Fault-Resilience for Bandwidth Management in Industrial Software-Defined Networks
Bibliographic record
Abstract
Industrial Cyber-Physical Systems (ICPS) expect assurances of timely delivery of data even during the occurrence of distinct faults. It is a challenge to manage the required bandwidth by providing resilience to link failures and dynamically changing bandwidth requirements. In this paper, we address the aforementioned challenge by exploring Software-Defined Networks (SDN). We present a framework coined SDN-RMbw (Software-Defined Networking Resilience Management for Bandwidth), which is a contract-based framework, where the components are bound to bandwidth contracts and a resilience manager. The bandwidth contracts state the bandwidth requirements of traffic flows. With each such contract, a monitor is associated, which is responsible to detect two events, run-time changes and link failures. Directly after receiving the event trigger reports from the monitor, new routes are calculated by a path-finding algorithm. Based on newly calculated routes, an observer detects whether the contract requirements are still satisfied, or the contract gets violated (termed as fault). To provide resilience to such faults in the network, a resilience manager integrated with control logic decides and executes a suitable response strategy. The proposed SDN-based framework aims at providing fault-resilience as well as adapting to different network-state changes. The proposed framework is evaluated using a Ryu SDN controller on a hardware testbed. Our results show that the proposed framework provides enhanced network resilience as compared to baseline mechanisms and improves the success rate up to 21% and bandwidth up to 111 Mbps under distinct network scenarios. Furthermore, extensive experimental emulations on the Mininet tool depicts the scalability of the proposed framework.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".