Demo: Application Monitoring as a Network Service
Bibliographic record
Abstract
The recent rise of cloud applications, representing large complex modern distributed services, has made performance monitoring a major issue and a critical process for both cloud providers and cloud customers. Many different monitoring techniques are used such as tracking resource consumption, performing application-specific measures or analyzing message exchanges. Typically the collected data is logged at the host on which the application is deployed, then either analyzed locally or forwarded to a remote analysis host. In contrast, this demonstration paper presents a Monitoring as a Service prototype that uses the advances in Software Defined Networking (SDN) to move some of the logging functionality into the network. The core of our MaaS is implemented as a virtual network function where agents are co-located with software switches in order to extract performance metrics from the message flows between components in a non-intrusive manner and send the calculated measures to the clients for visualization in near real-time. The MaaS has a lot of flexibility in how it is deployed and does not require to instrument software or platforms. In our demo we show the tool in action demonstrating how users can choose to monitor different service types and performance metrics in a user-friendly manner.
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".