arXiv (All)
open access
ContinuumBench: Benchmarking Joint Autoscaling and Placement Across Evaluation Regimes in the Cloud-Edge Continuum
Cloud-edge controllers coordinate service placement, replica scaling, and resource pre-warming to keep end-to-end latency within application deadlines. But evaluations often obscure the source of a reported gain: placement and scaling are studied separately; workload, connectivity, and calibration a
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Leveraging Serverless Computing for Flexibility and Scalability in Applications#106919
- Busy Time Minimization with Preemption, Migration, and One Resource Requirement#1048941
- Cross-Cloud Performance Benchmarking and Optimization#691313
- KubePACS: Kubernetes Cluster Using Performant, Highly Available, and Cost Efficient Spot Instances#138897
- A Cloud Continuum Research Infrastructure for Distributed CPS Experimentation#414076
Record · ID 986565
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive