Towards Energy Consumption and Carbon Footprint Testing for AI-driven IoT Services

Energy consumption and carbon emissions are expected to be crucial factors for Internet of Things (IoT) applications. Both the scale and the geo-distribution keep increasing, while Artificial Intelligence (AI) further penetrates the "edge" in order to satisfy the need for highly-responsive and intelligent services. To date, several edge/fog emulators are catering for IoT testing by supporting the deployment and execution of AI-driven IoT services in consolidated test environments. These tools enable the configuration of infrastructures so that they closely resemble edge devices and IoT networks. However, energy consumption and carbon emissions estimations during the testing of AI services are still missing from the current state of IoT testing suites. This study highlights important questions that developers of AI-driven IoT services are in need of answers, along with a set of observations and challenges, aiming to help researchers designing IoT testing and benchmarking suites to cater to user needs.Comment: Presented at the 2nd International Workshop on Testing Distributed Internet of Things Systems (TDIS 2022

A self-integration testbed for decentralized socio-technical systems

The Internet of Things (IoT) comes along with new challenges for experimenting, testing, and operating decentralized socio-technical systems at large-scale. In such systems, autonomous agents interact locally with their users, and remotely with other agents to make intelligent collective choices. Via these interactions they self-regulate the consumption and production of distributed (common) resources, e.g., self-management of traffic flows and power demand in Smart Cities. While such complex systems are often deployed and operated using centralized computing infrastructures, the socio-technical nature of these decentralized systems requires new value-sensitive design paradigms; empowering trust, transparency, and alignment with citizens’ social values, such as privacy preservation, autonomy, and fairness among citizens’ choices. Currently, instruments and tools to study such systems and guide the prototyping process from simulation, to live deployment, and ultimately to a robust operation of a high Technology Readiness Level (TRL) are missing, or not practical in this distributed socio-technical context. This paper bridges this gap by introducing a novel testbed architecture for decentralized socio-technical systems running on IoT. This new architecture is designed for a seamless reusability of (i) application-independent decentralized services by an IoT application, and (ii) different IoT applications by the same decentralized service. This dual self-integration promises IoT applications that are simpler to prototype, and can interoperate with decentralized services during runtime to self-integrate more complex functionality, e.g., data analytics, distributed artificial intelligence. Additionally, such integration provides stronger validation of IoT applications, and improves resource utilization, as computational resources are shared, thus cutting down deployment and operational costs. Pressure and crash tests during continuous operations of several weeks, with more than 80K network joining and leaving of agents, 2.4M parameter changes, and 100M communicated messages, confirm the robustness and practicality of the testbed architecture. This work promises new pathways for managing the prototyping and deployment complexity of decentralized socio-technical systems running on IoT, whose complexity has so far hindered the adoption of value-sensitive self-management approaches in Smart Cities

