实时性环境中JVM垃圾收集算法

提出一种适用于实时性环境的Java虚拟机垃圾收集算法。该算法通过对增量式收集器中堆空间划分方式和引用跟踪进行改进,以减少垃圾收集时带来的不确定性暂停,并且该算法可以指定用户在一个时间段内进行垃圾收集,以便使应用程序暂停时间达到最短,从而适用于实时性环境。实验结果表明,该算法能有效减少垃圾收集时暂停的频率和时长。     

Implementation of Java card Virtual Machine

Java card is a new system for programming smart cards,which is based on the Java language and Virtual Machine,Java card programs(applets)run in Java Card Runtime Environment (JCRE)including the java Card Virtual Machine(JCVM),the framework,the assoicated native methods and the API(Application Programming Interface),JCVM is implemented as two separate pieces: off-card VM(Virtual Machine)and on-card VM.The stack model and heap memory structure used by on-car VM and exception handling are introduced.Because there are limited resources wihin smart card environment,and garbage collection is not supported in JCVM,the preferred way to exception handling does not directly involve the use of throw,although the throw keyowrd is supported.Security is the most important feature of smart card.The Java Card applet security feature is also discussed.     

FLSP:一个高效的系统级垃圾收集算法

垃圾收集是Java操作系统的核心功能,它直接影响到整个系统效率。现代Java操作系统中使用的垃圾收集算法普遍还是沿用应用程序级的垃圾收集算法。应用程序级垃圾收集算法的优化主要面向于普通的Java虚拟机。而Ja-va操作系统与Java虚拟机相比有更高的操作权限和更灵活的资源管理策略,如何利用这些特点和权限来提高垃圾收集算法的效率是以前的垃圾收集算法所没有考虑的。本文分析了操作系统下内存管理和垃圾收集的特点,在JUnicorn操作系统上,利用操作系统平台提供的便利,设计并实现了一个高效的系统级垃圾收集算法FLSP。测试数据表明,在操作系统级别,这种垃圾收集算法能够提高13%的系统性能,并且使垃圾收集的停顿时间缩短50%。     

Bayes Theorem Based Virtual Machine Scheduling for Optimal Energy Consumption

This paper proposes an algorithm for scheduling Virtual Machines (VM) with energy saving strategies in the physical servers of cloud data centers. Energy saving strategy along with a solution for productive resource utilization for VM deployment in cloud data centers is modeled by a combination of “Virtual Machine Scheduling using Bayes Theorem” algorithm (VMSBT) and Virtual Machine Migration (VMMIG) algorithm. It is shown that the overall data center’s consumption of energy is minimized with a combination of VMSBT algorithm and Virtual Machine Migration (VMMIG) algorithm. Virtual machine migration between the active physical servers in the data center is carried out at periodical intervals as and when a physical server is identified to be under-utilized. In VM scheduling, the optimal data centers are clustered using Bayes Theorem and VMs are scheduled to appropriate data center using the selection policy that identifies the cluster with lesser energy consumption. Clustering using Bayes rule minimizes the number of server choices for the selection policy. Application of Bayes theorem in clustering has enabled the proposed VMSBT algorithm to schedule the virtual machines on to the physical server with minimal execution time. The proposed algorithm is compared with other energy aware VM allocations algorithms viz. “Ant-Colony” optimization-based (ACO) allocation scheme and “min-min” scheduling algorithm. The experimental simulation results prove that the proposed combination of ‘VMSBT’ and ‘VMMIG’ algorithm outperforms other two strategies and is highly effective in scheduling VMs with reduced energy consumption by utilizing the existing resources productively and by minimizing the number of active servers at any given point of time.     

基于类型的运行时环境存储管理算法

基于类型进行分类管理堆空间的垃圾回收算法通过废弃对象复用来降低运行时环境创建对象所需时间开销,同时还通过线程缓存、租赁等技术进一步增强运行时系统的存储管理效率.运行实验表明,该算法能够在回收线程和工作线程之间实现细粒度的并行性并缩短对象申请和回收时间,进而能够减少工作线程的停顿现象,增加服务器应用的平滑性以及提高堆空间的使用效率.     

