Network Flow-Based Hardware Allocation in Edge Computing
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Solution Overview
Problem
Existing edge computing platforms face resource constraints due to diverse and dynamic network connectivity, leading to inefficient resource allocation and interference with service level agreements (SLAs).
Innovation Solution
An intelligent framework that learns workload traffic flow characteristics to dynamically negotiate and allocate network and processing resources, selecting optimal network services for each workload in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If static resource allocation is used in edge computing platforms, then device complexity is reduced, but resource utilization efficiency deteriorates and service level agreements cannot be met under dynamic network conditions
Solution Approach 1:
The patent implements dynamic resource allocation by continuously monitoring network flow characteristics and automatically adjusting resource assignment based on real-time conditions. The system transitions from static pre-configured allocation to dynamic adaptive allocation, where resources are reassigned based on changing network conditions and workload requirements, thereby improving resource utilization efficiency without requiring overly complex manual intervention systems.
Solution Approach 2:
The patent employs machine learning models that automatically learn from network flow patterns and make autonomous decisions about resource allocation. The system self-adjusts by detecting traffic characteristics and independently optimizing resource distribution, eliminating the need for complex external management while improving productivity through intelligent autonomous operation.
2Reliability
If hardware partitioning mechanisms are implemented to support multiple services, then service isolation and reliability are improved, but resource flexibility and adaptability to dynamic conditions deteriorate
Solution Approach 1:
The patent creates a layered architecture where static hardware partitioning provides baseline service isolation and reliability, while a dynamic software layer on top continuously monitors network conditions and adjusts resource allocation within partition boundaries. This allows the system to maintain service isolation guarantees while adapting to dynamic traffic patterns through real-time flow-based resource management.
Solution Approach 2:
The patent pre-configures hardware partitioning structures to ensure service isolation and reliability guarantees are in place before dynamic conditions arise. These preliminary partitioning mechanisms establish the foundation for SLA compliance, while subsequent dynamic adjustments operate within these pre-established boundaries to maintain both reliability and flexibility.
3Reliability
If network resources are over-allocated to ensure SLA compliance, then service reliability is improved, but resource waste increases
Solution Approach 1:
The patent implements continuous feedback loops that monitor actual network flow characteristics, resource utilization metrics, and SLA compliance status. Based on this feedback, the system dynamically adjusts resource allocation to match actual demand, preventing both over-allocation and under-allocation. This feedback-driven approach ensures SLA compliance while minimizing resource waste by allocating exactly what is needed.
Solution Approach 2:
The patent changes resource allocation parameters dynamically based on observed network flow patterns and performance metrics. Instead of fixed over-allocation, the system adjusts allocation parameters in real-time to match actual service requirements, thereby maintaining reliability while reducing the energy loss associated with allocating more resources than necessary.
Data Source
AI summary
System and techniques for network flow-based hardware allocation are described herein. A workload for is obtained for execution. Here, the workload includes a flow that has a processing component and a network component. Then, during execution of the workload, the flow is repeatedly profiled and assigned a network service and a processing service during a next execution based on a network metric and a processing metric obtained from the profiling.