and self-managing capabilities of service-based cloud platforms. Our main contributions include the approach itself, which allows monitoring and analysing heterogeneous data in an extensible and scalable manner, the prototype of the EXCLAIM framework, and the Cloud Sensor Ontology. Our research also contributes to the state of the art in Software Engineering by demonstrating how existing techniques from several fields (i.e., Autonomic Computing, Service-Oriented Computing, Stream Processing, Semantic Sensor Web, and Big Data) can be combined in a novel way to create an extensible, scalable, modular, and declaratively defined monitoring and analysis solution

Towards a Sustainable IoT with Last-Mile Software Deployment

Billions of sensor-enabled IoT devices generate extreme amounts of e- waste. Because of the low cost and short lifespan of electronic components, it is often more convenient for consumers to buy a new device instead of re-using or re-purposing the old one. With the increased computing and connectivity capabilities, IoT devices can already receive code updates for new purposes (and thus extend their lifespan), but the cost of such operations often exceeds the price of device replacement due to constrained resources, hindered network connectivity, and distributed placement. This paper describes how these existing capabilities can enable last-mile software deployment at scale. We propose a hierarchical architecture for provisioning software updates from the cloud to terminal devices via edge gateways in a scalable and targeted manner. By enabling such an end-to-end software deployment architecture, the approach promotes hardware re-use via re-purposing and thus contributes to the creation of a more sustainable IoT.acceptedVersio

Towards a Model-Based Serverless Platform for the Cloud-Edge-IoT Continuum

One of the most prominent implementations of the serverless programming model is Function-as-a-Service (FaaS). Using FaaS, application developers provide source code of serverless functions, typically describing only parts of a larger application, and define triggers for executing these functions on infrastructure components managed by the FaaS provider. There are still challenges that hinder the wider adoption of the FaaS model across the whole Cloud-Edge-IoT continuum. These include the high heterogeneity of the Edge and IoT infrastructure, vendor lock-in, the need to deploy and adapt serverless functions as well as their supporting services and software stacks into their cyber-physical execution environment. As a first step towards addressing these challenges, we introduce the SERVERLEss4I0T platform for the design, deployment, and maintenance of applications over the Cloud-Edge-IoT continuum. In particular, our platform enables the specification and deployment of serverless functions on Cloud and Edge resources, as well as the deployment of their supporting services and software stacks over the whole Cloud-Edge-IoT continuum.acceptedVersio

Towards MLOps in Mobile Development with a Plug-in Architecture for Data Analytics

Smartphones are increasingly used as universal IoT gateways collecting data from connected sensors in a wide range of industrial applications. With the increasing computing capabilities, they are used not just for simple data aggregation and transferring, but have now become capable of performing advanced data analytics. As AI has become a key element in enterprise software systems, many software development teams rely on dedicated Machine Learning (ML) engineers who often follow agile development practices in their work. However, in the context of mobile app development, there is still limited tooling support for MLOps, mainly due to unsuitability of native programming languages such as Java and Kotlin to support ML-related programming tasks. This paper aims to address this gap and describes a plug-in architecture for developing, deploying and running ML modules for data analytics on the Android platform. The proposed approach advocates for modularity, extensibility, customisation, and separation of concerns, allowing ML engineers to develop their components independently from the main application in an agile and incremental manner.acceptedVersio

Model-based Continuous Deployment of SIS

This chapter is organized as follows. Section 4.2 provides an overview of the current state of the art and of the practice for the automatic deployment of SIS. Section 4.3 introduces our solutions for the automatic deployment of SIS, first describing how they can be integrated in order to form a coherent deployment bundle and then detailing each our two enablers: GENESIS and DivENACT. Section 4.4 focus on the support offered by our solutions to ensure the trustworthiness deployment of SIS. Finally, Section 4.5 draws some conclusions.publishedVersio