基于嵌入式Java虚拟机的垃圾收集优化算法

对分代垃圾收集算法进行分析和改进,提出一种适用于嵌入式Java虚拟机的垃圾收集优化算法。采取动态的分代方式,将旧生代的回收工作细分,从而充分利用堆空间,缩短分代回收中全收集导致的停顿时间。实验结果表明,该算法能保持较高的垃圾收集效率,系统平均停顿时间较少。     

基于Xen虚拟机的内存资源实时监控与按需调整

在虚拟计算环境中,难以实时地监控与分配内存资源。针对以上问题,基于Xen虚拟计算环境,提出一种能够实时监控Xen虚拟机内存(VMM)使用情况的XMMC方法并进行了实现。所提方法运用Xen虚拟机提供的超级调用,其不仅能实时地监控虚拟机内存使用情况,而且能实时动态按需分配虚拟机内存。实验结果表明,XMMC方法对虚拟机应用程序造成的性能损失很小,低于5%;能够对客户虚拟机的内存资源占用情况进行实时的监测与按需调整,为多虚拟机的管理提供方便。     

A study on virtual machine deployment for application outsourcing in mobile cloud computing

In mobile cloud computing, application offloading is implemented as a software level solution for augmenting computing potentials of smart mobile devices. VM is one of the prominent approaches for offloading computational load to cloud server nodes. A challenging aspect of such frameworks is the additional computing resources utilization in the deployment and management of VM on Smartphone. The deployment of Virtual Machine (VM) requires computing resources for VM creation and configuration. The management of VM includes computing resources utilization in the monitoring of VM in entire lifecycle and physical resources management for VM on Smartphone. The objective of this work is to ensure that VM deployment and management requires additional computing resources on mobile device for application offloading. This paper analyzes the impact of VM deployment and management on the execution time of application in different experiments. We investigate VM deployment and management for application processing in simulation environment by using CloudSim, which is a simulation toolkit that provides an extensible simulation framework to model the simulation of VM deployment and management for application processing in cloud-computing infrastructure. VM deployment and management in application processing is evaluated by analyzing VM deployment, the execution time of applications and total execution time of the simulation. The analysis concludes that VM deployment and management require additional resources on the computing host. Therefore, VM deployment is a heavyweight approach for process offloading on smart mobile devices.     

On scheduling garbage collector in dynamic real-time systems with statistical timing assurances

We consider garbage collection (GC) in dynamic real-time systems. We consider the time-based GC approach of running the collector as a separate, concurrent thread, and focus on real-time scheduling to obtain assurances on mutator timing behavior, while ensuring that memory is never exhausted. We present a scheduling algorithm called GCUA. The algorithm considers mutator activities that are subject to time/utility function time constraints, variable execution time demands, the unimodal arbitrary arrival model that allows a strong adversary, and resource overloads. We establish several properties of GCUA including probabilistically-satisfied utility lower bounds for each mutator activity, a lower bound on the system-wide total accrued utility, bounded sensitivity for the assurances to variations in mutator execution time demand estimates, and no memory exhaustion at all times. Our simulation experiments validate our analytical results and confirm the algorithm’s effectiveness and superiority.     

