A survey of memory management techniques in virtualized systems

Virtualization technology allows multiple operating systems to share hardware resources of a computer system in an isolated manner. Traditionally, memory is shared by an operating system using segmentation and paging techniques. With virtualization, memory partitioning and management has several new challenges. For isolated and safe execution, hypervisors do not provide direct access to hardware resources. Lack of direct access to the memory management hardware like page tables disqualifies direct usage of virtual memory solutions used on native (non-virtualized) setups. Further, aspects of dual control of the memory resource (by the guest OS and the hypervisor) and lack of semantics regarding memory usage in virtual machines present additional challenges for memory management. This paper surveys different techniques of memory partitioning and management across multiple guest OSs in a virtualized environment.An important goal of virtualization is to increase the physical machine utilization in order to save costs. With varying application demand for memory and diverse memory management policies of the guest OSs, ensuring optimal usage of memory is non-trivial. In this survey, challenges of memory management in virtualized systems, different memory management techniques with their implications, and optimizations to increase memory utilization are discussed in detail.     

VMMB: Virtual Machine Memory Balancing for Unmodified Operating Systems

Virtualization technology has been widely adopted in Internet hosting centers and cloud-based computing services, since it reduces the total cost of ownership by sharing hardware resources among virtual machines (VMs). In a virtualized system, a virtual machine monitor (VMM) is responsible for allocating physical resources such as CPU and memory to individual VMs. Whereas CPU and I/O devices can be shared among VMs in a time sharing manner, main memory is not amendable to such multiplexing. Moreover, it is often the primary bottleneck in achieving higher degrees of consolidation. In this paper, we present VMMB (Virtual Machine Memory Balancer), a novel mechanism to dynamically monitor the memory demand and periodically re-balance the memory among the VMs. VMMB accurately measures the memory demand with low overhead and effectively allocates memory based on the memory demand and the QoS requirement of each VM. It is applicable even to guest OS whose source code is not available, since VMMB does not require modifying guest kernel. We implemented our mechanism on Linux and experimented on synthetic and realistic workloads. Our experiments show that VMMB can improve performance of VMs that suffers from insufficient memory allocation by up to 3.6 times with low performance overhead (below 1%) for monitoring memory demand.     

基于微内核的虚拟机I/O安全机制

NOVA等微内核虚拟化架构解决了宏内核平台可信计算基体积和攻击面过大的问题, 但其仍缺乏虚拟机分等级保护和I/O资源访问控制等安全机制. 本文提出了安全域的概念, 并将虚拟机划分至不同的安全域, 进而建立可定制的I/O资源访问控制机制. 通过将访问控制模块添加至I/O资源访问的关键代码路径, 实现了不同安全域的I/O资源访问控制. 实验表明, 该机制提高了数据的隔离性与安全性, 仅对计算密集型、I/O密集型任务造成了较小的性能损耗.     

Power-efficient and high-performance block I/O framework for mobile virtualization systems

A virtualized system generally suffers from low I/O performance, mainly caused by its inherent abstraction overhead and frequent CPU transitions between the guest and hypervisor modes. The recent research of polling-based I/O virtualization partly solved the problem, but excessive polling trades intensive CPU usage for higher performance. This article presents a power-efficient and high-performance block I/O framework for a virtual machine, which allows us to use it even with a limited number of CPU cores in mobile or embedded systems. Our framework monitors system status, and dynamically switches the I/O process mode between the exit and polling modes, depending on the amounts of current I/O requests and CPU utilization. It also dynamically controls the polling interval to reduce redundant polling. The highly dynamic nature of our framework leads to improvements in I/O performance with lower CPU usage as well. Our experiments showed that our framework outperformed the existing exit-based mechanisms by 10.8 % higher I/O throughput, maintaining similar CPU usage by only 3.1 % increment. In comparison to the systems solely based on the polling mechanism, ours reduced the CPU usage roughly down to 10.0 % with no or negligible performance loss.     

