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[shba06]
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Piyush Shivam, Shivnath Babu, and Jeff Chase.
Learning application models for utility resource planning.
In Proc. IEEE Int'l Conference on Autonomic Computing
(ICAC'06), June 2006.
[ bib |
.pdf |
.pdf ]
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[wozh06]
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Murray Woodside, Tao Zheng, and Marin Litoiu.
Service system resource management based on a tracked layered
performance model.
In Proc. IEEE International Conference on Autonomic Computing
(ICAC'06), June 2006.
[ bib |
.pdf ]
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[beme05]
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Mohamed Bennani and Daniel A. Menasce.
Resource allocation for autonomic data centers using analytic
performance models.
In IEEE International Conference on Autonomic Computing
(ICAC'05), pages 229-240, 2005.
[ bib |
.pdf ]
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[teda05]
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Gerald Tesauro, Rajarshi Das, William E. Walsh, and Jeffrey O. Kephart.
Utility-function-driven resource allocation in autonomic systems.
In IEEE International Conference on Autonomic Computing
(ICAC'05), pages 342-343, 2005.
[ bib |
.pdf ]
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[wazh05]
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Zhikui Wang, Xiaoyun Zhu, and Sharad Singhal.
Utilization vs. SLO-based control for dynamic sizing of resource
partitions.
Technical Report HPL-2005-126R1, HP Laboratories, 2005.
[ bib |
.pdf |
.pdf ]
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[apca04]
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K. Appleby, S. B. Gao, J. R. Giles, and K.-W. Lee.
Policy-based automated provisioning.
IBM Systems Journal, 43(1):121-135, 2004.
[ bib |
.pdf ]
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[chsh03]
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Abhishek Chandra and Prashant Shenoy.
Effectiveness of dynamic resource allocation for handling internet
flash crowds.
Technical Report TR03-37, Department of Computer Science, University
of Massachusetts at Amherst, November 2003.
[ bib |
.pdf |
.pdf ]
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[boda03]
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Craig Boutilier, Rajarshi Das, Jeffrey O. Kephart, Gerald Tesauro, and
William E. Walsh.
Cooperative negotiation in autonomic systems using incremental
utility elicitation.
In Proceedings of the 19th Conference in Uncertainty in
Artificial Intelligence, pages 89-97, August 2003.
[ bib |
.pdf ]
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[utta03]
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Sandeep Uttamchandani, Carolyn Talcott, and David Pease.
Eos: An approach of using behavior implications for policy-based
self-management.
In Proc. 14th IFIP/IEEE International Workshop on Distributed
Systems: Operations and Management (DSOM), number 2867 in Lecture Notes in
Computer Science, pages 16-27. Springer-Verlag, 2003.
[ bib |
.pdf ]
This is an attempt to define the behavioural implications of policies (event-condition-action rules) so that a self-managing system can reason about which policies to use to adjust itself.
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[paga02]
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S. Parekh, N. Gandhi, J. Hellerstein, D. Tilbury, T. Jayram, and J. Bigus.
Using control theory to achieve service level objectives in
performance management.
Real Time Systems Journal, 23(1-2), 2002.
[ bib |
CiteSeer |
.pdf |
.pdf ]
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[zhlu02]
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Ronghua Zhang, Chenyang Lu, Tarek F. Abdelzaher, and John A. Stankovic.
Controlware: A middleware architecture for feedback control of
software performance.
In Proc. International Conference on Distributed Computing
Systems (ICDCS 2002), pages 301-310, 2002.
[ bib |
.pdf ]
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[chan01]
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Jeffrey S. Chase, Darrell C. Anderson, Prachi N. Thakar, Amin Vahdat, and
Ronald P. Doyle.
Managing energy and server resources in hosting centres.
In Proceedings of the 18th ACM Symposium on Operating System
Principles (SOSP'01), pages 103-116, 2001.
[ bib |
.pdf |
.pdf ]
Describes a system called Muse for allocating data center resources to servers. Hosted services are assumed to be able to scale via the the allocation of more servers or the allocation of more of the resources of a given server. For energy-conscious provisioning, keep active servers at a target utilization level and put inactive servers into low-power mode. Proposes resource allocation policies based on a comparison of resource costs and benefits. Costs refer to the costs of providing resources. Benefit refers to the utility of a particular level of application performance. Muse is expected to understand the relationship between resource allocation and application performance levels so that it can optimize the allocation of available resources among the competing applications.
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[shli00]
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Molly H. Shor, Kang Li, Jonathan Walpole, David Steere, and Calton Pu.
Application of control theory to modeling and analysis of computer
systems.
In Proceedings of the Japan-USA-Vietnam Workshop on Research and
Education in Systems, Computation and Control Engineering, HoChiMinh City,
Vietnam, June 2000.
[ bib |
.pdf |
.pdf ]
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[kalu00]
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M. Katchabaw, H. Lutfiyya, and M. Bauer.
Driving resource management with application-level quality of service
specifications.
Journal of Decision Support Systems, 28(2):71-87, 2000.
[ bib |
.ps ]
Describes some generic tools for embedding monitoring and control hooks into application software.
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[gost99]
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Ashvin Goel, David Steere, Calton Pu, and Jonathan Walpole.
Adaptive resource management via modular feedback control.
Technical Report CSE-99-003, Department of Computer Science and
Engineering, Oregon Graduate Institute, January 1999.
[ bib |
.pdf |
.pdf ]
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[gost98]
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Ashvin Goel, David Steere, Calton Pu, and Jonathan Walpole.
SWiFT: A feedback control and dynamic reconfiguration toolkit.
Technical Report CSE-98-009, Department of Computer Science and
Engineering, Oregon Graduate Institute, September 1998.
[ bib |
.pdf ]
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[auca98]
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Christina Aurrecoechea, Andrew T. Campbell, and Linda Hauw.
A survey of QoS architectures.
Multimedia Systems, 6(3):138-151, 1998.
[ bib |
CiteSeer |
.pdf |
.pdf ]
Emphasis is on QoS for multimedia streams.
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[amei97]
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J. Aman, C. K. Eilert, D. Emmes, P. Yocom, and D. Dillenberger.
Adaptive algorithms for managing a distributed data processing
workload.
IBM Systems Journal, 36(2):242-283, 1997.
[ bib |
.pdf ]
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