A formalized approach for designing a P2P‐based dynamic load balancing scheme
Bibliographic record
Abstract
Abstract Quality of service (QoS) is attracting more and more attention in many areas, including entertainment, emergency services, transaction services, and so on. Therefore, the study of QoS‐aware systems is becoming an important research topic in the area of distributed systems. In terms of load balancing, most of the existing QoS‐related load balancing algorithms focus on Routing Mechanism and Traffic Engineering. However, research on QoS‐aware task scheduling and service migration is very limited. In this paper, we propose a task scheduling algorithm using dynamic QoS properties, and we develop a Genetic Algorithm‐based Services Migration scheme aiming to optimize the performance of our proposed QoS‐aware distributed service‐based system. In order to verify the efficiency of our scheme, we implement a prototype of our algorithm using a P2P‐based JXTA technique, and do an emulation test and a simulation test in order to analyze our proposed solution. We compare our service‐migration‐based algorithm with non‐migration and non‐load‐balancing approaches, and find that our solution is much better than the other two in terms of QoS success rate. Furthermore, in order to provide more solid proofs of our research, we use DEVS to validate our system design. Copyright © 2010 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".