适应广域网的虚拟机在线迁移模型

针对广域网(WAN)环境下虚拟机(VM)迁移存在的诸多问题提出一种迁移模型。该模型持续检测节点间链路状态,优化磁盘、内存和CPU状态及网络的迁移时序,并采用磁盘周期同步、单向网络隧道及虚拟机本地化等方法。实验表明：模型减少了虚拟机跨WAN迁移的数据量,缩短了网络重定向路径,在模拟实验中迁移的总时间及暂停时间接近于共享存储方式。     

A new task scheduling method for distributed programs that require memory management

In parallel and distributed applications, it is very likely that object‐oriented languages, such as Java and Ruby, and large‐scale semistructured data written in XML will be employed. However, because of their inherent dynamic memory management, parallel and distributed applications must sometimes suspend the execution of all tasks running on the processors. This adversely affects their execution on the parallel and distributed platform. In this paper, we propose a new task scheduling method called CP/MM (Critical Path/Memory Management) which can efficiently schedule tasks for applications requiring memory management. The underlying concept is to consider the cost due to memory management when the task scheduling system allocates ready (executable) coarse‐grain tasks, or macro‐tasks, to processors. We have developed three task scheduling modules, including CP/MM, for a task scheduling system which is implemented on a Java RMI (Remote Method Invocation) communication infrastructure. Our experimental results show that CP/MM can successfully prevent high‐priority macro‐tasks from being affected by the garbage collection arising from memory management, so that CP/MM can efficiently schedule distributed programs whose critical paths are relatively long. Copyright © 2005 John Wiley & Sons, Ltd.     

Garbage Collector Scheduling in Dynamic, Multiprocessor Real-Time Systems

We consider garbage collection (GC) in dynamic, multiprocessor real-time systems. We consider the time-based, concurrent GC approach and focus on real-time scheduling to obtain mutator timing assurances, despite memory allocation and garbage collection. We present a scheduling algorithm called GCMUA. The algorithm considers mutator activities that are subject to time/utility function time constraints, stochastic execution-time and memory demands, and overloads. We establish that GCMUA probabilistically lower bounds each mutator activity's accrued utility, lower bounds the system-wide total accrued utility, and upper bounds the timing assurances' sensitivity to variations in mutator execution-time and memory demand estimates. Our simulation experiments validate our analytical results and confirm GCMUA's effectiveness.     

Modelling real-time applications based on resource reservations

This paper presents a strategy for the design of real-time applications relying on the resource-reservation paradigm, based on a new modelling element that describes the schedulable entities of the applications during the whole design cycle. A virtual view of this element is used first for evaluating a set of constraints that guarantee the application schedulability independently of the execution platform. Then, the element is viewed as a thread that receives the particular scheduling parameters required for a schedulable execution of the application in a specific platform. The purpose of the design process is to make both views compatible.     

基于云科学工作流调度的代价与能效优化算法

为了降低云环境中科学工作流调度的执行代价与数据中心能耗,提出了一种基于能效感知的工作流调度代价最优化算法CWCO-EA。算法在满足截止时间约束下,以最小化工作流执行代价与降低能耗为目标,将工作流的任务调度划分为四步执行。首先,通过代价效用的概念设计虚拟机选择策略,实现了子makespan约束下的任务与最优虚拟机间的映射;其次,通过串行与并行任务合并策略,同步降低了工作流的执行代价与能耗;然后,通过空闲虚拟机重用机制,改善了租用虚拟机的利用率,进一步提高了能效;最后,通过任务松驰策略实现了租用虚拟机的能力回收,节省了能耗。通过四种科学工作流的仿真实验,结果表明,CWCO-EA算法比较同类型算法,在满足截止时间的同时,可以同步降低工作流的执行代价与执行能耗。     

无用内存单元自动回收过程的实时性问题研究

自动垃圾回收技术是Java的核心技术之一。垃圾回收耗时的不确定性阻碍了Java在实时过程中的应用。基于最大响应时间分析方法，利用Petri网分析了垃圾回收过程。通过Java虚拟机内部堆空间的合理划分和采用多线程同步技术可以降低垃圾回收中的停滞现象，提高Java程序的执行速度和实时性。     

