Dynamic task offloading is essential for real-time robotic applications, enabling them to adapt to fluctuating computational demands and maintain efficiency under changing conditions. This paper introduces a dynamic task offloading framework that incorporates monitoring, decision making, offloading triggering, and performance monitoring to optimize resource usage by offloading real-time tasks to edge and cloud servers. A use case from the manufacturing industry demonstrates the framework’s application, enhancing robotic functions like motion planning. WebAssembly enables the execution of the robotics application across diverse environments, improving both portability and computational efficiency. By addressing key challenges, this work sets the stage for future offloading frameworks to meet the evolving needs of robotic systems.

Conventional cloud services and infrastructures are mainly designed to maximize utilization of resources and provide best-effort Quality-of-Service levels. However, many emerging use cases in both public and private cloud computing scenarios are time-critical in nature. For example, automated vehicles, smart cities, and automated factories, are all application domains characterized by the need for highly reliable and consistent low-latency services. The incorporation of predictable execution properties in cloud solutions is essential to meet these requirements. This paper provides an overview of the current research landscape in cloud computing, summarizing the key aspects to enable support of time-critical applications. The paper explores various levels of the typical cloud software stack: machine virtualization and containers, resource management and orchestration, fault tolerance, serverless computing, data storage and management, and communications.

This paper tackles the problem of optimal configuration and deployment of fault-tolerant time-critical service chains with arbitrary DAG-alike topologies. We propose RTilience, designed according to a scalable cloud microservice paradigm, and prototyped on top of the well-known Kubernetes cloud orchestrator. It features real-time reservation scheduling of containers to guarantee temporal isolation of time-critical tasks, leading to fine-grained control of compute latencies, while allowing for sharing physical CPUs among containers. A distributed routing library, ReqRoute, is configured with a timeout and primary and secondary routes, enabling autonomous and decentralized handling of failing requests. The routes are configured by a centralized controller that performs admission control, resource management of microservice instances, task placement, and fault detection and recovery, extending the features available in Kubernetes. Admission control is based on a theoretical framework enclosing a worst-case performance model for the experienced end-to-end response-time under various fault handling options, and an optimization framework that computes the optimum resource allocation for admitted services. Extensive experimentation of the proposed solution has been performed with synthetic examples, and an autonomous transport robot use-case, verifying that end-to-end deadlines are effectively respected, even in presence of high fault rates of individual microservice instances, according to the theoretical expectations. RTilience is made available as open-source software, released under a MIT license.

This work presents a model for designing and deploying time-critical, cloud-native applications under fault conditions. Our model considers the interactions and interferences among service components, as well as the possible occurrence of faults. Given a set of to-be-deployed applications with precise temporal constraints and a predefined configuration of the service components, we devised an optimizer to verify at design time if the cloud services guarantee compliance with the timing constraints while minimizing the resources needed to achieve fault tolerance.

This paper presents the Fault-Tolerant Real-Time Cloud (FTRTC) project that aims to design cloud computing infrastructures capable of hosting highly reliable and real-time applications. These applications are characterized by strict timing and reliability constraints, as well as critical failure scenarios. For instance, such requirements are commonly found in the context of Industry 4.0. We present a formalization of the problem of designing real-time cloud applications supporting an adjustable level of fault tolerance throughout their distributed execution in a cloud infrastructure. The contributions presented in this paper indicate important research directions when building cloud infrastructures able to supporting ultra-reliable real-time applications.