Recent years have witnessed two major trends in the development of complex real-time embedded systems. First, to reduce cost and enhance flexibility, multiple systems are sharing common computing platforms via virtualization technology, instead of being deployed separately on physically isolated hosts. Second, multicore processors are increasingly being used in real-time systems. The integration of real-time systems as virtual machines (VMs) atop common multicore platforms raises significant new research challenges in meeting the real-time performance requirements of multiple systems. This paper advances the state of the art in real-time virtualization by designing and implementing RTXen 2.0, a new real-time multicore VM scheduling framework in the popular Xen virtual machine monitor (VMM). RT-Xen 2.0 realizes a suite of real-time VM scheduling policies spanning the design space. We implement both global and partitioned VM schedulers; each scheduler can be configured to support dynamic or static priorities and to run VMs as periodic or deferrable servers. We present a comprehensive experimental evaluation that provides important insights into real-time scheduling on virtualized multicore platforms: (1) both global and partitioned VM scheduling can be implemented in the VMM at moderate overhead; (2) at the VMM level, while compositional scheduling theory shows partitioned EDF (pEDF) is better than global EDF (gEDF) in providing schedulability guarantees, in our experiments their performance is reversed in terms of the fraction of workloads that meet their deadlines on virtualized multicore platforms; (3) at the guest OS level, pEDF requests a smaller total VCPU bandwidth than gEDF based on compositional scheduling analysis, and therefore using pEDF at the guest OS level leads to more schedulable workloads in our experiments; (4) a combination of pEDF in the guest OS and gEDF in the VMM -configured with deferrable server -leads to the highest fraction of schedulable task sets compared to other real-time VM scheduling policies; and (5) on a platform with a shared last-level cache, the benefits of global scheduling outweigh the cache penalty incurred by VM migration.
We present a co-designed scheduling framework and platform architecture that together support compositional scheduling of real-time systems. The architecture is built on the Xen virtualization platform, and relies on compositional scheduling theory that uses periodic resource models as component interfaces.We implement resource models as periodic servers and consider enhancements to periodic server design that significantly improve response times of tasks and resource utilization in the system while preserving theoretical schedulability results. We present an extensive evaluation of our implementation using workloads from an avionics case study as well as synthetic ones. Comments
Abstract-Clouds have become appealing platforms for not only general-purpose applications, but also real-time ones. However, current clouds cannot provide real-time performance to virtual machines (VMs). We observe the demand and the advantage of co-hosting real-time (RT) VMs with non-real-time (regular) VMs in a same cloud. RT VMs can benefit from the easily deployed, elastic resource provisioning provided by the cloud, while regular VMs effectively utilize remaining resources without affecting the performance of RT VMs through proper resource management at both the cloud and the hypervisor levels. This paper presents RT-OpenStack, a cloud CPU resource management system for co-hosting real-time and regular VMs. RT-OpenStack entails three main contributions: (1) integration of a real-time hypervisor (RT-Xen) and a cloud management system (OpenStack) through a real-time resource interface; (2) a realtime VM scheduler to allow regular VMs to share hosts with RT VMs without interfering the real-time performance of RT VMs; and (3) a VM-to-host mapping strategy that provisions real-time performance to RT VMs while allowing effective resource sharing with regular VMs. Experimental results demonstrate that RTOpenStack can effectively improve the real-time performance of RT VMs while allowing regular VMs to fully utilize the remaining CPU resources.
Multicore processors are becoming ubiquitous, and it is becoming increasingly common to run multiple realtime systems on a shared multicore platform. While this trend helps to reduce cost and to increase performance, it also makes it more challenging to achieve timing guarantees and functional isolation.One approach to achieving functional isolation is to use virtualization. However, virtualization also introduces many challenges to the multicore timing analysis; for instance, the overhead due to cache misses becomes harder to predict, since it depends not only on the direct interference between tasks but also on the indirect interference between virtual processors and the tasks executing on them.In this paper, we present a cache-aware compositional analysis technique that can be used to ensure timing guarantees of components scheduled on a multicore virtualization platform. Our technique improves on previous multicore compositional analyses by accounting for the cache-related overhead in the components' interfaces, and it addresses the new virtualization-specific challenges in the overhead analysis. To demonstrate the utility of our technique, we report results from an extensive evaluation based on randomly generated workloads. Abstract-Multicore processors are becoming ubiquitous, and it is becoming increasingly common to run multiple real-time systems on a shared multicore platform. While this trend helps to reduce cost and to increase performance, it also makes it more challenging to achieve timing guarantees and functional isolation.One approach to achieving functional isolation is to use virtualization. However, virtualization also introduces many challenges to the multicore timing analysis; for instance, the overhead due to cache misses becomes harder to predict, since it depends not only on the direct interference between tasks but also on the indirect interference between virtual processors and the tasks executing on them.In this paper, we present a cache-aware compositional analysis technique that can be used to ensure timing guarantees of components scheduled on a multicore virtualization platform. Our technique improves on previous multicore compositional analyses by accounting for the cache-related overhead in the components' interfaces, and it addresses the new virtualizationspecific challenges in the overhead analysis. To demonstrate the utility of our technique, we report results from an extensive evaluation based on randomly generated workloads.
Abstract-As virtualization technology becomes ever more capable, large-scale distributed applications are increasingly deployed in virtualized environments such as data centers and computational clouds. Many large-scale applications have soft real-time requirements and benefit from low and predictable latency, even in the presence of diverse traffic patterns between virtualized hosts. In this paper, we examine the policies and mechanisms affecting communication latency between virtual machines based on the Xen platform, and identify limitations that could result in long or unpredictable network traffic latencies. To address these limitations, we propose VATC, a VirtualizationAware Traffic Control framework for prioritizing network traffic in virtualized hosts. Results of our experiments show how and why VATC can improve predictability and reduce delay for latency sensitive applications, while introducing limited overhead.
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