Location-Aware Container Scaling (LACS) in Geo-distributed Clouds
<p>Datasets and code for the problem of location-aware container scaling (LACS) in geo-distributed clouds:</p> <p>Randomly extracted one day’s workload from WikiBench and NASA HTTP: AppWorkload.py</p> <p>Facebook subscribers by January 2020 to simulate the distribution of application requests among different user regions: FacebookUserData.csv</p> <p>Sprint IP Network location as 82 user regions from 35 countries on 6 continents: SprintLocation.csv</p> <p>Observation on the network latency matrix among 82 user centres: LatencyMatrix.py</p> <p>Representative code using openAI's gym environment: deepscale.py</p>
ShareScore
36/100
Overall dataset sharing score
Score breakdown
These five areas show where the dataset supports — or may limit — practical reuse.
- Stewardship
- 4
- Harmonization
- 4
- Access
- 20
- Reuse readiness
- 8
- Engagement
- 0