Mcds Scheduling Eval

---
name: mcds-scheduling-eval
description: Evaluates AI-augmented workflow scheduling in mobile edge-cloud environments by comparing execution time, energy consumption, SLA violations, and fairness against state-of-the-art baselines under dynamic workloads and host mobility. Use when the user wants to benchmark on WFCommons (Pegasus workflows: BLAST, Cycles, Montage), or asks about evaluating this task. Reports SLA Violation Rate.
metadata:
  skill_kind: dataset_eval
  source_arxiv: 2112.07269
  bibtex_key: tuli2021mcds
  confidence: high
---

# mcds-scheduling-eval

> MCDS: AI Augmented Workflow Scheduling in Mobile Edge Cloud Computing Systems — Tuli et al. (2021) (arXiv:2112.07269, 2021)

## What this evaluates

Evaluates AI-augmented workflow scheduling in mobile edge-cloud environments by comparing execution time, energy consumption, SLA violations, and fairness against state-of-the-art baselines under dynamic workloads and host mobility.

## Datasets

- **WFCommons (Pegasus workflows: BLAST, Cycles, Montage)** — total ?; splits: benchmark (-1)

## Metrics

- `SLA Violation Rate` **(primary)** — range: percent
  - Fraction of workflows whose response time exceeds the application-specific deadline. The deadline is defined as the 98th percentile response time of the Closure baseline for that application.
- `Average Response Time` — range: other
  - Mean time from workflow submission to completion across all executed workflows.
- `Average Energy Consumption` — range: other
  - Normalized average power usage of the edge-cloud environment per scheduling interval.
- `Scheduling Fairness` — range: [0, 1]
  - Jain’s fairness index computed over the Instructions Per Second (IPS) of running tasks.

## Input / output format

**Input**: Workflow DAG specifications (tasks, precedence constraints, resource requirements) and real-time host resource utilization metrics (CPU, RAM, disk, bandwidth) at each scheduling interval.

**Output**: Task-to-host assignment mapping (placement decisions) for each workflow in the scheduling interval, including migration decisions if applicable.

## Scoring recipe

```python
def compute_metrics(predictions, gold, baseline_closure):
    sla_deadline = np.percentile(baseline_closure['response_time'], 98)
    sla_violations = sum(1 for w in predictions if w['response_time'] > sla_deadline) / len(predictions)
    avg_response = np.mean([w['response_time'] for w in predictions])
    avg_energy = np.mean([w['energy'] for w in predictions])
    ips = [w['ips'] for w in predictions]
    fairness = (np.sum(ips)**2) / (len(ips) * np.sum(np.array(ips)**2))
    return {'SLA_Violation_Rate': sla_violations, 'Avg_Response_Time': avg_response, 'Avg_Energy': avg_energy, 'Fairness': fairness}
```

## Common pitfalls

- SLA deadline is dynamically derived per application from the Closure baseline's 98th percentile response time, not a fixed global threshold.
- Workload arrival follows a Poisson process (λ=1.2 physical, λ=5 simulated) normalized by computational requirements, which must be replicated for fair comparison.
- Energy consumption is reported as normalized interval averages, not raw joules, and is combined with response time in the optimization objective but evaluated separately.

## Evidence (verbatim from paper)

> To compare the QoS, we consider metrics like energy consumption and response time. We also compare the SLA violation rates. The SLA of a workflow is violated if its response time is greater than the deadline. We consider the relative definition of SLA (as in[7]) where the deadline is the 98th percentile response time for the same application (BLAST/Cycles/Montage) on the state-of-the-art baseline Closure.

## Citation

```bibtex
@misc{tuli2021mcds,
  title={MCDS: AI Augmented Workflow Scheduling in Mobile Edge Cloud Computing Systems},
  author={Tuli et al. (2021)},
  year={2021},
  note={arXiv:2112.07269}
}
```

- arXiv: 2112.07269