Market-based self-optimization for autonomic service overlay networks
Bibliographic record
Abstract
Rather than managing their heterogeneity and dynamic behavior through centralized intervention, overlay nodes can be programmed to self-organize and self-manage the network. To achieve the highest performance within a service overlay, they are further expected to self-optimize the network, by cooperatively providing and allocating resources in an optimal manner. However, since nodes are inherently selfish about resources they contribute or consume, self-optimization could not be achieved if they are not given the correct incentives. In this paper, we investigate the effectiveness of a market-based incentive mechanism in directing nodes' behavior and enabling self-optimizations. We have designed an intelligent market model for a service overlay network, based on which individual nodes, being service producers and consumers, determine their own resource contributions, consumptions, or service prices based on their own utility maximization goals. We also propose optimal decision making solutions for nodes to achieve their self-interests; in particular, service providers are provided with a control-based pricing solution based on system identification techniques. With the multicast streaming application as an example, we show through simulations that, even when selfish nodes all seek their maximal utilities, the resulting network still achieves close-to-optimal performance in both steady and dynamic states. The results also indicate that, by encouraging nodes to behave selfishly and intelligently in a designed market, self-optimization in other autonomic systems may be facilitated in the presence of node selfishness.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".