Edge Computing QoS Orchestration via Dynamic Resource Adjustment
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current edge computing systems face challenges in maintaining end-to-end Quality of Service (QoS) due to dynamic shifts in demand, limited infrastructure at edge nodes, and inefficiencies in resource allocation, leading to overprovisioning and poor utilization, which hinders the ability to adapt to changing resource needs and technological advancements.
Innovation Solution
The implementation of a workload meta-model that formalizes relationships between resource assignments and components, allowing for iterative convergence towards desired QoS objectives, with dedicated resources and real-time telemetry to ensure high automation and adaptability, and the option to offer higher-tier service as compensation during temporary spikes in demand.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If resource allocation is increased to meet QoS objectives, then service quality is improved, but infrastructure complexity and cost increase
Solution Approach 1:
The patent implements dynamic resource allocation where the orchestrator continuously monitors QoS metrics and adjusts resource assignments in real-time based on actual demand. This allows the system to provide QoS guarantees only when needed, avoiding static overprovisioning and reducing infrastructure complexity while maintaining reliability.
Solution Approach 2:
The system employs self-tuning capabilities where the orchestrator automatically detects QoS violations and reassigns resources without human intervention. This self-service mechanism eliminates the need for complex manual configuration and management, reducing infrastructure complexity while ensuring QoS objectives are met.
2Reliability
If resource allocation is increased to handle dynamic demand, then service availability is improved, but resource utilization efficiency deteriorates
Solution Approach 1:
The orchestrator dynamically adjusts resource allocation based on real-time monitoring of QoS metrics and actual service demand. Resources are allocated to meet availability requirements only when needed, and released when demand decreases, thereby maintaining service availability while maximizing resource utilization efficiency through adaptive reassignment.
Solution Approach 2:
The system implements continuous feedback loops where QoS metrics are monitored, compared against objectives, and used to trigger resource reassignment actions. This feedback mechanism ensures service availability is maintained while preventing resource overallocation, thereby optimizing utilization efficiency by allocating resources based on actual performance needs.
3Ease of operation
If manual resource management is used, then system simplicity is maintained, but adaptability to changing demands deteriorates
Solution Approach 1:
The orchestrator provides automated resource management that adapts to changing demands without requiring complex manual configuration. The system self-tunes by monitoring QoS metrics and automatically reassigning resources, maintaining operational simplicity while achieving high adaptability to dynamic service demands through intelligent automation.
Solution Approach 2:
The system uses feedback from QoS metric monitoring to automatically adjust resource allocation in response to changing demands. This feedback-driven approach maintains system simplicity by eliminating manual intervention while achieving high adaptability through automated detection and response to performance variations.
4Reliability
If QoS monitoring is continuously performed, then service quality is maintained, but computational overhead increases
Solution Approach 1:
The orchestrator performs continuous QoS monitoring and uses feedback from metric comparisons to trigger resource reassignment only when violations occur. This feedback-based approach maintains service quality through continuous monitoring while reducing computational overhead by executing resource allocation changes only when necessary, rather than continuously reconfiguring resources.
Data Source
AI summary
Systems and techniques for end-to-end quality of service in edge computing environments are described herein. A set of telemetry measurements may be obtained for an ongoing dataflow between a device and a node of an edge computing system. A current key performance indicator (KPI) may be calculated for the ongoing dataflow. The current KPI may be compared to a target KPI to determine an urgency value. A set of resource quality metrics may be collected for resources of the network. The set of resource quality metrics may be evaluated with a resource adjustment model to determine available resource adjustments. A resource adjustment may be selected from the available resource adjustments based on an expected minimization of the urgency value. Delivery of the ongoing dataflow may be modified using the selected resource adjustment.