Energy Cost Conservation for Collaborative Edge Clouds with Online Mini-Batch Learning
Bibliographic record
Abstract
Edge clouds (ECs) have recently been shown with outstanding advantages in enhancing customized user service experiences, benefiting from user proximity and location-aware characteristics. However, operating a large-scale EC network would inevitably result in a significant energy cost for EC providers, potentially offsetting their service revenue without proper energy cost management. In this paper, we focus on conserving energy cost for EC providers by leveraging both electricity price-aware geographical load balancing and dynamic central processing unit (CPU) provisioning, considering the spatio-temporal diversities of electricity prices and user task demands. Due to the significant “switching cost” associated with turning CPUs and services on/off, we formulate a multi-timescale energy cost minimization problem that integrates large-timescale CPU provisioning and service placement, as well as small-timescale geographical task dispatching and CPU resource allocation. The Lagrange dual decomposition theory is exploited to handle the spatio-temporal variable couplings. A fully distributed mini-batch learning (MBL) algorithm that relies on parameter approximation for large-timescale decision makings is proposed to learn the optimal dual variables, i.e., the Lagrange multipliers. We present rigorous algorithm performance analysis, and conduct extensive simulations based on real data of electricity prices of Canada to demonstrate the superior performance of the MBL algorithm compared to several baseline schemes.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".