基于Xen的虚拟磁盘调度算法改进

Xen目前所采用的I/O调度算法能够较好的保证公平性;但在实际应用中,不同的虚拟机可能有不同的性能需求。该文研究了Xen虚拟存储的实现和I/O调度算法的原理,提出了基于反馈的动态优先级调度策略,通过对比测试验证了磁盘带宽在不同虚拟机之间的按需分配。     

Effectively deploying services on virtualization infrastructure

Virtualization technology provides an opportunity to acieve efficient usage of computing resources. However, the management of services on virtualization infrastructure is still in the preliminary stage. Contstructing user service environments quickly and efficiently remains a challenge. This paper presents a service oriented multiple-VM deployment system (SO-MVDS) for creating and configuring virtual appliances running services on-demand. The system provides a template management model where all the virtual machines are created based on the templates with the software environment pre-prepared. To improve the deployment performance, we explore some strategies for incremental mechanisms and deployment.We also design a service deployment mechanism to dynamically and automatically deploy multiple services within virtual appliances. We evaluate both the deployment time and I/O performance using the proposed incremental mechanism. The experimental results show that the incremental mechanism outperforms the clone one.     

负载类型相关的Xen虚拟机系统性能模型

针对Xen虚拟机系统执行网络I/O密集型负载时容易耗尽Domain0的CPU资源而过载和执行计算密集型负载时在客户域平均性能与数目之间存在线性规划的问题,提出了两个负载类型相关的性能模型。首先,通过分析Xen虚拟机系统处理网络I/O操作的CPU资源消耗规律,建立了CPU核共享和CPU核隔离两种情况下的客户域网络I/O操作请求次数计算模型;然后,通过分析多个相同客户域并行执行计算密集型负载的平均性能与一个相同客户域执行相同负载的性能表现之间的关系,建立了并行执行计算密集型负载的客户域平均性能分析模型。实验结果表明,两个性能模型能够有效地限制客户域提交的网络I/O操作请求次数以防止Xen虚拟机系统过载,并求解给定资源配置情况下执行计算密集型负载的Xen虚拟机系统客户域伸缩性数目。     

Opportunity cost algorithms for reduction of I/O and interprocess communication overhead in a computing cluster

Computing clusters (CC) consisting of several connected machines, could provide a high-performance, multiuser, timesharing environment for executing parallel and sequential jobs. In order to achieve good performance in such an environment, it is necessary to assign processes to machines in a manner that ensures efficient allocation of resources among the jobs. The paper presents opportunity cost algorithms for online assignment of jobs to machines in a CC. These algorithms are designed to improve the overall CPU utilization of the cluster and to reduce the I/O and the interprocess communication (IPC) overhead. Our approach is based on known theoretical results on competitive algorithms. The main contribution of the paper is how to adapt this theory into working algorithms that can assign jobs to machines in a manner that guarantees near-optimal utilization of the CPU resource for jobs that perform I/O and IPC operations. The developed algorithms are easy to implement. We tested the algorithms by means of simulations and executions in a real system and show that they outperform existing methods for process allocation that are based on ad hoc heuristics.     

Dynamic cluster resource allocations for jobs with known andunknown memory demands

The cluster system we consider for load sharing is a compute farm which is a pool of networked server nodes providing high-performance computing for CPU-intensive, memory-intensive, and I/O active jobs in a batch mode. Existing resource management systems mainly target at balancing the usage of CPU loads among server nodes. With the rapid advancement of CPU chips, memory and disk access speed improvements significantly lag behind advancement of CPU speed, increasing the penalty for data movement, such as page faults and I/O operations, relative to normal CPU operations. Aiming at reducing the memory resource contention caused by page faults and I/O activities, we have developed and examined load sharing policies by considering effective usage of global memory in addition to CPU load balancing in clusters. We study two types of application workloads: 1) Memory demands are known in advance or are predictable and 2) memory demands are unknown and dynamically changed during execution. Besides using workload traces with known memory demands, we have also made kernel instrumentation to collect different types of workload execution traces to capture dynamic memory access patterns. Conducting different groups of trace-driven simulations, we show that our proposed policies can effectively improve overall job execution performance by well utilizing both CPU and memory resources with known and unknown memory demands     

