Continuous QoS-compliant Orchestration in the Cloud-Edge Continuum
October 04, 2023 Β· Declared Dead Β· π Software, Practice & Experience
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Giuseppe Bisicchia, Stefano Forti, Ernesto Pimentel, Antonio Brogi
arXiv ID
2310.02985
Category
cs.SE: Software Engineering
Cross-listed
cs.DC,
cs.NI
Citations
9
Venue
Software, Practice & Experience
Last Checked
4 months ago
Abstract
The problem of managing multi-service applications on top of Cloud-Edge networks in a QoS-aware manner has been thoroughly studied in recent years from a decision-making perspective. However, only a few studies addressed the problem of actively enforcing such decisions while orchestrating multi-service applications and considering infrastructure and application variations. In this article, we propose a next-gen orchestrator prototype based on Docker to achieve the continuous and QoS-compliant management of multiservice applications on top of geographically distributed Cloud-Edge resources, in continuity with CI/CD pipelines and infrastructure monitoring tools. Finally, we assess our proposal over a geographically distributed testbed across Italy.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Software Engineering
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Microservices: yesterday, today, and tomorrow
π
π
The Cartographer
A Survey of Machine Learning for Big Code and Naturalness
R.I.P.
π»
Ghosted
An Overview on Smart Contracts: Challenges, Advances and Platforms
R.I.P.
π»
Ghosted
Slither: A Static Analysis Framework For Smart Contracts
R.I.P.
π»
Ghosted
ContractFuzzer: Fuzzing Smart Contracts for Vulnerability Detection
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted