Modified Floyd-Warshall algorithm for equal cost multipath in software-defined data center
Bibliographic record
Abstract
Load balancing in data centers have been a common practice in the last couple of decades. This has been done statically in traditional networks with little or no feedback information from the underlying network state. With the current large cloud data centers and continuous changing traffic patterns, the drive for more interactive and dynamic solution to reduce the latency and improve the network resources utilization has brought about the Software Defined Networking Paradigm. However, some load balancing solutions have been proposed, utilizing OpenFlow without a well-defined algorithm to reduce the path computation complexity as requests arrive on the network. This paper proposes a path load-balancing algorithm which utilizes a modified Floyd-Warshall All-Pairs Shortest Paths algorithm to compute and store equal cost paths information, utilizes the stored information for path selection, and maintain a real-time updates of those paths. Our evaluation demonstrates that the proposed algorithm performs better than the Global First Fit algorithm as we have significantly reduced the time to service request as a result of eliminating the recursive path computation for every client request.
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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 | 0.001 | 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) | 0.002 | 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".