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10th IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS'04)   p. 376
Incorporating Cost of Control into the Design of a Load Balancing Controller
Yixin Diao, IBM Thomas J. Watson Research Center, Hawthorne, New York
Joseph L. Hellerstein, IBM Thomas J. Watson Research Center, Hawthorne, New York
Adam J. Storm, IBM Toronto Lab, Markham, Ontario, Canada
Maheswaran Surendra, IBM Thomas J. Watson Research Center, Hawthorne, New York
Sam Lightstone, IBM Toronto Lab, Markham, Ontario, Canada
Sujay Parekh, IBM Thomas J. Watson Research Center, Hawthorne, New York
Christian Garcia-Arellano, IBM Toronto Lab, Markham, Ontario, Canada

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DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/RTTAS.2004.1317284
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Abstract
Load balancing is widely used in computing systems as a way to optimize performance by reducing bottleneck utilizations, such as adjusting the size of buffer pools to balance resource demands in a database management system. Load balancing is generally approached as a constrained optimization problem in which only the benefits of load balancing are considered. However, the costs of control are important as well. Herein, we study the value of including in controller design the trade-off between the cost of transient imbalances in resource utilizations and the cost of changing resource allocations. An example of the latter are actions such as resizing buffer pools that can reduce throughputs. This is because requests for data in pools whose memory is reduced immediately have longer access times whereas requests for data in pools whose memory is increased must fill this memory with data from disk before accessed times are reduced. We frame our study of control costs in terms of the widely used linear quadratic regulator (LQR). We develop a cost model that allows us to specify the LQR Q and R matrices based on the impact on system performance of changing resource allocations and transient load imbalances. Our studies of a DB2 Universal Database Server using benchmarks for online transaction processing and decision support workloads show that incorporating our cost model into the MIMO LQR controller results in a 14% improvement in performance beyond that achieved by dynamically allocating the size of buffers without properly considering the cost of control.
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Citation:  Yixin Diao, Joseph L. Hellerstein, Adam J. Storm, Maheswaran Surendra, Sam Lightstone, Sujay Parekh, Christian Garcia-Arellano, "Incorporating Cost of Control into the Design of a Load Balancing Controller," rtas, p. 376,  10th IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS'04),  2004