Kuber: Cost-Efficient Microservice Deployment Planner
Bibliographic record
Abstract
The microservice-based architecture - a SOA-inspired principle of dividing backend systems into indepen-dently deployed components that communicate with each other using language-agnostic APIs - has gained increased popularity in industry. Realistic microservice-based applications contain hundreds of services deployed on a cloud. As cloud providers typically offer a variety of virtual machine (VM) types, each with its own hardware specification and cost, picking a proper cloud configuration for deploying all microservices in a way that satisfies performance targets while minimizing the deployment costs becomes challenging. Existing work focuses on identifying the best VM types for recurrent (mostly high-performance computing) jobs. Yet, identifying the best VM type for the myriad of all possible service combinations and further identifying the optimal subset of combinations that minimizes deployment cost is an intractable problem for applications with a large number of services. To address this problem, we propose an approach, called Kuber, which utilizes a set of strategies to efficiently sample the neces-sary subset of service combinations and VM types to explore. Comparing Kuber with baseline approaches shows that Kuber is able to find the best deployment with the lowest search cost.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".