一种基于资源预分配的虚拟机软实时调度方法

虚拟机技术作为云计算的重要技术之一,近年来得到广泛关注,但是由于虚拟机管理层的存在,导致语义鸿沟,使得实时应用程序、并发程序等在虚拟机上的运行性能受到影响。分析和研究了Xen虚拟机管理器的Credit调度算法,针对其在并发调度和软实时调度方面存在的不足,提出了改进调度算法,实现了算法的调度器原型。新的调度算法对软实时虚拟机进行Credit比例预分配,采用动态调度时间片机制,以non-work-conserving方式实现软实时任务周期调度,保障调度周期满足运行周期要求。通过区分并发和非并发软实时虚拟机,采取不同的调度策略,在满足资源利用率的基础上,确保实时任务的顺利运行。测试结果表明,该调度算法在对并发和非并发软实时任务调度上,具有良好的表现,较好满足了软实时应用调度需求。     

基于协作型VMM的虚拟机执行环境动态配置模型

针对当前各类虚拟机监控器(VMM)在定制虚拟机执行环境过程中灵活性不足、可用性不强方面的问题,提出并设计了一种用户动态配置虚拟机执行环境的模型,并在协作型VMM之上进行了实现。结合Intel VT-x技术的实现机制,充分利用虚拟机控制结构(VMCS)中的执行控制域特性,通过为用户提供接口,对虚拟机配置文件进行操作,实现对虚拟机执行环境的实时动态配置。用户利用该模型能够快速构建具有不同运行时特性的虚拟机执行环境。测试结果表明,该模型能够提高VMM的可用性。     

Hartstone Uniprocessor Benchmark: Definitions and experiments for real-time systems

The purpose of this paper is to define a series of requirements and associated experiments called the Hartstone Uniprocessor Benchmark (HUB), to be used in testing the ability of a uniprocessor system to handle certain types of hard real-time applications. The benchmark model considers the real-time system as a set of periodic, aperiodic (sporadic), and synchronization (server) tasks. The tasks are characterized by their execution times (workloads), and deadlines. There are five series of experiments defined. They are, in order of increasing complexity, PH (Periodic Tasks, Harmonic Frequencies), PN (Periodic Tasks, Nonharmonic Frequencies), AH (Periodic Tasks with Aperiodic Processing), SH (Periodic Tasks with Synchronization), and SA (Periodic Tasks with Aperiodic Processing and Synchronization). The general stopping criteria of the experiments is defined as follows: Change one of the following four task set parameters: number of tasks, execution time(s), blocking time(s), or deadline(s) until a given task set is no longer schedulable, i.e., a deadline is missed. The derivation of the Hartstone experiments from one static scheduling algorithm (Rate Monotonic) and one dynamic scheduling algorithm (Earliest Deadline First) is presented. Because of its high-level application view of the underlying hardware and real-time system software the Hartstone experiments can be used for fast prototyping of real-time applications. Implementation of such benchmarks is useful in evaluating scheduling algorithms, scheduling protocols, and design paradigms, as well as evaluating real-time languages, the tasking system of compilers, real-time operating systems, and hardware configurations.     

Scheduling of hard real-time garbage collection

Automatic memory management or garbage collection greatly simplifies development of large systems. However, garbage collection is usually not used in real-time systems due to the unpredictable temporal behavior of current implementations of a garbage collector. In this paper we propose a real-time garbage collector that can be scheduled like a normal real-time thread with a deadline monotonic assigned priority. We provide an upper bound for the collector period so that the application threads will never run out of memory. Furthermore, we show that the restricted execution model of the Safety Critical Java standard simplifies root scanning and reduces copying of static data. Our proposal has been implemented and evaluated in the context of the Java processor JOP.     

一种改进的增量式JVM垃圾收集算法

非增量式Java虚拟机(JVM)垃圾回收算法的内存开销较大。为此,提出一种基于栈式分配策略的JVM增量式垃圾收集算法。对Java栈帧进行改造使其支持存储对象,改进增量式收集器中堆空间的划分、引用跟踪方式,以减少垃圾收集带来的不确定性暂停。实验结果表明,该算法能有效减少暂停的频率和时长,提高运行速度。