Cellular disco: resource management using virtual clusters on shared-memory multiprocessors

被引:22
作者
Govil, K [1 ]
Teodosiu, D [1 ]
Huang, YQ [1 ]
Rosenblum, M [1 ]
机构
[1] Stanford Univ, Comp Syst Lab, Stanford, CA 94305 USA
来源
ACM TRANSACTIONS ON COMPUTER SYSTEMS | 2000年 / 18卷 / 03期
关键词
management; reliability; performance; resource management; fault containment; virtual machines; scalable multiprocessors;
D O I
10.1145/354871.354873
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Despite the fact that large-scale shared-memory multiprocessors have been commercially available for several years, system software that fully utilizes all their features is still not available, mostly due to the complexity and cost of making the required changes to the operating system. A recently proposed approach, called Disco, substantially reduces this development cost by using a virtual machine monitor that leverages the existing operating system technology. In this paper we present a system called Cellular Disco that extends the Disco work to provide all the advantages of the hardware partitioning and scalable operating system approaches. We argue that Cellular Disco can achieve these benefits at only a small fraction of the development cost of modifying the operating system. Cellular Disco effectively turns a large-scale shared-memory multiprocessor into a virtual cluster that supports fault containment and heterogeneity, while avoiding operating system scalability bottlenecks. Yet at the same time, Cellular Disco preserves the benefits of a shared-memory multiprocessor by implementing dynamic, fine-grained resource sharing, and by allowing users to overcommit resources such as processors and memory. This hybrid approach requires a scalable resource manager that makes local decisions with limited information while still providing good global performance and fault containment. In this paper we describe our experience with a Cellular Disco prototype on a 32-processor SGI Origin 2000 system. We show that the execution time penalty for this approach is low, typically within 10% of the best available commercial operating system for most workloads, and that it can manage the CPU and memory resources of the machine significantly better than the hardware partitioning approach.
引用
收藏
页码:229 / 262
页数:34
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