云数据中心 I/O 资源池化

近年来,云计算的发展为数据中心带来了新的应用场景和需求.其中,虚拟化作为云服务的重要使能技术,对数据中心服务器I/O系统的性能、扩展性和设备种类多样性提出了更高的要求,沿用传统设备与服务器紧耦合的I/O架构将会导致资源冗余,数据中心服务器密度降低,布线复杂度增加等诸多问题.因此,文章围绕I/O资源池化架构的实现机制和方法展开研究,目标是解除设备与服务器之间的绑定关系,实现接入服务器对I/O资源的按需弹性化使用,从根本上解决云计算数据中心的I/O系统问题.同时,还提出了一种基于单根I/O虚拟化协议实现多根I/O资源池化的架构,该架构通过硬件的外设部件高速互连接口多根域间地址和标识符映射机制,实现了多个物理服务器对同一I/O设备的共享复用;通过虚拟I/O设备热插拔技术和多根共享管理机制,实现了虚拟I/O资源在服务器间的实时动态分配;采用现场可编程门阵列(Field-Programmable Gate Array)构建了该架构的原型系统.结果表明,该架构能够为各个共享服务器提供良好的I/O操作性能.     

Transparently bridging semantic gap in CPU management for virtualized environments

Consolidated environments are progressively accommodating diverse and unpredictable workloads in conjunction with virtual desktop infrastructure and cloud computing. Unpredictable workloads, however, aggravate the semantic gap between the virtual machine monitor and guest operating systems, leading to inefficient resource management. In particular, CPU management for virtual machines has a critical impact on I/O performance in cases where the virtual machine monitor is agnostic about the internal workloads of each virtual machine. This paper presents virtual machine scheduling techniques for transparently bridging the semantic gap that is a result of consolidated workloads. To enable us to achieve this goal, we ensure that the virtual machine monitor is aware of task-level I/O-boundedness inside a virtual machine using inference techniques, thereby improving I/O performance without compromising CPU fairness. In addition, we address performance anomalies arising from the indirect use of I/O devices via a driver virtual machine at the scheduling level. The proposed techniques are implemented on the Xen virtual machine monitor and evaluated with micro-benchmarks and real workloads on Linux and Windows guest operating systems.     

Quantitative characterization and analysis of the I/O behavior of acommercial distributed-shared-memory machine

This paper presents a unified evaluation of the I/O behavior of a commercial clustered DSM machine, the HP Exemplar. Our study has the following objectives: 1) To evaluate the impact of different interacting system components, namely, architecture, operating system, and programming model, on the overall I/O behavior and identify possible performance bottlenecks, and 2) To provide hints to the users for achieving high out-of-box I/O throughput. We find that for the DSM machines that are built as a cluster of SMP nodes, integrated clustering of computing and I/O resources, both hardware and software, is not advantageous for two reasons. First, within an SMP node, the I/O bandwidth is often restricted by the performance of the peripheral components and cannot match the memory bandwidth. Second, since the I/O resources are shared as a global resource, the file-access costs become nonuniform and the I/O behavior of the entire system, in terms of both scalability and balance, degrades. We observe that the buffered I/O performance is determined not only by the I/O subsystem, but also by the programming model, global-shared memory subsystem, and data-communication mechanism. Moreover, programming-model support can be used effectively to overcome the performance constraints created by the architecture and operating system. For example, on the HP Exemplar, users can achieve high I/O throughput by using features of the programming model that balance the sharing and locality of the user buffers and file systems. Finally, we believe that at present, the I/O subsystems are being designed in isolation, and there is a need for mending the traditional memory-oriented design approach to address this problem     

