DSSDN: Demand‐supply based load balancing in Software‐Defined Wide‐Area Networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Summary One of the unexplored research areas in Software Defined Networks (SDN) is load balancing of control messages ( e.g . p a c k e t _ i n ) among distributed controllers in Wide Area Networks. In SDN, on every unsuccessful match in the flow table for the incoming traffic flows, the switch sends p a c k e t _ i n to the controller for further action against the traffic flow. The p a c k e t _ i n messages are one of the major contributors of the control request (load) received by the controller. When it exceeds a certain threshold limit, the response time for the control request increases nonlinearly due to the over CPU utilization and congestion. When the controller gets overloaded, typically the OpenFlow‐enabled Devices (OFDevices) are migrated from the current controller to another under loaded controller domain. This migration might cause large degradation of end users' QoS metrics. To resolve this issue, we introduce basic demand and supply curve based DSSDN, a new load balancing method that utilizes the load factors of Software Defined Wide Area Networks controllers. This method selects the OFDevice which causes maximum load on the controller and traversing minimum users traffic through it. The Karush‐Kuhn‐Tucker conditions are employed during the optimal controller selection by the OFDevices to improve the response time effectively. During implementation, virtual threads running on the controller representing the OFDevices are used to take the optimal decision instead of actual OFDevices. The experimental results show that during migration, the DSSDN stabilizes the load hikes, improves QoS, and increase the end users' utility without much disruptions in the network state.
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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 it