From Peers to the Cloud: Utilizing Distributed Resources for Content Delivery and User Collaboration
Bibliographic record
Abstract
In this thesis, we tackle the problem of content delivery and user collaboration with emerging Internet technologies. Our investigation starts from peer-to-peer (P2P) sharing with social relations to contemporary cloud computing with flexible resource provisioning. We seek to leverage distributed resources for efficient sharing and collaboration, which leads to a hybrid system design that seamlessly bridges users' local resources to public datacenters. We first explore social-network-based optimizations inpeer-to-peer content delivery. We give solid evidences that long-term social relations can be found and applied to enhance the sharing efficiency in peer-to-peer networks, and present practical implementation strategies for the popular BitTorrent system. We then investigate the performance of cloud-based file synchronization applications and identify the bottlenecks in their system design, in particular, the task interferences. We propose an interference-aware provisioning algorithm, which effectively mitigates the problem. We further examine the users' interactions in state-of-the-art cloud-based distributed interactive applications. We find that, despite the benefit in terms of cost savings and better scalability, the cloud-based deployment greatly increases the users' interaction latency. We demonstrate that a smart assignment algorithms for virtual machines can remarkably reduce such latency. Finally, we present a real-world system design that effectively bridges users' local resources to enterprise cloud platforms. Our measurements as well as system analysis indicate that it serves as a complement of great potentials to enterprise cloud services.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".