Optimizing Virtual Machine I/O Performance in Virtualized Cloud by Differenciated-frequency Scheduling and Functionality Offloading

Many enterprises are increasingly moving their applications to private cloud environments or public cloud platforms. A key technology driving cloud computing is virtualization which can serve multiple VMs in one physical machine hence providing better management flexibility and significant savings in operational costs. However, one important consequence of virtualized hosts in the cloud is the negative impact it has on the I/O performance of the applications running in the VMs

An interface to implement NUMA policies in the Xen hypervisor

International audienceWhile virtualization only introduces a small overhead on machines with few cores, this is not the case on larger ones. Most of the overhead on the latter machines is caused by the Non-Uniform Memory Access (NUMA) architecture they are using. In order to reduce this overhead, this paper shows how NUMA placement heuristics can be implemented inside Xen. With an evaluation of 29 applications on a 48-core machine, we show that the NUMA placement heuristics can multiply the performance of 9 applications by more than 2

A Performance Evaluation of Hypervisor, Unikernel, and Container Network I/O Virtualization

Hypervisors and containers are the two main virtualization techniques that enable cloud computing. Both techniques have performance overheads on CPU, memory, networking, and disk performance compared to bare metal. Unikernels have recently been proposed as an optimization for hypervisor-based virtualization to reduce performance overheads. In this thesis, we evaluate network I/O performance overheads for hypervisor-based virtualization using Kernel-based Virtual Machine (KVM) and the OSv unikernel and for container-based virtualization using Docker comparing the different configurations and optimizations. We measure the raw networking latency and throughput and CPU utilization by using the Netperf benchmarking tool and measure network intensive application performance using the Memcached key-value store and the Mutilate benchmarking tool. We show that compared to bare metal Linux, Docker with bridged networking has the least performance overhead with OSv using vhost-net coming a close second

Building Computing-As-A-Service Mobile Cloud System

The last five years have witnessed the proliferation of smart mobile devices, the explosion of various mobile applications and the rapid adoption of cloud computing in business, governmental and educational IT deployment. There is also a growing trends of combining mobile computing and cloud computing as a new popular computing paradigm nowadays. This thesis envisions the future of mobile computing which is primarily affected by following three trends: First, servers in cloud equipped with high speed multi-core technology have been the main stream today. Meanwhile, ARM processor powered servers is growingly became popular recently and the virtualization on ARM systems is also gaining wide ranges of attentions recently. Second, high-speed internet has been pervasive and highly available. Mobile devices are able to connect to cloud anytime and anywhere. Third, cloud computing is reshaping the way of using computing resources. The classic pay/scale-as-you-go model allows hardware resources to be optimally allocated and well-managed. These three trends lend credence to a new mobile computing model with the combination of resource-rich cloud and less powerful mobile devices. In this model, mobile devices run the core virtualization hypervisor with virtualized phone instances, allowing for pervasive access to more powerful, highly-available virtual phone clones in the cloud. The centralized cloud, powered by rich computing and memory recourses, hosts virtual phone clones and repeatedly synchronize the data changes with virtual phone instances running on mobile devices. Users can flexibly isolate different computing environments. In this dissertation, we explored the opportunity of leveraging cloud resources for mobile computing for the purpose of energy saving, performance augmentation as well as secure computing enviroment isolation. We proposed a framework that allows mo- bile users to seamlessly leverage cloud to augment the computing capability of mobile devices and also makes it simpler for application developers to run their smartphone applications in the cloud without tedious application partitioning. This framework was built with virtualization on both server side and mobile devices. It has three building blocks including agile virtual machine deployment, efficient virtual resource management, and seamless mobile augmentation. We presented the design, imple- mentation and evaluation of these three components and demonstrated the feasibility of the proposed mobile cloud model

Adaptive runtime techniques for power and resource management on multi-core systems

