A novel bio-inspired load balancing algorithm with QoS assurance for large-scale peer-to-peer systems
Bibliographic record
Abstract
Quality of Service(QoS) is attracting more and more attention in many areas, including entertainment, emergency services and 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 help analyze our 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.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".