Breaking Service Function Chains with Khaleesi
Bibliographic record
Abstract
Network Function Virtualization (NFV) has recently emerged as a means to replace vendor specific, purpose built equipment with commodity hardware and leverage the open APIs and application orchestration for on demand deployment and scaling of network services. A well studied problem in NFV is the orchestration of Service Function Chains, (SFCs), i.e., a set of Virtual Network Functions (VNFs) chained together to realize a network service. State-of-the-art literature on SFC orchestration assumes a strict traversal order of VNFs in an SFC and less attention has been paid to SFCs with relaxed VNF orderings. In this paper, we address the problem of Flexible Service Function Chain Orchestration that jointly allocates compute and network resources for SFCs while considering a relaxed traversal order for some pairs of VNFs. We propose Khaleesi, a suite of solutions that consists of: (i) an Integer Linear Program (ILP) for optimally solving the problem; and (ii) a heuristic algorithm to scale to larger instances of the problem. Our simulation results show that flexible SFCs can increase revenue earned per unit cost by as much as ≈10% compared to a rigid SFC.
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 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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".