Local Resource Broker for Dynamic Compute Scaling
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Solution Overview
Problem
Current resource management systems in high-performance computing environments face challenges in efficiently utilizing on-demand compute resources, leading to unnecessary infrastructure costs and inadequate peak demand handling.
Innovation Solution
An on-demand system and method for managing resources, which includes a local resource broker that communicates instructions to an on-demand compute environment to dynamically modify resources, allowing for peak resource utilization only when needed, thereby reducing infrastructure costs and enabling flexible resource provisioning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If infrastructure is built to accommodate peak demand, then system reliability is improved, but device complexity and cost increase
Solution Approach 1:
The patent implements dynamic resource allocation where the compute environment transitions between different operational states (local-only mode and hybrid mode) based on real-time workload conditions. The resource broker dynamically provisions remote resources when local capacity is insufficient, allowing the system to adapt its infrastructure complexity to actual demand rather than maintaining fixed peak-capability infrastructure.
Solution Approach 2:
The system design allows local compute resources to serve multiple functions: handling routine workloads independently and serving as a bridge to remote resources when needed. The resource broker itself performs multiple roles including local resource management, remote resource provisioning, and workload routing, reducing the need for separate dedicated infrastructure components.
2Adaptability or versatility
If on-demand resource provisioning is implemented, then adaptability is improved, but device complexity increases
Solution Approach 1:
The resource broker acts as an intermediary layer between local compute resources and remote on-demand resources. It abstracts the complexity of remote resource provisioning, authentication, and management from both the user and the local resources, presenting a simplified interface while handling the complex coordination of cross-environment resource allocation.
Solution Approach 2:
The system implements automated resource provisioning where the resource broker autonomously evaluates workload requirements, identifies capacity shortages, and provisions remote resources without manual intervention. The broker self-manages the entire lifecycle including resource allocation, monitoring, and cleanup, reducing operational complexity despite increased adaptability.
Data Source
AI summary
Disclosed is an on-demand system and method for managing resources in an on-demand compute environment from a local compute environment. The method includes receiving information at a local resource broker that is associated with resources within an on-demand compute environment, based on the information, communicating instructions from the local resource broker to the on-demand compute environment and modifying resources associated with the on-demand compute environment based on the instructions.