Quality of Service Aware Orchestration for Cloud-Edge Continuum Applications.

Area of Science:

• Computer Science
• Distributed Systems
• Cloud Computing

Background:

• The proliferation of connected, edge devices necessitates new computing paradigms.
• Edge computing offers low latency but faces resource constraints compared to cloud computing.
• Combining cloud and edge computing addresses complex application requirements like time sensitivity and data privacy.

Purpose of the Study:

• To propose an architecture for dynamic application orchestration in the cloud-edge continuum.
• To enhance application quality of service (QoS) through intelligent scheduling.
• To address the challenges of managing complex applications across distributed cloud and edge resources.

Main Methods:

• Development of a novel architecture for dynamic orchestration.
• Implementation of a distributed scheduling approach with per-application customization.
• Integration with Kubernetes for practical deployment and evaluation.

Main Results:

• The proposed architecture enables dynamic orchestration of applications across the cloud-edge continuum.
• The distributed scheduling approach is scalable and adaptable to complex algorithms.
• Evaluation demonstrates the architecture's viability for QoS-aware scheduling.

Conclusions:

• The developed architecture effectively manages applications in cloud-edge environments.
• It facilitates advanced scheduling algorithms that prioritize application quality of service.
• This approach unlocks the full potential of layered cloud-edge infrastructures.