Virtualization platforms like Proxmox VE, which extend hypervisors such as KVM with orchestration tools, are increasingly adopted in High-performance computing (HPC) for improved flexibility and resource management. This study evaluates Proxmox VE against KVM and bare-metal environments to quantify virtualization overheads in CPU-bound and inter-process communication (IPC) workloads. Using HPL and NetPIPE benchmarks, competitive runs show Proxmox VE's 0.5%-1.1% compute overhead versus bare-metal (KVM: 0.9%-1.2%), rising to 1.8%-2.0% for cooperative MPI-based HPL. Proxmox VE reduces RAM usage by 15%-20% over KVM via dynamic ballooning. Intra-VM IPC achieves native performance (120 Gbps, <0.005 μs), while intra-host IPC sustains 50 Gbps (vs. KVM's 60 Gbps) with 20%-30% latency increases. Inter-host IPC under high contention suffers 30% bandwidth loss and 25%-30% latency degradation. CPU utilization remains stable (50%) across platforms. Results demonstrate Proxmox VE's viability for CPU-bound/single-host HPC, with network optimizations needed for distributed workflows.

Current indoor positioning technologies face significant limitations in dynamic industrial environments, where metallic structures and signal interference degrade accuracy. While Bluetooth Low Energy (BLE) RSSI-based systems offer a cost-effective and energy-efficient solution, their performance is often hampered by path loss variability in dynamic environments. Usually, to improve BLE RSSI-based positioning accuracy, the number of infrastructure nodes (IN) may be increased. However, increasing the number of static infrastructure nodes (SIN) leads to increased complexity, cost, and network load.To address these challenges, we propose a novel collaborative indoor positioning method integrating autonomous mobile robots (AMRs) as mobile infrastructure nodes (MINs). Unlike traditional static deployments, our system leverages AMRs equipped with high-accuracy onboard positioning and BLE receivers to dynamically collect and correlate RSSI data while navigating the environment. This approach enables real-time adaptive localization by fusing MIN data with fixed SIN measurements using a machine learning-based fusion model (combining Neural Networks and Kernel Density Estimation).We validate our method through a real-world industrial testbed, demonstrating a 27% improvement in localization accuracy compared to static BLE systems, with an equal number of equipment within dynamic industrial landscapes with a high packet loss rate..

Providing Artificial Intelligence as a Service (AIaaS) for the next generation of Industry, known as Industry 5.0, is committed to providing an accurate indoor localization system. Providing Indoor localization, particularly in dynamic industrial environments where operational efficiency relies on precise location information, is a challenging task. This paper presents a Collaborative Indoor Positioning System (CIPS) facilitated by a location-aware autonomous mobile robot equipped with Bluetooth Low Energy (BLE) beacons to enhance indoor localization accuracy through dynamic path loss parameter estimation. The robot traverses the facility while periodically broadcasting its ID and real-time location to a central server. By correlating the robot’s ground-truth positions with Received Signal Strength Indicator (RSSI) measurements from fixed BLE receivers, the server continuously updates path loss parameters using a log-normal shadowing model and linear regression. Evaluation of the proposed system is conducted using a real testbed with a deployed application system in an office environment. Experimental results demonstrate a significant improvement in distance estimation accuracy, ranging between 19% and 32%, and a 20% reduction in location estimation error compared to conventional methods, highlighting the potential of the proposed CIPS approach for enhancing indoor localization in industrial settings.

Serverless computing abstracts infrastructure management, enabling cost-efficient and scalable application deployment. However, multiple operations can be performed simultaneously, leading to system degradation of resources exhaustion during prolonged executions. This study assesses resource utilization in serverless environments using Knative, testing four OS-container engine configurations (Ubuntu/Docker, Ubuntu/Podman, Debian/Docker, Debian/Podman) under stress workload. Results show Ubuntu paired with Docker achieves optimal resource efficiency, avoids software aging, and maintains consistent performance, making it the most effective configuration for scalable serverless deployments. The absence of progressive degradation in resource metrics underscores serverless architectures’ potential to resist software aging through autoscaling. Findings provide actionable insights for optimizing resource management in open-source serverless platforms.