Energy-related costs are among the major contributors to the total cost of ownership of data centers and high-performance computing (HPC) clusters. As a result, future data centers must be energy-efficient to meet the continuously increasing computational demand. Constraining the power consumption of the servers is a widely used approach for managing energy costs and complying with power delivery limitations. In tandem, virtualization has become a common practice, as virtualization reduces hardware and power requirements by enabling consolidation of multiple applications on to a smaller set of physical resources. However, administration and management of data center resources have become more complex due to the growing number of virtualized servers installed in data centers. Therefore, designing autonomous and adaptive energy efficiency approaches is crucial to achieve sustainable and cost-efficient operation in data centers. Many modern data centers running enterprise workloads successfully implement energy efficiency approaches today. However, the nature of multi-threaded applications, which are becoming more common in all computing domains, brings additional design and management challenges. Tackling these challenges requires a deeper understanding of the interactions between the applications and the underlying hardware nodes. Although cluster-level management techniques bring significant benefits, node-level techniques provide more visibility into application characteristics, which can then be used to further improve the overall energy efficiency of the data centers. This thesis proposes adaptive runtime power and resource management techniques on multi-core systems. It demonstrates that taking the multi-threaded workload characteristics into account during management significantly improves the energy efficiency of the server nodes, which are the basic building blocks of data centers. The key distinguishing features of this work are as follows: We implement the proposed runtime techniques on state-of-the-art commodity multi-core servers and show that their energy efficiency can be significantly improved by (1) taking multi-threaded application specific characteristics into account while making resource allocation decisions, (2) accurately tracking dynamically changing power constraints by using low-overhead application-aware runtime techniques, and (3) coordinating dynamic adaptive decisions at various layers of the computing stack, specifically at system and application levels. Our results show that efficient resource distribution under power constraints yields energy savings of up to 24% compared to existing approaches, along with the ability to meet power constraints 98% of the time for a diverse set of multi-threaded applications

Hypertracing: Tracing through virtualization layers

Cloud computing enables on-demand access to remote computing resources. It provides dynamic scalability and elasticity with a low upfront cost. As the adoption of this computing model is rapidly growing, this increases the system complexity, since virtual machines (VMs) running on multiple virtualization layers become very difficult to monitor without interfering with their performance. In this paper, we present hypertracing, a novel method for tracing VMs by using various paravirtualization techniques, enabling efficient monitoring across virtualization boundaries. Hypertracing is a monitoring infrastructure that facilitates seamless trace sharing among host and guests. Our toolchain can detect latencies and their root causes within VMs, even for boot-up and shutdown sequences, whereas existing tools fail to handle these cases. We propose a new hypervisor optimization, for handling efficient nested paravirtualization, which allows hypertracing to be enabled in any nested environment without triggering VM exit multiplication. This is a significant improvement over current monitoring tools, with their large I/O overhead associated with activating monitoring within each virtualization layer

Doctor of Philosophy

dissertationA modern software system is a composition of parts that are themselves highly complex: operating systems, middleware, libraries, servers, and so on. In principle, compositionality of interfaces means that we can understand any given module independently of the internal workings of other parts. In practice, however, abstractions are leaky, and with every generation, modern software systems grow in complexity. Traditional ways of understanding failures, explaining anomalous executions, and analyzing performance are reaching their limits in the face of emergent behavior, unrepeatability, cross-component execution, software aging, and adversarial changes to the system at run time. Deterministic systems analysis has a potential to change the way we analyze and debug software systems. Recorded once, the execution of the system becomes an independent artifact, which can be analyzed offline. The availability of the complete system state, the guaranteed behavior of re-execution, and the absence of limitations on the run-time complexity of analysis collectively enable the deep, iterative, and automatic exploration of the dynamic properties of the system. This work creates a foundation for making deterministic replay a ubiquitous system analysis tool. It defines design and engineering principles for building fast and practical replay machines capable of capturing complete execution of the entire operating system with an overhead of several percents, on a realistic workload, and with minimal installation costs. To enable an intuitive interface of constructing replay analysis tools, this work implements a powerful virtual machine introspection layer that enables an analysis algorithm to be programmed against the state of the recorded system through familiar terms of source-level variable and type names. To support performance analysis, the replay engine provides a faithful performance model of the original execution during replay

Maruchi OS kankyo o shiensuru sofutowea oyobi hadowea kino no teian

制度:新 ; 報告番号:甲3534号 ; 学位の種類:博士(工学) ; 授与年月日:2012/2/25 ; 早大学位記番号:新587

Efficient shared memory message passing for inter-VM communications

Thanks to recent advances in virtualization technologies, it is now possible to benefit from the flexibility brought by virtual machines at little cost in terms of CPU performance. However on HPC clusters some overheads remain which prevent widespread usage of virtualization. In this article, we tackle the issue of inter-VM MPI communications when VMs are located on the same physical machine. To achieve this we introduce a virtual device which provides a simple message passing API to the guest OS. This interface can then be used to implement an efficient MPI library for virtual machines. The use of a virtual device makes our solution easily portable across multiple guest operating systems since it only requires a small driver to be written for this device. We present an implementation based on Linux, the KVM hypervisor and Qemu as its userspace device emulator. Our implementation achieves near native performance in terms of MPI latency and bandwidth