Defeating network jitter for virtual machines

Virtualization based cloud computing hosts networked applications in virtual machines (VMs), and provides each VM the desired degree of performance isolation using resource isolation mechanisms. Existing isolation solutions address heavily on resource proportionality such as CPU, memory and I/O bandwidth, but seldom focus on resource provisioning rate. Even the VM is allocated with adequate resources, if they can not be provided in a timely manner, problems such as network jitter will be very serious and significantly affect the performance of cloud applications like internet audio/video streaming. This paper systematically analyzes and illustrates the causes of unpredictable network latency in virtualized execution environments. We decouple the design goals of resource proportionality from resource provisioning rate, and adopt divide-and-conquer strategy to defeat network jitter for VMs: (1) in VMM CPU scheduling, we differentiate self-initiated I/O from event-triggered I/O, and individually map them to periodic and aperiodic real-time domains to schedule them together; (2) in network traffic shaping of VMs, we introduce the concept of smooth window to smooth network latency and apply closed-loop feedback control to maintain network resource consumption. We implement our solutions in Xen 4.1.0 and Linux 2.6.32.13. The experimental results with both real-life applications and low-level benchmarks show that our solutions can significantly reduce network jitter, and meanwhile effectively maintain resource proportionality.published_or_final_versionThe 4th IEEE International Conference on Utility and Cloud Computing (UCC 2011), Victoria, NSW, 5-8 December 2011. In Proceedings of the 4th IEEE-UCC, 2011, p. 65-7

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

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

Adaptive Resource Management for Uncertain Execution Platforms

Embedded systems are becoming increasingly complex. At the same time, the components that make up the system grow more uncertain in their properties. For example, current developments in CPU design focuses on optimizing for average performance rather than better worst case performance. This, combined with presence of 3rd party software components with unknown properties, makes resource management using prior knowledge less and less feasible. This thesis presents results on how to model software components so that resource allocation decisions can be made on-line. Both the single and multiple resource case is considered as well as extending the models to include resource constraints based on hardware dynam- ics. Techniques for estimating component parameters on-line are presented. Also presented is an algorithm for computing an optimal allocation based on a set of convex utility functions. The algorithm is designed to be computationally efficient and to use simple mathematical expres- sions that are suitable for fixed point arithmetics. An implementation of the algorithm and results from experiments is presented, showing that an adaptive strategy using both estimation and optimization can outperform a static approach in cases where uncertainty is high

Extensible Performance-Aware Runtime Integrity Measurement

Today\u27s interconnected world consists of a broad set of online activities including banking, shopping, managing health records, and social media while relying heavily on servers to manage extensive sets of data. However, stealthy rootkit attacks on this infrastructure have placed these servers at risk. Security researchers have proposed using an existing x86 CPU mode called System Management Mode (SMM) to search for rootkits from a hardware-protected, isolated, and privileged location. SMM has broad visibility into operating system resources including memory regions and CPU registers. However, the use of SMM for runtime integrity measurement mechanisms (SMM-RIMMs) would significantly expand the amount of CPU time spent away from operating system and hypervisor (host software) control, resulting in potentially serious system impacts. To be a candidate for production use, SMM RIMMs would need to be resilient, performant and extensible. We developed the EPA-RIMM architecture guided by the principles of extensibility, performance awareness, and effectiveness. EPA-RIMM incorporates a security check description mechanism that allows dynamic changes to the set of resources to be monitored. It minimizes system performance impacts by decomposing security checks into shorter tasks that can be independently scheduled over time. We present a performance methodology for SMM to quantify system impacts, as well as a simulator that allows for the evaluation of different methods of scheduling security inspections. Our SMM-based EPA-RIMM prototype leverages insights from the performance methodology to detect host software rootkits at reduced system impacts. EPA-RIMM demonstrates that SMM-based rootkit detection can be made performance-efficient and effective, providing a new tool for defense

Run-time Support for Real-Time Multimedia in the Cloud

REACTION 2013. 2nd International Workshop on Real-time and distributed computing in emerging applications. December 3rd, 2013, Vancouver, Canada.This paper summarizes key research findings in the area of real-time performance and predictabil- ity of multimedia applications in cloud infrastruc- tures, namely: outcomes of the IRMOS European Project, addressing predictability of standard vir- tualized infrastructures; Osprey, an Operating Sys- tem with a novel design suitable for a multitude of heterogeneous workloads including real-time soft- ware; MediaCloud, a novel run-time architecture for offering on-demand multimedia processing facil- ities with unprecedented dynamism and flexibility in resource management. The paper highlights key research challenges ad- dressed by these projects and shortly presents ad- ditional questions lying ahead in this area

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

MorphoSys: efficient colocation of QoS-constrained workloads in the cloud

In hosting environments such as IaaS clouds, desirable application performance is usually guaranteed through the use of Service Level Agreements (SLAs), which specify minimal fractions of resource capacities that must be allocated for unencumbered use for proper operation. Arbitrary colocation of applications with different SLAs on a single host may result in inefficient utilization of the host’s resources. In this paper, we propose that periodic resource allocation and consumption models -- often used to characterize real-time workloads -- be used for a more granular expression of SLAs. Our proposed SLA model has the salient feature that it exposes flexibilities that enable the infrastructure provider to safely transform SLAs from one form to another for the purpose of achieving more efficient colocation. Towards that goal, we present MORPHOSYS: a framework for a service that allows the manipulation of SLAs to enable efficient colocation of arbitrary workloads in a dynamic setting. We present results from extensive trace-driven simulations of colocated Video-on-Demand servers in a cloud setting. These results show that potentially-significant reduction in wasted resources (by as much as 60%) are possible using MORPHOSYS.National Science Foundation (0720604, 0735974, 0820138, 0952145, 1012798