Enabling customer-provided resources for cloud computing: Potentials,challenges, and implementation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Recent years have witnessed cloud computing as an efficient means for providing resources as a form of utility. Driven by the strong demands, industrial pioneers have offered commercial cloud platforms, mostly datacenter-based, which are known to be powerful and effective. Yet, as the cloud customers are pure consumers, their local resources, though abundant, have been largely ignored. In this paper, We present SpotCloud, a real working system that seamlessly integrates the customers' local resources into the cloud platform, enabling them to sell, buy, and utilize these resources. We also investigate the potentials and challenges towards enabling customer-provided resources for cloud computing. Given that these local resources are highly heterogeneous and dynamic, we closely examine two critical challenges in this new context: (1) How can the customers be motivated to contribute or utilize such resources? and (2) How can high service availability be ensured out of the dynamic resources? We demonstrate a distributed market for potential sellers to flexibly and adaptively determine their resource prices through a repeated seller competition game. We also present an optimal resource provisioning algorithm that ensures service availability with minimized lease and migration costs. The evaluation results indicate it as a flexible and less expensive complement to the pure datacenter-based cloud.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it