Determining Optimal Polling Frequency Using a Learning Automata-based Solution to the Fractional Knapsack Problem
Bibliographic record
Abstract
Previous approaches to resource allocation in Web monitoring target optimal performance under restricted capacity constraints (Pandey et al., 2003; Wolf et al., 2002). The resource allocation problem is generally modelled as a knapsack problem with known deterministic properties. However, for practical purposes the Web must often be treated as stochastic and unknown. Unfortunately, estimating unknown knapsack properties (e.g., based on an estimation phase (Pandey et al., 2003; Wolf et al., 2002)) delays finding an optimal or near-optimal solution. Dynamic environments aggravate this problem further when the optimal solution changes with time. In this paper, we present a novel solution for the nonlinear fractional knapsack problem with a separable and concave criterion function (Bretthauer and Shetty, 2002). To render the problem realistic, we consider the criterion function to be stochastic with an unknown distribution. At every time instant, our scheme utilizes a series of informed guesses to move, in an online manner, from a "current" solution, towards the optimal solution. At the heart of our scheme, a game of deterministic learning automata performs a controlled random walk on a discretized solution space. Comprehensive experimental results demonstrate that the discretization resolution determines the precision of our scheme. In order to yield a required precision, the current resource allocation solution is consistently improved, until a near-optimal solution is found. Furthermore, our proposed scheme quickly adapts to periodically switching environments. Thus, we believe that our scheme is qualitatively superior to the class of estimation-based schemes
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".