Towards Energy-Aware Machine Learning in Geo-Distributed IoT Settings

No abstract available

Research and development of a web service for monitoring a storage cloud

136 σ.Σκοπός αυτής της διπλωματικής εργασίας υπήρξε η διερεύνηση και η μελέτη των κατάλληλων τεχνολογιών με στόχο τη ανάπτυξη και σχεδιασμό διαδικτυακής υπηρεσίας για τη επίβλεψη νέφους αποθήκευσης δεδομένων (storage cloud). Για τη αποκόμιση των κατάλληλων γνώσεων αρχικά μελετήθηκε το τι είναι cloud computing, ο διαχωρισμός του σε κατηγορίες, ποια τα πλεονεκτήματα και οι ανησυχίες που έχουν οργανισμοί για τη μεταφορά των υπηρεσιών τους σε νέφος. Ειδικό βάρος δόθηκε στη μελέτη των χαρακτηριστικών και την αρχιτεκτονική των νεφών αποθήκευσης δεδομένων (storage clouds). Ακολούθως μελετήθηκαν τα χαρακτηριστικά του αρχιτεκτονικού μοντέλου REST και τα πλεονεκτήματα του έναντι άλλων μοντέλων όπως το SOAP. Έπειτα, μελετήθηκε η γλώσσα σήμανσης HTML5 και δόθηκε ειδικό βάρος στο EventSource API που ειδικεύεται στη λήψη Server-Side Events (SSEs) στη πλευρά του πελάτη. Έπειτα, μελετήθηκε το πρωτόκολλο CDMI (Cloud Data Management Interface) σε αποθηκευτικά νέφη. Τέλος, αναλύθηκαν οι προκλήσεις που συναντάμε στην επίβλεψη νεφών και ειδικά στην επίβλεψη νεφών από τη μεριά του πελάτη (client-side cloud monitoring). Η διαδικτυακή υπηρεσία που αναπτύχθηκε παρέχει τη δυνατότητα σε χρήστες αποθηκευτικού νέφους, οι οποίοι εγγράφονται στη υπηρεσία μέσω διαδικτύου, να λαμβάνουν πληροφορίες για γεγονότα (events) που συμβαίνουν στο νέφος. Η διαδικτυακή υπηρεσία τρέχει στον περιηγητή του χρήστη χωρίς τη μεσολάβηση αλλού λογισμικού εγκατεστημένο τοπικά στον υπολογιστή. Με τον τρόπο αυτό μεταφέρουμε την επίβλεψη του αποθηκευτικού νέφους στη πλευρά του πελάτη. Η διαδικτυακή υπηρεσία ακολουθεί το αρχιτεκτονικό μοντέλο REST για την επικοινωνία της με το νέφος, υλοποιεί το πρωτόκολλο CDMI και ειδικότερα τις ουρές ειδοποίησης (Notification Queues) ενώ η ενημέρωση του χρήστη γίνεται με τη χρήση HTML5 EventSource. Αρχικά ο χρήστης δίνει τα στοιχεία του για επαλήθευση από το σύστημα, ακολούθως δημιουργεί μια ουρά γεγονότων και μεταβαίνει στη σελίδα παρακολούθησης γεγονότων όπου αρχίζει η ροή ενημερώσεων από το νέφος προς τον περιηγητή του. Στη σελίδα παρακολούθησης γεγονότων ο χρήστης έχει στη διάθεση του διάφορα εργαλεία όπως κονσόλα άφιξης νέου event και γραφικές παραστάσεις για γραφική πλοήγηση ανάμεσα στα αντικείμενα που φθάνουν μέσω των event από το νέφος.The objective of this thesis was to research and study the most suitable technologies available to develop and design a web service for monitoring a storage cloud system. To obtain the proper knowledge initially studied was cloud computing, its separation in different categories, what are the benefits and concerns that organizations face when thinking of transferring their services to the cloud. Special emphasis was given to studying the characteristics and architecture of storage clouds. Next studied were the characteristics of the REST architectural style and its advantages over other models such as SOAP. Afterwards studied was the HTML5 markup language with emphasis given to the EventSource API which specializes in receiving Server-Side Events (SSEs) on the client side. Next, studied was the CDMI (Cloud Data Management Interface) protocol for storage clouds. Finally, we analyzed the challenges encountered in cloud monitoring and especially in client-side cloud monitoring. The web service developed gives the opportunity to storage cloud users, who subscribe to the service via the internet, to receive information about events that occur in the cloud. The web service runs on the user’s browser without the need of other software installed locally on his desktop. By doing this, we transfer the monitoring of the storage cloud to the client side, discharging the cloud from the work load. The web service uses the REST architectural style for the communication with the cloud, implements the CDMI protocol and especially notification queues while informing the user of new events is done using HTML5 EventSource. Initially, the user provides the system with his credentials for verification and then creates an event queue. With the creation of a queue, a stream of new events can now start from the cloud to the user’s browser. At the Event Monitoring Page the user has a variety of tools to use such as Event Arrival Console and Graphs for graphical navigation between objects processed that arrived from the cloud.Δημήτρης Γ. Τριχινά