A disk bandwidth allocation mechanism with priority

Virtualization is a popular technology. Services and applications running on each virtual machine have to compete with each other for limited physical computer or network resources. Each virtual machine has different I/O requirement and special priority. Without proper scheduling resource management, a load surge in a virtual machine may inevitably degrade other’s performance. In addition, each virtual machine may run different kinds of application, which have different disk bandwidth demands and service priorities. When assigning I/O resources, we should deal with each case on demand. In this paper, we propose a dynamic virtual machine disk bandwidth control mechanism in virtualization environment. A Disk Credit Algorithm is introduced to support a fine-gained disk bandwidth allocation mechanism among virtual machines. We can assign disk bandwidth according to each virtual machine’s service priority/weight and its requirement. Related experiments show that the mechanism can improve the VMs’ isolation and guarantee the performance of the specific virtual machine well.     

A model of checkpoint behavior for applications that have I/O

Due to the increase and complexity of computer systems, reducing the overhead of fault tolerance techniques has become important in recent years. One technique in fault tolerance is checkpointing, which saves a snapshot with the information that has been computed up to a specific moment, suspending the execution of the application, consuming I/O resources and network bandwidth. Characterizing the files that are generated when performing the checkpoint of a parallel application is useful to determine the resources consumed and their impact on the I/O system. It is also important to characterize the application that performs checkpoints, and one of these characteristics is whether the application does I/O. In this paper, we present a model of checkpoint behavior for parallel applications that performs I/O; this depends on the application and on other factors such as the number of processes, the mapping of processes and the type of I/O used. These characteristics will also influence scalability, the resources consumed and their impact on the IO system. Our model describes the behavior of the checkpoint size based on the characteristics of the system and the type (or model) of I/O used, such as the number I/O aggregator processes, the buffering size utilized by the two-phase I/O optimization technique and components of collective file I/O operations. The BT benchmark and FLASH I/O are analyzed under different configurations of aggregator processes and buffer size to explain our approach. The model can be useful when selecting what type of checkpoint configuration is more appropriate according to the applications’ characteristics and resources available. Thus, the user will be able to know how much storage space the checkpoint consumes and how much the application consumes, in order to establish policies that help improve the distribution of resources.

     

Toward cost-effective storage provisioning for DBMSs

Data center operators face a bewildering set of choices when considering how to provision resources on machines with complex I/O subsystems. Modern I/O subsystems often have a rich mix of fast, high performing, but expensive SSDs sitting alongside with cheaper but relatively slower (for random accesses) traditional hard disk drives. The data center operators need to determine how to provision the I/O resources for specific workloads so as to abide by existing service level agreements, while minimizing the total operating cost (TOC) of running the workload, where the TOC includes the amortized hardware costs and the run-time energy costs. The focus of this paper is on introducing this new problem of TOC-based storage allocation, cast in a framework that is compatible with traditional DBMS query optimization and query processing architecture. We also present a heuristic-based solution to this problem, called DOT. We have implemented DOT in PostgreSQL, and experiments using TPC-H and TPC-C demonstrate significant TOC reduction by DOT in various settings.     

SDFS: A software-defined file system for multitenant cloud storage

Performance isolation is highly desirable in cloud platforms where the virtual disks of virtual machines are simply large files on the shared and networked storage servers. However, existing isolation techniques cannot deal with the implications of the file system used by the networked storage servers, such that underlying resource usage is unpredictable (eg, the delayed write-back mechanism could postpone writes, and the journaling mechanism could amplify writes). The lack of visibility on underlying resource usage leads to the predicament of being unable to meet isolation goals. In this paper, we present a software-defined file system (SDFS) that exploits the underlying file system to allocate resources at per-image-file granularity and provide tenants with guaranteed throughput. The SDFS comprises two components: control plane and data plane. At the control plane, we provide a set of system calls to document tenant performance requirements into the metadata of image files. At the data plane, we construct a file-based scheduler to manage memory and disk resources according to the tenant performance requirements. The SDFS design does not require a modification to guest operating systems, hypervisors, or file server protocols. Through a prototype implementation, we demonstrate that the SDFS can meet isolation goals and increase resource utilization with negligible overhead.     

