When hybrid cloud meets flash crowd: Towards cost-effective service provisioning
Bibliographic record
Abstract
With rapid development in online shopping, e-commerce websites are facing intensive user requests from an increasing number of customers. Especially in promotion seasons, these websites may encounter flash crowds which pull heavy pressure o private infrastructure and even make he website unavailable. Such severe flash crowds can be addressed by leveraging hybrid cloud solution, which relieves workloads of the private cloud by offloading the excessive user requests to the IaaS public cloud. However, the bursty and fluctuation of flash crowds bring challenges to distributing user requests with targest of delay-minimizing and cost-saving. In his paper, we apply the queueing theory to evaluate the average response time and explore the tradeoff between performance and cost in the hybrid cloud. By taking advantage of Lyapunov optimization techniques, we design an online decision algorithm for request distribution which achieves the average response time arbitrarily close to the theoretically optimum and controls he outsourcing cost based on a given budge. The simulation results demonstrate ha in a hybrid cloud, our solution can reduce he cost of e-commerce services as well as guarantee performance when encountering flash crowds.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".