Fog Orchestrator Resource Partitioning for Multi-Tenant Bottlenecks
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Solution Overview
Problem
Fog computing networks face challenges in managing competing client applications in multi-tenant systems, leading to service bottlenecks and performance deterioration due to inadequate resource allocation and management.
Innovation Solution
A fog orchestrator is configured to determine and allocate fog node resources, receive reservation priority values from client devices, and partition client applications based on these values to optimize resource usage and manage competing applications effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If fog networks use distributed and ad hoc architecture to provide computing resources closer to the edge, then performance and latency are improved, but resource management complexity increases
Solution Approach 1:
The patent introduces a fog orchestrator as an intermediary component that manages resource allocation and application partitioning across the distributed fog network. The orchestrator receives resource allocation requests, determines optimal fog node assignments, and coordinates resource distribution, thereby simplifying the management complexity while maintaining the performance benefits of distributed architecture.
2Ease of operation
If fog networks service multiple competing client applications using first-in-first-out mechanism, then system simplicity is maintained, but resource allocation efficiency deteriorates
Solution Approach 1:
The patent changes the service mechanism from a simple first-in-first-out approach to a priority-based allocation system. The fog orchestrator evaluates multiple parameters including application priority levels, resource availability, and performance requirements to dynamically determine resource allocation decisions, thereby improving efficiency while adding manageable complexity.
Solution Approach 2:
The resource allocation mechanism transitions from static first-in-first-out to dynamic priority-based allocation. The system continuously adjusts resource distribution based on current network conditions, application demands, and performance metrics, allowing optimal resource utilization while maintaining operational simplicity through automated decision-making.
3Device complexity
If fog networks allocate resources without performance-driven partitioning, then system simplicity is maintained, but service bottlenecks occur leading to performance deterioration
Solution Approach 1:
The patent implements performance-driven partitioning as a preliminary action where the fog orchestrator proactively analyzes application requirements and pre-determines optimal resource allocation strategies before bottlenecks occur. This anticipatory approach partitions applications based on their performance needs and allocates resources accordingly, preventing service degradation before it happens.
Solution Approach 2:
The system incorporates feedback mechanisms where the fog orchestrator continuously monitors resource utilization, application performance metrics, and network conditions. Based on this feedback, the orchestrator dynamically adjusts resource allocation and partitioning decisions to optimize service performance and prevent bottlenecks, creating a closed-loop control system.
Data Source
AI summary
Various implementations disclosed herein enable improved allocation of fog node resources, which supports performance driven partitioning of competing client applications. In various implementations, methods are performed by a fog orchestrator configured to determine allocations of fog resources for competing client applications and partition the competing client applications based on the fog resource allocations. Methods include receiving reservation priority values (RPVs) associated with a plurality of client applications competing for a contested fog node resource, transmitting, to a subset of client devices, a request to provide updated RPVs, and awarding the contested fog node resource to one of the plurality of client applications based on the received RPVs and any updated RPVs. In various implementations, methods also include determining, for each of the plurality of client applications, a respective mapping for a respective plurality of separable components of the client application based on the awarded contested fog node resource.