A work-efficient distributed algorithm for reachability analysis

This work presents a novel distributed symbolic algorithm for reachability analysis that can effectively exploit, as needed, a large number of machines working in parallel. The novelty of the algorithm is in its dynamic allocation and reallocation of processes to tasks and in its mechanism for recovery from local state explosion. As a result, the algorithm is work-efficient: it utilizes only those resources that are actually needed. In addition, its high adaptability makes it suitable for exploiting the resources of very large and heterogeneous distributed, nondedicated environments. Thus, it suitable for verifying very large systems. We implemented our algorithm in a tool called Division. Our experimental results show that the algorithm is indeed work-efficient. Although the goal of this research is to check larger models, the results also indicate that the algorithm can obtain high speedups, because communication overhead is very small.     

Automatic Skeleton-Driven Memory Affinity for Transactional Worklist Applications

Memory affinity has become a key element to achieve scalable performance on multi-core platforms. Mechanisms such as thread scheduling, page allocation and cache prefetching are commonly employed to enhance memory affinity which keeps data close to the cores that access it. In particular, software transactional memory (STM) applications exhibit irregular memory access behavior that makes harder to determine which and when data will be needed by each core. Additionally, existing STM runtime systems are decoupled from issues such as thread and memory management. In this paper, we thus propose a skeleton-driven mechanism to improve memory affinity on STM applications that fit the worklist pattern employing a two-level approach. First, it addresses memory affinity in the DRAM level by automatic selecting page allocation policies. Then it employs data prefetching helper threads to improve affinity in the cache level. It relies on a skeleton framework to exploit the application pattern in order to provide automatic memory page allocation and cache prefetching. Our experimental results on the STAMP benchmark suite show that our proposed mechanism can achieve performance improvements of up to 46 %, with an average of 11 %, over a baseline version on two NUMA multi-core machines.     

A mechanism for achieving a bound on execution performance of process group to limit CPU abuse

The secure OS has been the focus of several studies. However, CPU resources, which are important resources for executing a program, are not the object of access control in secure OS. For preventing the abuse of CPU resources, we had earlier proposed a new type of execution resource that controls the maximum CPU usage (Tabata et al. in Int. J. Smart Home 1(2):109–128, 2007). The previously proposed mechanism can control only one process at a time. Because most services involve multiple processes, the mechanism should control all the processes in each service. In this paper, we propose an improved mechanism that helps to achieve a bound on the execution performance of a process group in order to limit unnecessary processor usage. We report the results of an evaluation of our proposed mechanism.     

Scalable Design and Implementations for MPI Parallel Overlapping I/O

We investigate the message passing interface input/output (MPI I/O) implementation issues for two overlapping access patterns: the overlaps among processes within a single I/O operation and the overlaps across a sequence of I/O operations. The former case considers whether I/O atomicity can be obtained in the overlapping regions. The latter focuses on the file consistency problem on parallel machines with client-side file caching enabled. Traditional solutions for both overlapping I/O problems use whole file or byte-range file locking to ensure exclusive access to the overlapping regions and bypass the file system cache. Unfortunately, not only can file locking serialize I/O, but it can also increase the aggregate communication overhead between clients and I/O servers. For atomicity, we first differentiate MPI's requirements from the portable operating system interface (POSIX) standard and propose two scalable approaches, graph coloring and process-rank ordering, which can resolve access conflicts and maintain I/O parallelism. For solving the file consistency problem across multiple I/O operations, we propose a method called persistent file domains, which tackles cache coherency with additional information and coordination to guarantee safe cache access without using file locks