Enabling efficient application monitoring in cloud data centers using\n SDN
Bibliographic record
Abstract
Software Defined Networking (SDN) not only enables agility through the\nrealization of part of the network functionality in software but also\nfacilitates offering advanced features at the network layer. Hence, SDN can\nsupport a wide range of middleware services; network performance monitoring is\nan example of these services that are already deployed in practice. In this\npaper, we exploit the use of SDNs to efficiently provide application monitoring\nfunctionality. The recent rise of complex cloud applications has made\nperformance monitoring a major issue. We show that many performance indicators\ncan be inferred from messages exchanged among application components. By\nanalyzing these messages, we argue that the overhead of performance monitoring\ncould be effectively moved from the end hosts into the SDN middleware of the\ncloud infrastructure which enables more flexible placement of logging\nfunctionality. This paper explores several approaches for supporting\napplication monitoring through SDN. In particular, we combine selective\nforwarding in SDN to enable message filtering and reformatting, and propose a\ncustomized port sniffing technique. We describe the implementation of the\napproach within the standard SDN software, namely OVS. We further provide a\ncomprehensive performance evaluation to analyze advantages and disadvantages of\nour approach, and highlight the trade-offs.\n
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".