Modern interactive and data-intensive applications must operate under demanding time constraints, prompting a shift toward the adoption of specialized software and hardware network acceleration technologies. This specialization, however, poses significant scalability, flexibility, security, and economic sustainability challenges for application developers. Cloud computing holds the potential to overcome these obstacles by offering the cost-effective option to access specialized acceleration technologies through standard cloud interfaces. Nevertheless, that integration is still challenging for cloud providers. In the cloud, physical resources are hidden behind a virtualization layer, whereas acceleration technologies make applications directly interact with the hardware. To bridge this gap, recent literature explores the possibility of empowering cloud platforms with accelerated networking as a commodity, thus offering the innovative option of Network Acceleration as a Service. This survey reviews these recent research efforts by adopting popular technologies like XDP, DPDK, and RDMA as a reference. To organize the surveyed research in a comprehensive framework, we identify four key aspects that pose critical problems to the integration of acceleration options in cloud computing -access interfaces, virtualization techniques, serviceability, and security -and systematically discuss the associated challenges. Then, we present the issues to be further addressed and outline the most promising research directions for the full integration of network acceleration within next-generation cloud computing platforms.

Edge cloud computing is a promising programming and deployment paradigm to empower delay-sensitive applications. By executing close to the network edge, distributed applications can have quicker reactions to event occurrence and consequently prompter dynamic adaptations. In addition, recent improvements in connectivity support allow developers to benefit from heterogeneous and alternative communication technologies (e.g., RDMA, DPDK, XDP, etc.) to meet the requirements of network-intensive edge applications. However, exploiting these technologies makes applications statically tailored to a specific network interface; this significantly limits the potential of edge cloud computing, where application components should be able to migrate seamlessly at runtime. INSANE aims at solving that issue by exposing a technology-agnostic middleware API that lets developers simply specify their QoS communication requirements; the dynamic selection of the most appropriate technology on the currently hosting edge node is delegated to INSANE. The paper also presents how it is possible to develop two different INSANE-based applications (a decentralized messaging system and an image streaming framework) with a few lines of code. Finally, an extensive performance evaluation shows that our middleware adds very limited ns-scale overhead to the raw acceleration technologies.

The integration of distributed big data analytics into modern industrial environments has become increasingly critical, particularly with the rise of data-intensive applications and the need for real-time processing at the edge. While High-Performance Computing (HPC) systems offer robust petabyte-scale capabilities for efficient big data analytics, the performance of big data frameworks, especially on ARM-based HPC systems, remains underexplored. This paper presents an extensive experimental study on deploying Apache Spark 3.0.2, the de facto standard in-memory processing system, on an ARM-based HPC system. This study conducts a comprehensive performance evaluation of Apache Spark through representative big data workloads, including K-means clustering, to assess the effects of latency variations, such as those induced by network delays, memory bottlenecks, or computational overheads, on application performance in industrial IoT and edge computing environments. Our findings contribute to an understanding of how big data frameworks like Apache Spark can be effectively deployed and optimized on ARM-based HPC systems, particularly when leveraging vectorized instruction sets such as SVE, contributing to the broader goal of enhancing the integration of cloud–edge computing paradigms in modern industrial environments. We also discuss potential improvements and strategies for leveraging ARM-based architectures to support scalable, efficient, and real-time data processing in Industry 4.0 and beyond.

The Open Radio Access Network (O-RAN) is reshaping cellular architectures through disaggregation, openness, and programmability, enabling intelligent control across modular RAN components. A growing ecosystem of user-defined control-plane applications, namely rApps, xApps, and the emerging dApps, operates at different timescales and unlocks advanced control loop capabilities, but introduces diverse and stringent Quality of Service (QoS) requirements for communication. Current implementations typically rely on cloud-based messaging systems, which privilege transparency and ease of use for developers and limit support for time-sensitive workloads. In this paper, we propose that O-RAN control loops base their internal communication on INSANE, a cloud-native, data-centric middleware that supports multiple networking stacks, including kernel-bypass option such as eBPF XDP, DPDK, and RDMA. Our preliminary evaluation shows that INSANE achieves nearly 2× higher throughput than widely used alternatives, while reducing 99.9th-percentile latency of over an order of magnitude for small messages. At the same time, INSANE preserves a uniform and easy-to-use programming interface. These results highlight INSANE as a promising foundation for faster, more predictable control loops, a significant step toward the ultimate goal of enabling AI-driven RAN optimizations in O-RAN systems.