The increasing deployment of distributed sensor networks for environmental monitoring introduces challenges in data collection. Traditional Cloud-based architectures often struggle with high latency, bandwidth constraints, and scalability issues, especially when handling spatially dense and high-frequency sensor data. To address these challenges, we propose a Fog-based interpolation and Cloud aggregation framework that distributes computational tasks across Edge, Fog, and Cloud layers to enhance the efficiency of smart sensing applications. Our approach exploits localized spatial interpolation methods (e.g., Inverse Distance Weighting and Radial Basis Functions) directly at Fog nodes, allowing for fast computation and reduced data transmission to the Cloud, which remains only in charge of refining and merging the interpolated datasets. The proposed hierarchical data processing strategy minimizes network usage and enhances scalability due to local processing at the Fog layer, making it ideal for large-scale smart computing systems. The paper focuses on the analysis of the trade-offs between computational overhead, accuracy, and data transmission efficiency through an experimental validation conducted using real-world vehicles' paths. Results demonstrate that Fog-assisted interpolation can reduce latency, achieving up to 81% improvement in some cases, while maintaining accuracy comparable to a global approach in the Cloud.

In a multiple cloud environment, the placement of execution environments is crucial and may be subject to data constraints. Data could be anchored to some environments due to regulatory compliance, data sovereignty issues, or performance optimization. Consequently, applications and microservices must be designed to operate efficiently with these constraints. This en-forces specific placement strategies to obtain good performances and scalability. This paper proposes a technique to enforce a constrained data-centric deployment placement in a federated multi-cloud environment. The algorithm analyzes a graph model of microservices interaction, considering communication with data storage and adopting “anchors” for implementing a data-centric placement strategy. The results show how a better placement based on data position in multiple clouds improves performance in terms of overall system response time. This also allows microservices to offload near data sources for multi-cloud environments, improving overall system performance without violating data movement constraints.

Abstract The microservices architectural style aims at improving software maintenance and scalability by decomposing applications into independently deployable components. A common criticism about this style is the risk of increasing response times due to communication, especially with very granular entities. Locality‐aware placement of microservices onto the underlying hardware can contribute to keeping response times low. However, the complex graphs of invocations originating from users' calls largely depend on the specific workload (e.g., the length of an invocation chain could depend on the input parameters). Therefore, many existing approaches are not suitable for modern infrastructures where application components can be dynamically redeployed to take into account user expectations. This paper contributes to overcoming the limitations of static or off‐line techniques by presenting a big data pipeline to dynamically collect tracing data from running applications that are used to identify a given number of microservices groups whose deployment allows keeping low the response times of the most critical operations under a defined workload. The results, obtained in different working conditions and with different infrastructure configurations, are presented and discussed to draw the main considerations about the general problem of defining boundary, granularity, and optimal placement of microservices on the underlying execution environment. In particular, they show that knowing how a specific workload impacts the constituent microservices of an application, helps achieve better performance, by effectively lowering response time (e.g., up to a reduction), through the exploitation of locality‐driven clustering strategies for deploying groups of services.

The microservices architectural style has gained widespread acceptance. However, designing applications according to this style is still challenging. Common difficulties concern finding clear boundaries that guide decomposition while ensuring performance and scalability. With the aim of providing software architects and engineers with a systematic methodology, we introduce a novel actor-driven decomposition strategy to complement the domain-driven design and overcome some of its limitations by reaching a finer modularization yet enforcing performance and scalability improvements. The methodology uses a multi-level scalability assessment framework that supports decision-making over iterative steps. At each iteration, architecture alternatives are quantitatively evaluated at multiple granularity levels. The assessment helps architects to understand the extent to which architecture alternatives increase or decrease performance and scalability. We applied the methodology to drive further decomposition of the core microservices of a real data-intensive smart mobility application and an existing open-source benchmark in the e-commerce domain. The results of an in-depth evaluation show that the approach can effectively support engineers in (i) decomposing monoliths or coarse-grained microservices into more scalable microservices and (ii) comparing among alternative architectures to guide decision-making for their deployment in modern infrastructures that orchestrate lightweight virtualized execution units.

Continuous data streams, generated by modern sensed cities, open many opportunities and perspectives in terms of developing new innovative services. To exploit this potential, flexible and scalable platforms are needed to ease the design, development, deployment, and operations of new city services. In recent years, several problem-specific platforms have been proposed in different application domains; however, to boost the evolution of smart cities, we claim the need for city-oriented platforms that can be easily customized to address different day-to-day life challenging problems. In this paper, we present the main architectural challenges and solutions proposed for the design of a novel open-source platform (named PROMENADE) characterized by: i ) a data-driven graph-based modeling support to ensure high generality for addressing disparate problems related to the networked nature of many city infrastructures and systems, ii ) the dynamic nature of the graph entities updated in real-time from different sources ( e.g., IoT/Edge networks, data providers, etc.), and iii ) high efficiency, scalability and flexibility to easily support new city services. The platform is designed around a general-purpose core that provides a set of built-in standard features such as data ingestion , storage, processing, and visualization exposed as a collection of containerized microservices . A specialization of the platform has been developed for road networks monitoring. It has been deployed in OpenShift/Kubernetes and tested using realistic datasets collected from the city of Lyon, France. The analysis addresses an important problem of big data processing pipelines: the synchronization between data ingestion and processing in order to produce an accurate result in useful time. To this end, we study different approaches for synchronization and show how the end-to-end latency is kept under control by leveraging the scalability of the platform.