Evaluating Latency in Multiprocessing Embedded Systems for the Smart Grid

Smart grid endpoints need to use two environments within a processing system (PS), one with a Linux-type operating system (OS) using the Arm Cortex-A53 cores for management tasks, and the other with a standalone execution or a real-time OS using the Arm Cortex-R5 cores. The Xen hypervisor and the OpenAMP framework allow this, but they may introduce a delay in the system, and some messages in the smart grid need a latency lower than 3 ms. In this paper, the Linux thread latencies are characterized by the Cyclictest tool. It is shown that when Xen hypervisor is used, this scenario is not suitable for the smart grid as it does not meet the 3 ms timing constraint. Then, standalone execution as the real-time part is evaluated, measuring the delay to handle an interrupt created in programmable logic (PL). The standalone application was run in A53 and R5 cores, with Xen hypervisor and OpenAMP framework. These scenarios all met the 3 ms constraint. The main contribution of the present work is the detailed characterization of each real-time execution, in order to facilitate selecting the most suitable one for each application.This work has been supported by the Ministerio de Economía y Competitividad of Spain within the project TEC2017-84011-R and FEDER funds as well as by the Department of Education of the Basque Government within the fund for research groups of the Basque university system IT978-16. It has also been supported by the Basque Government within the project HAZITEK ZE-2020/00022 as well as the Ministerio de Ciencia e Innovación of Spain through the Centro para el Desarrollo Tecnológico Industrial (CDTI) within the project IDI-20201264; in both cases, they have been financed through the Fondo Europeo de Desarrollo Regional 2014-2020 (FEDER funds). It has also been supported by the University of the Basque Country within the scholarship for training of research staff with code PIF20/135

Multi-Mode Virtualization for Soft Real-Time Systems

Real-time virtualization is an emerging technology for embedded systems integration and latency-sensitive cloud applications. Earlier real-time virtualization platforms require offline configuration of the scheduling parameters of virtual machines (VMs) based on their worst-case workloads, but this static approach results in pessimistic resource allocation when the workloads in the VMs change dynamically. Here, we present Multi-Mode-Xen (M2-Xen), a real-time virtualization platform for dynamic real-time systems where VMs can operate in modes with different CPU resource requirements at run-time. M2-Xen has three salient capabilities: (1) dynamic allocation of CPU resources among VMs in response to their mode changes, (2) overload avoidance at both the VM and host levels during mode transitions, and (3) fast mode transitions between different modes. M2-Xen has been implemented within Xen 4.8 using the real-time deferrable server (RTDS) scheduler. Experimental results show that M2-Xen maintains real-time performance in different modes, avoids overload during mode changes, and performs fast mode transitions

Freezing time emulating new and faster devices with virtual machines

Recent proposals of emerging data storage devices make it necessary to reevaluate all levels of the storage hierarchy to optimize the software stack performance. However, these new devices are not always widely available and therefore early experiments may be impossible. Emulators aim at mimicking as close as possible the behavior of a component, nonetheless, emulating new and fast storage devices is a challenging task due to time perception. In this work, we propose an approach to emulate storage devices using virtual machines (VMs) allowing the evaluation of a new device within a real system. We use a technique called freezing time, which pauses a VM to manipulate its clock and hide the real I/O completion time. Our approach is implemented at the hypervisor level and it is transparent to the guest operating system or application. We evaluate the technique under a real system using regular magnetic disks to emulate faster storage devices. Our method presented a latency error of 6.5% compared to a real device. Moreover, decoupled experiment between two laboratories, at the Barcelona Super Computing Center (BSC) in Spain, and the Center of Computer Science and Free Software (C3SL) in Brazil, demonstrated that our approach is reproducible and promising to allow the virtual evaluation of next-gen storage devices.This work was partially supported by the Spanish Ministry of Science and Innovation under the TIN2015-65316 Grant, the Generalitat de Catalunya under contract 2014-SGR-1051, the Serrapilheira Institute (Grant number Serra-1709-16621), as well as the European Union’s Horizon 2020 Research and Innovation Programme, under Grant Agreement no. 671951 (NEXTGenIO) for the extensions added after the MASCOTS paper.Peer ReviewedPostprint (author's final draft

Analysis of Performance and Power Aspects of Hypervisors in Soft Real-Time Embedded Systems

The exponential growth of malware designed to attack soft real-time embedded systems has necessitated solutions to secure these systems. Hypervisors are a solution, but the overhead imposed by them needs to be quantitatively understood. Experiments were conducted to quantify the overhead hypervisors impose on soft real-time embedded systems. A soft real-time computer vision algorithm was executed, with average and worst-case execution times measured as well as the average power consumption. These experiments were conducted with two hypervisors and a control configuration. The experiments showed that each hypervisor imposed differing amounts of overhead, with one achieving near native performance and the other noticeably impacting the performance of the system

Benchmarking of bare metal virtualization platforms on commodity hardware

In recent years, System Virtualization became a fundamental IT tool, whether it is type-2/hosted virtualization, mostly exploited by end-users in their personal computers, or type-1/bare metal, well established in IT departments and thoroughly used in modern datacenters as the very foundation of cloud computing. Though bare metal virtualization is meant to be deployed on server-grade hardware (for performance, stability and reliability reasons), properly configured desktop-class systems are often used as virtualization “servers”, due to their attractive performance/cost ratio. This paper presents the results of a study conducted on such systems, about the performance of Windows 10 and Ubuntu Server 16.04 guests, when deployed in what we believe are the type-1 platforms most in use today: VMware ESXi, Citrix XenServer, Microsoft Hyper-V, and KVM-based (represented by oVirt and Proxmox). Performance is measured using three synthetic benchmarks: PassMark for Windows, UnixBench for Ubuntu Server, and the cross-platform Flexible I/O Tester. The benchmarks results may be used to choose the most adequate type-1 platform (performance-wise), depending on guest OS, its performance requisites (CPU-bound, IO-bound, or balanced) and its storage type (local/remote) used.info:eu-repo/semantics/publishedVersio