Subscriber-Driven Cloud Interference Mitigation for Network Services
Bibliographic record
Abstract
Network services, e.g., video streaming services, are increasingly being deployed on public cloud platforms. Such services often employ horizontal scaling where a group of resource instances, e.g., virtual machines (VMs), handle the incoming workload. The response time of such services is often affected by interference, i.e., contention among resource instances belonging to multiple cloud subscribers for shared cloud resources. Most commercial cloud platforms do not support built-in mechanisms to detect interference and mitigate its impact. This paper outlines a solution called PRIMA that subscribers of such platforms, i.e., network service operators, can deploy to ensure a specified end user response time target is met even in the face of fluctuations in workload and interference. PRIMA uses automated and controlled performance tests to build models that capture the joint impact of workload and interference on the response time of each resource instance employed by a service. PRIMA adapts the system to changing workload and interference conditions by using these models at runtime to control the number of instances in the system and the distribution of load among these instances. Unlike existing subscriber-oriented interference mitigation techniques in literature, PRIMA provides an explicit mechanism to guarantee that the specified response time threshold is met at every resource instance assigned to a service. Furthermore, in contrast to these approaches PRIMA can help an operator avoid using more instances than necessary for handling the observed workload and interference.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".