Model-based fleet deployment in the IoT–edge–cloud continuum

With the increasing computing and networking capabilities, IoT devices and edge gateways have become part of a larger IoT–edge–cloud computing continuum, where processing and storage tasks are distributed across the whole network hierarchy, not concentrated only in the cloud. At the same time, this also introduced continuous delivery practices to the development of software components for network-connected gateways and sensing/actuating nodes. These devices are placed on end users’ premises and are characterized by continuously changing cyber-physical contexts, forcing software developers to maintain multiple application versions and frequently redeploy them on a distributed fleet of devices with respect to their current contexts. Doing this correctly and efficiently goes beyond manual capabilities and requires an intelligent and reliable automated solution. This paper describes a model-based approach to automatically assigning multiple software deployment plans to hundreds of edge gateways and connected IoT devices implemented in collaboration with a smart healthcare application provider. From a platform-specific model of an existing edge computing platform, we extract a platform-independent model that describes a list of target devices and a pool of available deployment plans. Next, we use constraint solving to automatically assign deployment plans to devices at once with respect to their specific contexts. The result is transformed back into the platform-specific model and includes a suitable deployment plan for each device, which is then consumed by our engine to deploy software components not only on edge gateways but also on their downstream IoT devices with constrained resources and connectivity. We validate the approach with a fleet deployment prototype integrated into a DevOps toolchain used by the partner application provider. Initial experiments demonstrate the viability of the approach and its usefulness in supporting DevOps for edge and IoT software development.publishedVersio

Machine Learning for Fatigue Detection using Fitbit Fitness Trackers

Fatigue can be a pre-cursor to many illnesses and injuries, and cause fatal work-related incidents. Fatigue detection has been traditionally performed in lab conditions with stationary medical-grade diagnostics equipment for electroencephalography making it impractical for many in-field scenarios. More recently, the ubiquitous use of wearable sensor-enabled technologies in sports, everyday life or fieldwork has enabled collecting large amounts of physiological information. According to recent studies, the collected biomarkers related to sleep, physical activity or heart rate have proven to be in correlation with fatigue, making it a natural fit for applying automated data analysis using Machine Learning. Accordingly, this paper presents our novel Machine Learning-driven approach to fatigue detection using biomarkers collected by general-purpose wearable fitness trackers. The developed method can successfully predict fatigue symptoms among target users, and the overall methodology can be further extended to other diagnostics scenarios which rely on collected wearable data.acceptedVersio

Function-as-a-Service for the Cloud-to-Thing Continuum: A Systematic Mapping Study

Until recently, Internet of Things applications were mainly seen as a means to gather sensor data for further processing in the Cloud. Nowadays, with the advent of Edge and Fog Computing, digital services are dragged closer to the physical world, with data processing and storage tasks distributed across the whole Cloud-to-Thing continuum. Function-as-a-Service (FaaS) is gaining momentum as one of the promising programming models for such digital services. This work investigates the current research landscape of applying FaaS over the Cloud-to-Thing continuum. In particular, we investigate the support offered by existing FaaS platforms for the deployment, placement, orchestration, and execution of functions across the whole continuum using the Systematic Mapping Study methodology. We selected 33 primary studies and analyzed their data, bringing a broad view on the current research landscape in the area.acceptedVersio

Data Processing in Cyber-Physical-Social Systems Through Edge Computing

Cloud and Fog computing have established a convenient and widely adopted approach for computation offloading, where raw data generated by edge devices in the Internet of Things (IoT) context is collected and processed remotely. This vertical offloading pattern, however, typically does not take into account increasingly pressing time constraints of the emerging IoT scenarios, in which numerous data sources, including human agents (i.e., Social IoT), continuously generate large amounts of data to be processed in a timely manner. Big data solutions could be applied in this respect, provided that networking issues and limitations related to connectivity of edge devices are properly addressed. Although edge devices are traditionally considered to be resource-constrained, main limitations refer to energy, networking, and memory capacities, whereas their ever-growing processing capabilities are already sufficient to be effectively involved in actual (big data) processing. In this context, the role of human agents is no longer limited to passive data generation, but can also include their voluntary involvement in relatively complex computations. This way, users can share their personal computational resources (i.e., mobile phones) to support collaborative data processing, thereby turning the existing IoT into a global cyber-physical-social system (CPSS). To this extent, this paper proposes a novel IoT/CPSS data processing pattern based on the stream processing technology, aiming to distribute the workload among a cluster of edge devices, involving mobile nodes shared by contributors on a voluntary basis, and paving the way for cluster computing at the edge. Experiments on an intelligent surveillance system deployed on an edge device cluster demonstrate the feasibility of the proposed approach, illustrating how its distributed in-memory data processing architecture can be effective