Mbalancer：虚拟机内存资源动态预测与调配  

MBalancer:Predictive Dynamic Memory Balancing for Virtual Machines

作　　者：王志钢[1] 汪小林[1] 靳辛欣[1] 王振林 罗英伟[1] 

机构地区：[1]北京大学信息科学技术学院,北京100871 [2]Department of Computer Science and Technology, Michigan Technological University, Michigan 49931-1295, USA

出　　处：《软件学报》2014年第10期2206-2219,共14页Journal of Software

基　　金：国家自然科学基金(61232008,61170055,61272158,61328201);国家高技术研究发展计划(863)(2012AA010905);高等学校博士学科点专项科研基金(20110001110101);深圳市生物、互联网、新能源、新材料产业发展专项资金(JC201104210107A)

摘　　要：在现代数据中心,虚拟化技术在资源管理、服务器整合、提高资源利用率等方面发挥了巨大的作用,已成为云计算架构中关键的抽象层次和重要的支撑性技术.在虚拟化环境中,如果要保证高资源利用率和系统性能,必须有一个高效的内存管理方法,使得虚拟机的物理内存大小能够满足应用程序不断变化的内存需求.因此,如何在单机以及数据中心内进行内存资源的动态调控,就成为了一个关键性问题.实现了一个低开销、高精确度的内存工作集跟踪机制,进而进行相应的本地或者全局的内存调控.采用了多种动态内存调控技术:气球技术能够在单机内有效地为各个虚拟机动态调节内存;远程缓存技术可在物理机之间进行内存调度;虚拟机迁移可将虚拟机负载在多个物理主机间进行均衡.深入分析了以上各种方案的优缺点,并根据内存超载的情况有针对性地设计了相应的调控策略,实验数据表明:所提出的预测式的内存资源管理方法能够对内存资源进行在线监控和动态调配,并有效地提高了数据中心的内存资源利用率,降低了数据中心能耗.Virtualization technology intends to deliver flexibility, consolidation, and high resource utilization to data centers. High resource utilization as well as high performance promised by virtualization largely depends on effective and efficient physical memory resource management scheme where memory allocation can adjust to dynamic memory demands of applications. This paper presents a predictive memory resource management scheme that combines memory resource monitoring and balancing to improve the resource utilization of a virtualized data center. A design is provided for a new low-overhead working set size tracing mechanism without loss of prediction accuracy. With accurate prediction, the presented scheme further resorts to either local or global memory balancing when the predicted trend of memory demand of a virtual machine exceeds its current allocation. Multiple mechanisms are employed： Ballooning can dynamically adjust memory allocation within a single host, remote cache enables a host to take the idle memory of another host as its network cache, and virtual machine migration moves virtual machines across multiple physical servers. The strength and weakness of each mechanism and design selection policy for memory balancing according to memory pressure are also discussed. Experimental results show that the global memory balancing achieves a significant center-wide speedup and energy conservation.

关 键 词：虚拟机 数据中心 工作集跟踪 内存管理 性能 

分 类 号：TP316[自动化与计算机技术—计算机软件与理论]