Real-Time Virtualization and Cloud Computing

In recent years, we have observed three 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, multi-core processors are increasingly being used in real-time systems. Third, developers are exploring the possibilities of deploying real-time applications as virtual machines in a public cloud. The integration of real-time systems as virtual machines (VMs) atop common multi-core platforms in a public cloud raises significant new research challenges in meeting the real-time latency requirements of applications. In order to address the challenges of running real-time VMs in the cloud, we first present RT-Xen, a novel real-time scheduling framework within the popular Xen hypervisor. We start with single-core scheduling in RT-Xen, and present the first work that empirically studies and compares different real-time scheduling schemes on a same platform. We then introduce RT-Xen 2.0, which focuses on multi-core scheduling and spanning multiple design spaces, including priority schemes, server schemes, and scheduling policies. Experimental results demonstrate that when combined with compositional scheduling theory, RT-Xen can deliver real-time performance to an application running in a VM, while the default credit scheduler cannot. After that, we present RT-OpenStack, a cloud management system designed to support co-hosting real-time and non-real-time VMs in a cloud. RT-OpenStack studies the problem of running real-time VMs together with non-real-time VMs in a public cloud. Leveraging the resource interface and real-time scheduling provided by RT-Xen, RT-OpenStack provides real-time performance guarantees to real-time VMs, while achieving high resource utilization by allowing non-real-time VMs to share the remaining CPU resources through a novel VM-to-host mapping scheme. Finally, we present RTCA, a real-time communication architecture for VMs sharing a same host, which maintains low latency for high priority inter-domain communication (IDC) traffic in the face of low priority IDC traffic

CloudScope: diagnosing and managing performance interference in multi-tenant clouds

© 2015 IEEE.Virtual machine consolidation is attractive in cloud computing platforms for several reasons including reduced infrastructure costs, lower energy consumption and ease of management. However, the interference between co-resident workloads caused by virtualization can violate the service level objectives (SLOs) that the cloud platform guarantees. Existing solutions to minimize interference between virtual machines (VMs) are mostly based on comprehensive micro-benchmarks or online training which makes them computationally intensive. In this paper, we present CloudScope, a system for diagnosing interference for multi-tenant cloud systems in a lightweight way. CloudScope employs a discrete-time Markov Chain model for the online prediction of performance interference of co-resident VMs. It uses the results to optimally (re)assign VMs to physical machines and to optimize the hypervisor configuration, e.g. the CPU share it can use, for different workloads. We have implemented CloudScope on top of the Xen hypervisor and conducted experiments using a set of CPU, disk, and network intensive workloads and a real system (MapReduce). Our results show that CloudScope interference prediction achieves an average error of 9%. The interference-aware scheduler improves VM performance by up to 10% compared to the default scheduler. In addition, the hypervisor reconfiguration can improve network throughput by up to 30%

Limitations and Solutions for Real-Time Local Inter-Domain Communication in Xen

As computer hardware becomes increasingly powerful, there is an ongoing trend towards integrating complex, legacy real-time systems using fewer hosts through virtualization. Especially in embedded systems domains such as avionics and automotive engineering, this kind of system integration can greatly reduce system weight, cost, and power requirements. When systems are integrated in this manner, network communication may become local inter-domain communication (IDC) within the same host. This paper examines the limitations of inter-domain communication in Xen, a widely used open-source virtual machine monitor (VMM) that recently has been extended to support real-time domain scheduling. We find that both the VMM scheduler and the manager domain can significantly impact real-time IDC performance under different conditions, and show that improving the VMM scheduler alone cannot deliver real-time performance for local IDC. To address those limitations, we present the RTCA, a Real-Time Communication Architecture within the manager domain in Xen, along with empirical evaluations whose results demonstrate that the latency of communication tasks can be improved dramatically from ms to μs by a combination of the RTCA and a real-time VMM scheduler

