Incorporating cost of control into the design of a load balancing controller
Bibliographic record
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".