This study delves into the dynamic landscape of cost versus performance ratio within the Raspberry Pi family of computers, specifically scrutinizing the Raspberry Pi B and Raspberry Pi Zero lines. Based on previous analyses, our comprehensive investigation encompasses all generations of the Raspberry Pi B and Zero lines available until January 2024. Prices are meticulously adjusted to the 2012 dollar value, aligning with the inaugural launch of the Raspberry Pi. The findings illuminate an upward in performance around 229 times over an 11-year period, coupled with a notable decline in the cost per unit of performance. The impact of the dollar's depreciation since 2012 further accentuates these trends.

Cloud computing is a method for accessing and managing computing resources over the internet, providing flexibility, scalability, and cost-efficiency. Cloud computing relies more and more on OS-level virtualization tools such as Docker and Podman, enabling users to create and run containers, which are widely used for application management. Given its significance in cloud infrastructures, it is crucial to have a better understanding of OS-level virtualization performance, especially in tasks related to container management (ex: creation, destruction). In this paper, we conducted benchmarking tests on Docker and Podman to evaluate their performance in various container management scenarios and with different image sizes. The results revealed that Podman excels in quickly instantiating small-sized containers, while Docker demonstrates superior performance with larger-sized containers.

Systems-on-a-chip (SoC) represents a rupture on the traditional HPC infrastructure and started to be adopted together OS-level virtualization to provide services in Fog and Edge computing scenarios. In this work, we analyzed the performance of OS-level virtualization solutions - Linux Containers (LXC) and Docker - for HPC activities running in SoC systems in order to discover how OS-level virtualization and resource sharing affects the performance of virtualized environments. We evaluated CPU and (network and inter-process) communication performance taking in account two different scenarios: without communication between benchmark processes in execution and; with processes communication in a cooperative way. Results shows that both tools are suitable for HPC applications in SoC systems, despite the notice of some performance reduction due resource sharing.

Organizations need process data in real time. Cloud Computing is a common choice but it presents high latency on data transfer. As an alternative to reduces the latency, Edge computing based on System-on-a-Chip systems (SoC-based systems) can retain part of the data processing, allowing resource sharing among multiple requests through the use of OS-level virtualization. In this work, we analyze the performance of two popular OS-level virtualization solutions - Linux Containers (LXC) and Docker - for I/O-bound activities running in SoC systems, in order to understand how resource sharing affects the processing performance.

Our investigation aims to answer which scenarios LXC, a lightweight virtualization solution, can offer a better performance than KVM, a hypervisor-based virtualization, or even equal to native environments. For that, we are considering HPC I/O bound applications and the effects of resource sharing on the performance of virtualized environments with both tools. We conducted experiments with traditional benchmarks in two different scenarios: without communication between benchmark processes in execution and; with processes communication in a cooperative way. Results indicate that LXC can offer a better performance for I/O-bound applications in most cases than KVM; and LXC is less affected while we increase the degree of resource sharing between multiple abstractions hosted at the same host server.

Despite of Cloud infrastructures can be used as High Performance Computing (HPC) platforms, many issues from virtualization overhead had kept them unrelated. However, with advent of container-based virtualizers, this scenario acquires new perspectives because this technique promises to decrease the virtualization overhead, achieving a near-native performance. In this work, we analyzed the performance of a container-based virtualization solution - Linux Container (LXC) - against a hyper visor-based virtualization solution - KVM - under HPC activities. For our experiments, we considered CPU and communication (network and inter-process communication), and results showed the virtualizer type can impact distinctly in performance according to resource used by application.

High Performance Computing (HPC) aggregates computing power in order to solve large and complex problems in different knowledge areas. Nowadays, HPC users can utilize virtualized infrastructures as a low-cost alternative to deploy their applications. However, virtualization brings some challenges for HPC, specially in regard to overhead caused by hyper visors. In this work, our main goal is to analyze the performance of two hyper visors (KVM and Virtual Box) under HPC activities, considering full virtualization, and Para virtualization approaches. We used the HPC Challenge Benchmark (HPCC) to evaluate processor, RAM, inter-process communication and network communication performance. Our results show KVM in Para virtualization mode has a similar performance of a native cluster.