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Bound analysis of closed queueing networks with workload burstiness
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Joint International Conference on Measurement and Modeling of Computer Systems archive
Proceedings of the 2008 ACM SIGMETRICS international conference on Measurement and modeling of computer systems table of contents
Annapolis, MD, USA
SESSION: Theory table of contents
Pages 13-24  
Year of Publication: 2008
ISBN:978-1-60558-005-0
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Authors
Giuliano Casale  College of William and Mary, Williamsburg, VA, USA
Ningfang Mi  College of William and Mary, Williamsburg, VA, USA
Evgenia Smirni  College of William and Mary, Williamsburg, VA, USA
Sponsors
SIGMETRICS: ACM Special Interest Group on Measurement and Evaluation
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
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ABSTRACT

Burstiness and temporal dependence in service processes are often found in multi-tier architectures and storage devices and must be captured accurately in capacity planning models as these features are responsible of significant performance degradations. However, existing models and approximations for networks of first-come first-served (FCFS) queues with general independent (GI) service are unable to predict performance of systems with temporal dependence in workloads.

To overcome this difficulty, we define and study a class of closed queueing networks where service times are represented by Markovian Arrival Processes (MAPs), a class of point processes that can model general distributions, but also temporal dependent features such as burstiness in service times. We call these models MAP queueing networks. We introduce provable upper and lower bounds for arbitrary performance indexes (e.g., throughput, response time, utilization) that we call Linear Reduction (LR) bounds. Numerical experiments indicate that LR bounds achieve a mean accuracy error of 2 percent.

The result promotes LR bounds as a versatile and reliable bounding methodology of the performance of modern computer systems.


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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Collaborative Colleagues:
Giuliano Casale: colleagues
Ningfang Mi: colleagues
Evgenia Smirni: colleagues