A Survey on Load Balancing Algorithms for VM Placement in Cloud Computing

The emergence of cloud computing based on virtualization technologies brings huge opportunities to host virtual resource at low cost without the need of owning any infrastructure. Virtualization technologies enable users to acquire, configure and be charged on pay-per-use basis. However, Cloud data centers mostly comprise heterogeneous commodity servers hosting multiple virtual machines (VMs) with potential various specifications and fluctuating resource usages, which may cause imbalanced resource utilization within servers that may lead to performance degradation and service level agreements (SLAs) violations. To achieve efficient scheduling, these challenges should be addressed and solved by using load balancing strategies, which have been proved to be NP-hard problem. From multiple perspectives, this work identifies the challenges and analyzes existing algorithms for allocating VMs to PMs in infrastructure Clouds, especially focuses on load balancing. A detailed classification targeting load balancing algorithms for VM placement in cloud data centers is investigated and the surveyed algorithms are classified according to the classification. The goal of this paper is to provide a comprehensive and comparative understanding of existing literature and aid researchers by providing an insight for potential future enhancements.Comment: 22 Pages, 4 Figures, 4 Tables, in pres

Developing power‐aware scheduling mechanisms for computing systems virtualized by Xen

Cloud computing emerges as one of the most important technologies for interconnecting people and building the so‐called Internet of People (IoP). In such a cloud‐based IoP, the virtualization technique provides the key supporting environments for running the IoP jobs such as performing data analysis and mining personal information. Nowadays, energy consumption in such a system is a critical metric to measure the sustainability and eco‐friendliness of the system. This paper develops three power‐aware scheduling strategies in virtualized systems managed by Xen, which is a popular virtualization technique. These three strategies are the Least performance Loss Scheduling strategy, the No performance Loss Scheduling strategy, and the Best Frequency Match scheduling strategy. These power‐aware strategies are developed by identifying the limitation of Xen in scaling the CPU frequency and aim to reduce the energy waste without sacrificing the jobs running performance in the computing systems virtualized by Xen. Least performance Loss Scheduling works by re‐arranging the execution order of the virtual machines (VMs). No performance Loss Scheduling works by setting a proper initial CPU frequency for running the VMs. Best Frequency Match reduces energy waste and performance loss by allowing the VMs to jump the queue so that the VM that is put into execution best matches the current CPU frequency. Scheduling for both single core and multicore processors is considered in this paper. The evaluation experiments have been conducted, and the results show that compared with the original scheduling strategy in Xen, the developed power‐aware scheduling algorithm is able to reduce energy consumption without reducing the performance for the jobs running in Xen

Elastic management of tasks in virtualized environments

Nowadays, service providers in the Cloud offer complex services ready to be used as it was a commodity like water or electricity to their customers. A key technology for this approach is virtualization which facilitates provider's management and provides on-demand virtual environments, which are isolated and consolidated in order to achieve a better utilization of the provider's resources. However, dealing with some virtualization capabilities, such as the creation of virtual environments, implies an effort for the user in order to take benefit from them. In order to avoid this problem, we are contributing the research community with the EMOTIVE (Elastic Management of Tasks in Virtualized Environments) middleware, which allows executing tasks and providing virtualized environments to the users without any extra effort in an efficient way. This is a virtualized environment manager which aims to provide virtual machines that fulfils with the user requirements in terms of software and system capabilities. Furthermore, it supports fine-grained local resource management and provides facilities for developing scheduling policies such as migration and checkpointing.Postprint (published version

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

Evaluating I/O Scheduling in Virtual Machines Based on Application Load

In recent years, cloud computing services and virtualization technology have been widely used. Virtualization requires the access to underlying resources to go through a virtualization layer, which reduces the operation efficiency, especially the access to disk I/O will easily become the bottleneck of the whole system. Therefore, how to improve the I/O performance of virtualization applications has become a hot spot in current researches, especially on I/O scheduling algorithm. While the design and selection of traditional I/O scheduling algorithms are greatly restricted by the seek time and latency of the underlying disks, the virtualization layer in a virtual environment to some extent shields the perception of the scheduling algorithm of virtual machines on the characteristics of the underlying hardware. Whether the traditional algorithms are applicable and how the multi-layer I/O scheduling system in virtualization collaborates to better meet the I/O performance requirements have become pressing issues. In this paper, the authors will explain how the I/O scheduler in Linux system works under different application loads in two scenarios (real machine and virtual machine), and take open-source Xen as examples to test and evaluate the influence of combination of the Dom0 scheduling algorithm and the virtual domain scheduling algorithm on I/O performance under different application loads, and then put forward the preferred proposals of I/O scheduler in virtual domains