# Cloud Vm Ranking Eval

> This benchmark evaluates how effectively different Virtual Machines (VMs) can execute specific application workloads by ranking them according to weighted hardware attributes. It probes the capability to map domain-specific application requirements to underlying infrastructure performance characteristics. Use when the user wants to benchmark on Cloud VM Benchmarking Suite, or asks about evaluating this task. Reports S_i.

- Skill: `qhjqhj00/cloud-vm-ranking-eval` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/cloud-vm-ranking-eval`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/cloud-vm-ranking-eval/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: DevOps & Infra
- Author: qhjqhj00 (https://skillmd.com/u/qhjqhj00)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/qhjqhj00/cloud-vm-ranking-eval

---


# cloud-vm-ranking-eval

> Cloud Benchmarking for Performance — Varghese et al. (2014) (arXiv:1411.0912, 2014)

## What this evaluates

This benchmark evaluates how effectively different Virtual Machines (VMs) can execute specific application workloads by ranking them according to weighted hardware attributes. It probes the capability to map domain-specific application requirements to underlying infrastructure performance characteristics.

## Datasets

- **Cloud VM Benchmarking Suite** — total ?; splits: (unstated)

## Metrics

- `S_i` **(primary)** — range: other
  - S_i = \bar{G}_{i,k} \cdot W_k, where \bar{G}_{i,k} is the normalized attribute group for VM i, and W_k is the user-provided weight (0–5) for group k.

## Input / output format

**Input**: Attribute values r_{i,j} for each VM, grouped into categories (memory, processor, computation, storage), and user-provided weights W_k (0–5) for each group.

**Output**: A ranked list of VMs (Rp_i) ordered by descending performance score S_i.

## Scoring recipe

```python
# Normalize attributes across all candidate VMs
r_bar = (r - mean(r, axis=0)) / std(r, axis=0)
# Compute weighted score per VM
S = sum(r_bar[i, k] * W[k] for k in groups)
# Rank VMs by score descending
Rp = argsort(-S)
```

## Common pitfalls

- Normalization is performed relative to the set of candidate VMs, not a fixed baseline, so rankings are pool-dependent.
- Weights must be explicitly provided by domain experts based on application requirements; uniform weights defeat the purpose of application-aware ranking.
- Validation requires empirical execution time comparison against the theoretical ranking, not just score matching.

## Evidence (verbatim from paper)

> The score of each VM is calculated as $S_{i}\=\bar{G}_{i,k}.W_{k}$. The scores are ordered in a descending order for generating $Rp_{i}$ which is the ranking of the VMs based solely on performance.

## Citation

```bibtex
@misc{varghese2014cloudbenchmarking,
  title={Cloud Benchmarking for Performance},
  author={Varghese et al. (2014)},
  year={2014},
  note={arXiv:1411.0912}
}
```

- arXiv: 1411.0912