Enabling Efficient Application Monitoring in Cloud Data Centers using SDN
Bibliographic record
Abstract
Nowadays, many cloud applications can be considered large complex distributed services. The increasing sophistication and complexity have made performance monitoring a major issue and a critical process for both cloud providers and cloud customers. Existing monitoring techniques instrument the applications to collect measurements and log them at the host nodes on which the application is deployed. Such an approach introduces overhead and can slow down the application. New paradigms such as Software Defined Networking (SDN) and Network Function Virtualization (NFV) show promise for moving some of the measurement collection and logging functionality into the network as a lot of information can be extracted from messages exchanged between application components. Such a methodology could enable the cloud infrastructure to provide a Monitoring as a Service to applications in a transparent manner without software instrumentation and allowing for a more flexible placement of logging functionality. In this paper, we explore mechanisms to integrate application monitoring into SDN. In particular, we analyze whether switch based message filtering is feasible and we propose a customized port sniffing approach. We discuss the implementation aspects using OVS. The results confirm that moving application monitoring to the network is indeed an attractive option.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".