Optimal Load Balancing and Energy Cost Management for Internet Data Centers in Deregulated Electricity Markets
Bibliographic record
Abstract
Along with the rapid increasing energy consumption, the energy cost of Internet data centers (IDCs) has been skyrocketing. A novel scheme of geographical load balancing was proposed to reduce electricity bills for service providers. However, one important challenge faced by service providers has not been considered properly. In service systems, the service delay faced by consumers includes the queuing delay and the transmission delay. While existing work only consider the queuing delay, the transmission delay introduced by geographical load balancing has been overlooked. It is one of the most important factors affecting the quality of service for real-time service systems. In this paper, we take the transmission delay into our design consideration and formulate a mixed-integer nonlinear programming (MINLP) problem with coupled constraint to achieve the optimal load balancing and energy cost management for IDCs while meeting the service-level agreements (SLA) of consumers. A novel heuristic based branch and bound with feedback (HBBF) algorithm is proposed to decouple the MINLP problem with coupled constraint efficiently. Extensive performance evaluations based on real electricity price data and site-to-site transmission delay data demonstrate the effectiveness of our proposed algorithm.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".