Adaptive SLA-based elasticity management algorithms for a virtualized IP multimedia subsystem
Bibliographic record
Abstract
The IP Multimedia System (IMS) is an important reference service delivery platform for next generation networks and is considered as a de-facto standard for IP-based multimedia communication services. In its current design, the IMS faces important challenges in terms of scalability and elasticity, and lacks the ability to adaptively manage the network resources and dynamically dimension the network nodes based on load and demand. Network function virtualization and cloud computing are two important concepts that can be leveraged to address those challenges in IMS environments. In this work, we propose two adaptive SLA-based elasticity management algorithms for virtualized IMS environments. Our proposed algorithms use two SLA attributes (the call setup delay and user priority) to dynamically control the CPU resources allocated/de-allocated to virtualized IMS nodes. The aims of our proposed algorithms are: 1) to ensure efficient usage and sharing of CPU resources by various IMS components; 2) to reduce the overall power consumption in virtualized IMS platforms; and 3) to enhance the user experience when using IMS networks. We have tested the proposed algorithms by setting up a virtualized IMS environment using OpenIMS Core and Xen as the hypervisor. The results obtained show that our proposed algorithms meet the SLA constraints, even when subjected to dynamic load, thereby enhancing the overall QoS. We have also compared the proposed algorithms with Xen Server's existing CPU resource scaling governors and the results indicate that our algorithms work better when compared to the existing governors.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".