Bibliographic record
Abstract
The essence of the peer-to-peer design philosophy is to design protocols for end hosts, or “peers”, to work in collaboration to achieve a certain design objective, such as the sharing of a large file. From a theoretical perspective, it has been recognized that the peer-to-peer design paradigm resembles gossip protocols, and with appropriate algorithmic design, it maximizes the network flow rates in multicast sessions. Over the past ten years, research on peer-to-peer computing and systems, a unique and intriguing category of distributed systems, has received a tremendous amount of research attention from academia and industry alike. Peer-to-peer computing eventually culminated in a number of successful commercial systems, showing the viability of their design philosophy in the Internet. The peer-to-peer design paradigm has pushed all design choices of innovative protocols to the edge of the Internet, and in most cases to end hosts themselves. It represents one of the best incarnation of the end-to-end argument, one of the frequently disputed design philosophies that guided the design of the Internet. Yet, research on peer-to-peer computing has recently receded from the spotlight, and suffered from a precipitous fall that was as dramatic as its meteoric rise to the culmination of its popularity. This article presents a cursory glimpse of existing results over the past ten years in peer-to-peer computing, with a particular focus on understanding what has stimulated its rise in popularity, what has contributed to its commercial success, and eventually, what has led to its precipitous fall in research attention. Our insights in this article may be beneficial when we develop our thoughts on the design paradigm of cloud computing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.033 |
| Scholarly communication | 0.015 | 0.045 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".