Cache-aware Interfaces for Compositional Real-Time Systems

Interface-based compositional analysis is by now a fairly established area of research in real-time systems. However, current research has not yet fully considered practical aspects, such as the effects of cache interferences on multicore platforms. This position paper discusses the analysis challenges and motivates the need for cache scheduling in this setting, and it highlights several research questions towards cache-aware interfaces for compositional systems on multicore platforms

vCAT: Dynamic Cache Management Using CAT Virtualization

This paper presents vCAT, a novel design for dynamic shared cache management on multicore virtualization platforms based on Intel’s Cache Allocation Technology (CAT). Our design achieves strong isolation at both task and VM levels through cache partition virtualization, which works in a similar way as memory virtualization, but has challenges that are unique to cache and CAT. To demonstrate the feasibility and benefits of our design, we provide a prototype implementation of vCAT, and we present an extensive set of microbenchmarks and performance evaluation results on the PARSEC benchmarks and synthetic workloads, for both static and dynamic allocations. The evaluation results show that (i) vCAT can be implemented with minimal overhead, (ii) it can be used to mitigate shared cache interference, which could have caused task WCET increased by up to 7.2 x, (iii) static management in vCAT can increase system utilization by up to 7 x compared to a system without cache management; and (iv) dynamic management substantially outperforms static management in terms of schedulable utilization (increase by up to 3 x in our multi-mode example use case)

Overhead-Aware Compositional Analysis of Real-Time Systems

Over the past decade, interface-based compositional schedulability analysis has emerged as an effective method for guaranteeing real-time properties in complex systems. Several interfaces and interface computation methods have been developed, and they offer a range of tradeoffs between the complexity and the accuracy of the analysis. However, none of the existing methods consider platform overheads in the component interfaces. As a result, although the analysis results are sound in theory, the systems may violate their timing constraints when running on realistic platforms. This is due to various overheads, such as task release delays, interrupts, cache effects, and context switches. Simple solutions, such as increasing the interface budget or the tasks’ worst-case execution times by a fixed amount, are either unsafe (because of the overhead accumulation problem) or they waste a lot of resources. In this paper, we present an overhead-aware compositional analysis technique that can account for platform overheads in the representation and computation of component interfaces. Our technique extends previous overhead accounting methods, but it additionally addresses the new challenges that are specific to the compositional scheduling setting. To demonstrate that our technique is practical, we report results from an extensive evaluation on a realistic platform

Self-Adapting MAC Layer for Wireless Sensor Networks

The integration of wireless sensors with mobile phones is gaining momentum as an enabling platform for numerous emerging applications. These mobile systems face dynamic environments where both application requirements and ambient wireless conditions change frequently. Despite the existence of many MAC protocols however, none can provide optimal performance along multiple dimensions, in particular when the conditions are frequently changing. Instead of pursuing a one-MAC-fit all approach we present a Self-Adapting MAC Layer (SAML) comprising (1) a Reconfigurable MAC Architecture (RMA) that can switch to different MAC protocols at run time and (2) a learning-based MAC Selection Engine that selects the protocol most suitable for the current condition and requirements. As the ambient conditions or application requirements change SAML dynamically switches MAC protocols to gain the desired performance. To the application SAML appears as a traditional MAC protocol and its benefits are realized without troubling the application with the underlying complexity. To test the system we implement SAML in TinyOS 2.x and realize three prototypes containing up to five MACs. We evaluate the system in controlled tests and real-world environments using a new gateway device that integrates a 802.15.4 radio with Android phones. Our experimental results show that SAML provides an efficient and reliable MAC switching, while adheres to the application specified requirements

Cache-Aware Compositional Analysis of Real-Time Multicore Virtualization Platforms

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 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 workload

Cache-Aware Compositional Analysis of Real-Time Multicore Virtualization Platforms

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 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

Holistic resource allocation for multicore real-time systems

This paper presents CaM, a holistic cache and memory bandwidth resource allocation strategy for multicore real-time systems. CaM is designed for partitioned scheduling, where tasks are mapped onto cores, and the shared cache and memory bandwidth resources are partitioned among cores to reduce resource interferences due to concurrent accesses. Based on our extension of LITMUSRT with Intel’s Cache Allocation Technology and MemGuard, we present an experimental evaluation of the relationship between the allocation of cache and memory bandwidth resources and a task’s WCET. Our resource allocation strategy exploits this relationship to map tasks onto cores, and to compute the resource allocation for each core. By grouping tasks with similar characteristics (in terms of resource demands) to the same core, it enables tasks on each core to fully utilize the assigned resources. In addition, based on the tasks’ execution time behaviors with respect to their assigned resources, we can determine a desirable allocation that maximizes schedulability under resource constraints. Extensive evaluations using real-world benchmarks show that CaM offers near optimal schedulability performance while being highly efficient, and that it substantially outperforms existing solutions

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

RT-OpenStack: CPU Resource Management for Real-Time Cloud Computing

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 pro per 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

RT-OpenStack: a Real-Time Cloud Management System

Clouds have become appealing platforms for running not only general-purpose applications but also real-time applications. However, current clouds cannot provide real-time performance for virtual machines (VM) for two reasons: (1) the lack of a real-time virtual machine monitor (VMM) scheduler on a single host, and (2) the lack of a real-time aware VM placement scheme by the cloud manager. While real-time VM schedulers do exist, prior solutions employ either heuristics-based approaches that cannot always achieve predictable latency or apply real-time scheduling theory that may result in low CPU utilization. We observe the demand and advantage for co-hosting real-time (RT) VMs with non-real-time (regular) VMs in the same cloud. On the one hand, RT VMs can benefit from the easily deployed, elastic resource provisioning provided by a cloud; on the other hand, regular VMs can fully utilize the cloud without affecting the performance of RT VMs through proper resource management at both the cloud and hypervisor levels. This paper presents RT-OpenStack, a cloud management system for co-hosting both 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) an extension of the RT-Xen VM scheduler to allow regular VMs to share hosts with RT VMs without jeopardizing 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 among regular VMs. Experimental results demonstrate that RTOpenStack can support latency guarantees for RT VMs, and at the same time let regular VMs fully utilize the remaining CPU resources