FlockAI: A Testing Suite for ML-Driven Drone Applications

Machine Learning (ML) is now becoming a key driver empowering the next generation of drone technology and extending its reach to applications never envisioned before. Examples include precision agriculture, crowd detection, and even aerial supply transportation. Testing drone projects before actual deployment is usually performed via robotic simulators. However, extending testing to include the assessment of on-board ML algorithms is a daunting task. ML practitioners are now required to dedicate vast amounts of time for the development and configuration of the benchmarking infrastructure through a mixture of use-cases coded over the simulator to evaluate various key performance indicators. These indicators extend well beyond the accuracy of the ML algorithm and must capture drone-relevant data including flight performance, resource utilization, communication overhead and energy consumption. As most ML practitioners are not accustomed with all these demanding requirements, the evaluation of ML-driven drone applications can lead to sub-optimal, costly, and error-prone deployments. In this article we introduce FlockAI, an open and modular by design framework supporting ML practitioners with the rapid deployment and repeatable testing of ML-driven drone applications over the Webots simulator. To show the wide applicability of rapid testing with FlockAI, we introduce a proof-of-concept use-case encompassing different scenarios, ML algorithms and KPIs for pinpointing crowded areas in an urban environment

FlockAI: A Testing Suite for ML-Driven Drone Applications

Machine Learning (ML) is now becoming a key driver empowering the next generation of drone technology and extending its reach to applications never envisioned before. Examples include precision agriculture, crowd detection, and even aerial supply transportation. Testing drone projects before actual deployment is usually performed via robotic simulators. However, extending testing to include the assessment of on-board ML algorithms is a daunting task. ML practitioners are now required to dedicate vast amounts of time for the development and configuration of the benchmarking infrastructure through a mixture of use-cases coded over the simulator to evaluate various key performance indicators. These indicators extend well beyond the accuracy of the ML algorithm and must capture drone-relevant data including flight performance, resource utilization, communication overhead and energy consumption. As most ML practitioners are not accustomed with all these demanding requirements, the evaluation of ML-driven drone applications can lead to sub-optimal, costly, and error-prone deployments. In this article we introduce FlockAI, an open and modular by design framework supporting ML practitioners with the rapid deployment and repeatable testing of ML-driven drone applications over the Webots simulator. To show the wide applicability of rapid testing with FlockAI, we introduce a proof-of-concept use-case encompassing different scenarios, ML algorithms and KPIs for pinpointing crowded areas in an urban environment

Towards Energy Consumption and Carbon Footprint Testing for AI-driven IoT Services

Energy consumption and carbon emissions are expected to be crucial factors for Internet of Things (IoT) applications. Both the scale and the geo-distribution keep increasing, while Artificial Intelligence (AI) further penetrates the "edge" in order to satisfy the need for highly-responsive and intelligent services. To date, several edge/fog emulators are catering for IoT testing by supporting the deployment and execution of AI-driven IoT services in consolidated test environments. These tools enable the configuration of infrastructures so that they closely resemble edge devices and IoT networks. However, energy consumption and carbon emissions estimations during the testing of AI services are still missing from the current state of IoT testing suites. This study highlights important questions that developers of AI-driven IoT services are in need of answers, along with a set of observations and challenges, aiming to help researchers designing IoT testing and benchmarking suites to cater to user needs.Comment: Presented at the 2nd International Workshop on Testing Distributed Internet of Things Systems (TDIS 2022

A Self-stabilizing Control Plane for Fog Ecosystems

Fog Computing is now emerging as the dominating paradigm bridging the compute and connectivity gap between sensing devices and latency-sensitive services. However, as fog deployments scale by accumulating numerous devices inter-connected over highly dynamic and volatile network fabrics, the need for self-healing in the presence of failures is more evident. Using the prevailing methodology of self-stabilization, we propose a fault-tolerant framework for control planes that enables fog services to cope and recover from a very broad fault model. Specifically, our model considers network uncertainties, packet drops, node fail-stops and violations of the assumptions according to which the system was designed to operate (e.g., system state corruption). Our self-stabilizing algorithms guarantee automatic recovery within a constant number of communication rounds without the need for external (human) intervention. To showcase the framework\u27s effectiveness, the correctness proof of the self-stabilizing algorithmic process is accompanied by a comprehensive evaluation featuring an open and reproducible testbed utilizing real-world data from the smart vehicle domain. Results show that our framework ensures a fog system recovers from faults in constant time, analytics are computed correctly, while the control plane overhead scales linearly towards the IoT load