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Cellular Disco: resource management using virtual clusters on shared-memory multiprocessors
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Source ACM Symposium on Operating Systems Principles archive
Proceedings of the seventeenth ACM symposium on Operating systems principles table of contents
Charleston, South Carolina, United States
Pages: 154 - 169  
Year of Publication: 1999
ISBN:1-58113-140-2
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Authors
Kinshuk Govil  Computer Systems Laboratory, Stanford University
Dan Teodosiu  Hewlett-Packard Laboratories, Palo Alto, CA
Yongqiang Huang  Computer Systems Laboratory, Stanford University
Mendel Rosenblum  Computer Systems Laboratory, Stanford University
Sponsor
SIGOPS: ACM Special Interest Group on Operating Systems
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 5,   Downloads (12 Months): 47,   Citation Count: 17
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ABSTRACT

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 bottle-necks. 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.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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CITED BY  17

Collaborative Colleagues:
Kinshuk Govil: colleagues
Dan Teodosiu: colleagues
Yongqiang Huang: colleagues
Mendel Rosenblum: colleagues