Internet-enabled physical devices with "smart" processing capabilities are becoming the tools for understanding the complexity of the global inter-connected world we inhabit. The Internet of Things (IoT) churns tremendous amounts of data flooding from devices scattered across multiple locations to the processing engines of almost all industry sectors. However, as the number of "things" surpasses the population of the technology-enabled world, real-time processing and energy-efficiency are great challenges of the big data era transitioning to IoT. In this article, we introduce a lightweight adaptive monitoring framework suitable for smart IoT devices with limited processing capabilities. Our framework, inexpensively and in place dynamically adapts the monitoring intensity and the amount of data disseminated through the network based on the current evolution and variability of the monitoring stream. By accomplishing this, energy consumption and data volume are reduced, allowing IoT devices to preserve battery and ease processing on cloud computing and big data services. Experiments on real-world data from cloud services, internet security services, wearables and intelligent transportation services, show that our framework achieves a balance between efficiency and accuracy. Specifically, our framework reduces data volume by 74%, energy consumption by at least 71%, while achieving a greater than 89% accuracy.

Software teams of all sizes are embracing the DevOps philosophy to rapidly deliver applications by adopting the microservice paradigm. Industry trends show clearly that microservice adoption is expanding rapidly. However, microservices are not without challenges. Deployment at scale calls for implementing autonomic principles and solutions, which lead to higher complexity and increased failure risk.

Federated learning (FL) is a transformative approach to Machine Learning that enables the training of a shared model without transferring private data to a central location. This decentralized training paradigm has found particular applicability in edge computing, where IoT devices and edge nodes often possess limited computational power, network bandwidth, and energy resources. While various techniques have been developed to optimize the FL training process, an important question remains unanswered: how should resources be allocated in the training workflow? To address this question, it is crucial to understand the nature of these resources. In physical environments, the allocation is typically performed at the node level, with the entire node dedicated to executing a single workload. In contrast, virtualized environments allow for the dynamic partitioning of a node into containerized units that can adapt to changing workloads. Consequently, the new question that arises is: how can a physical node be partitioned into virtual resources to maximize the efficiency of the FL process? To answer this, we investigate various resource allocation methods that consider factors such as computational and network capabilities, the complexity of datasets, as well as the specific characteristics of the FL workflow and ML backend. We explore two scenarios: (i) running FL over a finite number of testbed nodes and (ii) hosting multiple parallel FL workflows on the same set of testbed nodes. Our findings reveal that the default configurations of state-of-the-art cloud orchestrators are sub-optimal when orchestrating FL workflows. Additionally, we demonstrate that different libraries and ML models exhibit diverse computational footprints. Building upon these insights, we discuss methods to mitigate computational interferences and enhance the overall performance of the FL pipeline execution.

Cloud computing enables on-demand provisioning of computing resources to IT solutions, following a utility-based approach. Technology and standardization developments in traditional public utilities enable customers to seamlessly migrate across utility providers without being required to make changes to their home appliances. Dubbed as the fifth utility, cloud computing customers should have user-friendly tools and mechanisms at their disposal, which preserve application deployments across different resource providers. Here, the authors present current efforts to develop an open source Cloud Application Management Framework (CAMF) based on the Eclipse Rich Client Platform. This framework facilitates cloud application lifecycle management in a vendor-neutral approach.

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.