A scheduling and load balancing scheme for dynamic P2P‐based system
Bibliographic record
Abstract
Abstract Scheduling and load balancing have been one of the key issues in distributed systems due to their significant impact on system performance as well as system resource utilization. With the rapid development of Peer‐to‐Peer (P2P) network technology, traditional scheduling and load balancing techniques are facing new challenges because P2P‐based systems require more dynamic mechanisms for task scheduling and load balancing among heterogeneous network computing nodes. In this paper, we propose a scheme for dynamic scheduling and load balancing in a P2P‐based environment. Our scheme aims at Service‐Oriented P2P‐based distributed systems, however, it can be applied to traditional distributed architecture straightforwardly. Furthermore, we evaluate the performance of our scheme using simulation experiments in a cluster‐based distributed computing environment. Indeed, our results show that our scheme can achieve significant system performance gain compared to commonly used random and round robin scheduling algorithms in P2P‐based systems. Meanwhile, we use a real‐time distributed simulation to evaluate our algorithm, which can provide more trustful results in terms of performance evaluation. Indeed, the ease of using a distributed real‐time simulation, to measure the real‐time performance of the algorithm can promote more flexible and efficient methods for the development of load balancing algorithms. This is especially the case for the hard‐to‐predict P2P‐based distributed systems. Copyright © 2010 John Wiley & Sons, Ltd.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 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 | 0.001 |
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".