Earth observation (EO) problems require processing large volumes of data, such as those involved in environmental studies using diverse spatiotemporal variables. In this context, the computing continuum offers a promising model to support EO tasks. It enables low-latency data processing near ground stations and broad data sharing through the cloud. Nevertheless, designing systems in the computing continuum introduces challenges. One is the coordination of distributed entities across different computational environments (e.g., the edge, the fog, or the cloud). Another is managing data across heterogeneous infrastructures. This paper presents OrbiFaaS, a serverless-based method for building Continuum Earth Observation Systems (EOS) using an orbital model. OrbiFaaS organizes a continuum EOS as a system of multiple orbits, each representing a different computational environment arranged around a core of data sources. Orbits near the core correspond to low-latency environments (e.g., the edge), while those farther away exhibit higher latencies (e.g., the cloud). Each orbit contains satellites, which represent service provider infrastructures. Organizations can use these satellites to deploy multiple data management services, such as containerized microservices or functions. Satellites are interconnected using data containers that establish communication channels based on available filesystem, memory, and network resources. We evaluate OrbiFaaS in a case study involving the processing of satellite images across multiple environments. Our experimental results demonstrate the feasibility and efficiency of OrbiFaaS for constructing Earth observation systems.

Context: Organizations increasingly rely on artificial intelligence (AI) and machine learning (ML) to process data, automate tasks, and enhance decision-making. At the same time, the computing continuum enables AI and ML to be deployed closer to data sources, thereby reducing system latency and response time. Objective: Managing applications across this distributed environment is challenging due to the need for manual deployment, integration, and compliance with non-functional requirements (NFRs) such as security and fault tolerance. Therefore, there is a need for frameworks that automate the deployment and execution of computing continuum systems while integrating both functional and non-functional requirements. Method: This paper presents Nez , a design-driven skeleton model for building continuum AI and analytics systems. Nez construction model automatically and transparently integrates AI/ML applications with non-functional components to create continuum systems that are deployed dynamically across multiple distributed infrastructures. Results: We conducted case studies on the processing of medical imagery and satellite imagery to provide automatic and continuous support for decision-makers. Nez has already been deployed at the Mexican hospital, Instituto Nacional de Rehabilitación Luis Gerardo Ibarra Ibarra , to create an AI-based data flow supporting bone cancer diagnosis. The evaluation shows that Nez outperforms state-of-the-art tools such as Nextflow, Makeflow, and Parsl, achieving improvements in response time of 28.46%, 17.46%, and 23.54%, respectively. Conclusion: Nez efficiently transforms organizational data flow designs into continuum computing services. This enables organizations to construct continuum AI-based and analytical systems that account for both functional and non-functional requirements.

Computing continuum systems are emerging as a solution for organizations to process data across diverse infrastructures, reducing latency compared to traditional cloud computing. However, managing I/O operations in such distributed and heterogeneous environments remains an open research challenge. In this paper, we present A-Flow, a model for constructing I/O systems to manage data exchange in computing continuum environments. These systems are built around data distribution patterns defined by structures called abstract communication channels (ACCs). ACCs are established between processing stages using memory, file system, and network resource connections. To prevent resource overload during execution, A-Flow automatically selects the appropriate communication channel based on user-defined criteria such as throughput or resource utilization. We implemented this model in a prototype and evaluated it through a case study focused on managing medical data in HDF5 format across different environments. The evaluation revealed that A-Flow’s ACCs can be integrated into existing stage-based systems found in the state of the art. The results highlight the efficiency and effectiveness of A-Flow in enabling dataflows across heterogeneous infrastructures, addressing key challenges in computing continuum.

Computing continuum is becoming a solution for organizations to process and analyze data for supporting decision-making processes. In this context, serverless paradigm is arising as a solution to manage continuum computing. However, the management of data storage still represents an obstacle for integrating continuum computing and serverless paradigms into a single solution, as this has to be performed transparently to users through multiple infrastructures. This paper presents StructMesh, a storage framework for serverless continuum systems. This framework is based on a processing plane where functions are managed as patterns, and a data plane based on storage meshes that represent maps of storage resources available in a given infrastructure. The logical interconnection of storage meshes enables organizations to integrate storage resources into a single unified storage service, which creates data exchange channels for continuum processing throughout multiple infrastructures. These meshes include load-balancing and data allocation/location algorithms for transparently and automatically managing the inputs/outputs of functions throughout these channels, as well as non-functional requirement schemes for organizations to manage sensitive data. We developed a framework prototype that harmonizes processing serverless functions with storage functions for building serverless pipeline services. A case study was conducted by using these services for processing meteorological and earth observation data throughout multiple infrastructures. The evaluation revealed the efficiency of StructMesh when managing data through fog and cloud infrastructures. It also showed the feasibility of StructMesh to create and enable continuum data exchange channels for serverless pipelines.

MeshStore, a fault-tolerant serverless storage model for edge-fog-cloud continuum systems, enables organizations to integrate distributed heterogeneous storage resources into a single unified storage service for the sharing of data through serverless functions deployed on edge-fog-cloud environments to create continuum dataflows. This unified service automatically and transparently manages the input/output data of serverless functions by coupling storage structures, including load-balancing and data allocation/location algorithms. Organizations also can add non-functional requirement properties (e.g., either reliability or security) to the storage structures when managing sensitive data.

Cloud storage has been the solution for organizations to manage the exponential growth of data observed over the past few years. However, end-users still suffer from side-effects of cloud service outages, which particularly affect edge-fog-cloud environments. This paper presents SeRSS, a storage mesh architecture to create and operate reliable, configurable, and flexible serverless storage services for heterogeneous infrastructures. A case study was conducted based on-the-fly building of storage services to manage medical imagery. The experimental evaluation revealed the efficiency of SeRSS to manage and store data in a reliable manner in heterogeneous infrastructures.

In organizational environments, such as in hospitals, data have to be processed, preserved, and shared with other organizations in a cost-efficient manner. Moreover, organizations have to accomplish different mandatory non-functional requirements imposed by the laws, protocols, and norms of each country. In this context, this paper presents a Federated Content Distribution System to build infrastructure-agnostic health data synchronization services. In this federation, each hospital manages local and federated services based on a pub/sub model. The local services manage users and contents (i.e., medical imagery) inside the hospital, whereas federated services allow the cooperation of different hospitals sharing resources and data. Data preparation schemes were implemented to add non-functional requirements to data. Moreover, data published in the content distribution system are automatically synchronized to all users subscribed to the catalog where the content was published.