Postina: A Publish/Subscribe Middleware Designed for Massively Multiplayer Games
Bibliographic record
Abstract
Postina is a network middleware designed for massively multiplayer online games (MMOG). It combines publish/subscribe functionalities with direct messaging, a feature of critical importance for MMOGs. In MMOGs, numerous messages such as state updates are sent to different clients. While some messages must be multicast to a large group of clients, other messages are private and sent to a single client only. Using a traditional client-server approach limits the number of simultaneous players, a property that is undesired in MMOGs. A potential solution would be to use publish/subscribe systems which are designed for scalability. Pure publish/subscribe systems, however, do not provide any possibility to send a message directly to a single client as the peers do not have any knowledge of the network topology. In this thesis we first study different publish/subscribe systems and the fea-tures they offer to choose an appropriate middleware providing the required functionalities for MMOGs. Additionally, we design Postina, an API for network layers in MMOGs that offers a convenient interface combining publish/subscribe and direct messaging. We then implement a version of Postina using Scribe, a topic-based publish/subscribe system built on top of the distributed hashtable Pastry. To fulfil the requirements of MMOGs, we add extra features such as reliable direct messaging and the capability of issuing subscriptions for other clients. This version of Postina is then integrated into Mammoth, the massively multiplayer game research framework developed at McGill University. Finally, the efficiency of the new network layer is tested to determine the maximum number of simultaneous players. This thesis was written at McGill University (Montréal, Québec) under the supervision of Professor Jörg Kienzle and Alexandre Denault (PhD can-
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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 | 0.001 |
| 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 | 0.001 |
| 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".