On Dominating Set Allocation Policies in Real-Time Wide-Area Distributed Systems
Bibliographic record
Abstract
This paper investigates resource allocation policies for achieving real-time content distribution with subsecond delay bounds on the current Internet. Resource allocation in real-time systems has been concerned primarily with meeting time constraints on single processors and multiprocessors. On a single processor, the main degree of freedom available for the real-time designer is the scheduling policy. On a (partitioned) multiprocessor, a second degree of freedom is the partitioning policy. This paper explores a third degree of freedom unique to large-scale (i.e., widearea) distributed systems with non-negligible communication delays among individual nodes. We call it the dominating set allocation policy. This policy is a primary determinant of schedulability in such systems. We present some initial steps towards understanding the properties of different dominating set allocation policies in terms of resulting task schedulability. We describe an optimal dominating set allocation algorithm (subject to certain design decisions), propose a number of simple heuristics, and evaluate them using realistic Internet measurements and HTTP workload. The key contribution of this paper lies in the practical applicability of the proposed heuristics in achieving delay guarantees (with a high probability) over best-effort wide-area networks.
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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 | 0.008 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".