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

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

Calidad de servicio en computación en la nube: técnicas de modelado y sus aplicaciones

Recent years have seen the massive migration of enterprise applications to the cloud. One of the challenges posed by cloud applications is Quality-of-Service (QoS) management, which is the problem of allocating resources to the application to guarantee a service level along dimensions such as performance, availability and reliability. This paper aims at supporting research in this area by providing a survey of the state of the art of QoS modeling approaches suitable for cloud systems. We also review and classify their early application to some decision-making problems arising in cloud QoS management

Effective Resource and Workload Management in Data Centers

The increasing demand for storage, computation, and business continuity has driven the growth of data centers. Managing data centers efficiently is a difficult task because of the wide variety of datacenter applications, their ever-changing intensities, and the fact that application performance targets may differ widely. Server virtualization has been a game-changing technology for IT, providing the possibility to support multiple virtual machines (VMs) simultaneously. This dissertation focuses on how virtualization technologies can be utilized to develop new tools for maintaining high resource utilization, for achieving high application performance, and for reducing the cost of data center management.;For multi-tiered applications, bursty workload traffic can significantly deteriorate performance. This dissertation proposes an admission control algorithm AWAIT, for handling overloading conditions in multi-tier web services. AWAIT places on hold requests of accepted sessions and refuses to admit new sessions when the system is in a sudden workload surge. to meet the service-level objective, AWAIT serves the requests in the blocking queue with high priority. The size of the queue is dynamically determined according to the workload burstiness.;Many admission control policies are triggered by instantaneous measurements of system resource usage, e.g., CPU utilization. This dissertation first demonstrates that directly measuring virtual machine resource utilizations with standard tools cannot always lead to accurate estimates. A directed factor graph (DFG) model is defined to model the dependencies among multiple types of resources across physical and virtual layers.;Virtualized data centers always enable sharing of resources among hosted applications for achieving high resource utilization. However, it is difficult to satisfy application SLOs on a shared infrastructure, as application workloads patterns change over time. AppRM, an automated management system not only allocates right amount of resources to applications for their performance target but also adjusts to dynamic workloads using an adaptive model.;Server consolidation is one of the key applications of server virtualization. This dissertation proposes a VM consolidation mechanism, first by extending the fair load balancing scheme for multi-dimensional vector scheduling, and then by using a queueing network model to capture the service contentions for a particular virtual machine placement

Global Data Plane: A Widely Distributed Storage and Communication Infrastructure

With the advancement of technology, richer computation devices are making their way into everyday life. However, such smarter devices merely act as a source and sink of information; the storage of information is highly centralized in data-centers in today’s world. Even though such data-centers allow for amortization of cost per bit of information, the density and distribution of such data-centers is not necessarily representative of human population density. This disparity of where the information is produced and consumed vs where it is stored only slightly affects the applications of today, but it will be the limiting factor for applications of tomorrow.The computation resources at the edge are more powerful than ever, and present an opportunity to address this disparity. We envision that a seamless combination of these edge-resources with the data-center resources is the way forward. However, the resulting issues of trust and data-security are not easy to solve in a world full of complexity. Toward this vision of a federated infrastructure composed of resources at the edge as well as those in data-centers, we describe the architecture and design of a widely distributed system for data storage and communication that attempts to alleviate some of these data security challenges; we call this system the Global Data Plane (GDP).The key abstraction in the GDP is a secure cohesive container of information called a DataCapsule, which provides a layer of uniformity on top of a heterogeneous infrastructure. A DataCapsule represents a secure history of transactions in a persistent form that can be used for building other applications on top. Existing applications can be refactored to use DataCapsules as the ground truth of persistent state; such a refactoring enables cleaner application design that allows for better security analysis of information flows. Not only cleaner design, the GDP also enables locality of access for performance and data privacy—an ever growing concern in the information age.The DataCapsules are enabled by an underlying routing fabric, called the GDP network, which provides secure routing for datagrams in a flat namespace. The GDP network is a core component of the GDP that enables various GDP components to interact with each other. In addition to the DataCapsules, this underlying network is available to applications for native communication as well. Flat namespace networks are known to provide a number of desirable properties, such as location independence, built-in multicast, etc. However, existing architectures for such networks suffer from routing security issues, typically because malicious entities can claim to possess arbitrary names and thus, receive traffic intended for arbitrary destinations. GDP network takes a different approach by defining an ownership of the name and the associated mechanisms for participants to delegate routing for such names to others. By directly integrating with GDP network, applications can enjoy the benefits of flat namespace networks without compromising routing security.The Global Data Plane and DataCapsules together represent our vision for secure ubiquitous storage. As opposed to the current approach of perimeter security for infrastructure, i.e. drawing a perimeter around parts of infrastructure and trusting everything inside it, our vision is to use cryptographic tools to enable intrinsic security for the information itself regardless of the context in which such information lives. In this dissertation, we show how to make this vision a reality, and how to adapt real world applications to reap the benefits of secure ubiquitous storage