Scheduling System Endpoint Path Conversion for HPC Resource Allocation
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Solution Overview
Problem
Current scheduling processes in high-performance computing environments, such as grids and clusters, face inefficiencies in optimizing resource allocation and data transfers due to the complexity of multiple resource types and paths, leading to scalability issues and prolonged analysis times.
Innovation Solution
The method involves converting the compute environment's topology into endpoint-to-endpoint paths, mapping replica resources to available endpoints, and iteratively identifying cost schedules to optimize job processing by aggregating similar workload requests and using high-level global information to determine scheduling constraints, thereby reducing processor load and analysis time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional scheduling processes are used to manage resource allocation and data transfers in high-performance computing environments, then comprehensive resource management is achieved, but processor load increases and analysis time is prolonged
Solution Approach 1:
The patent segments the scheduling problem into two distinct phases: an offline planning phase that generates candidate schedules, and an online selection phase that chooses the optimal schedule. This segmentation allows complex scheduling decisions to be made in advance, reducing real-time processor load and analysis time while maintaining comprehensive resource management capabilities.
Solution Approach 2:
The system performs preliminary scheduling analysis and generates candidate schedules offline before actual job execution. By pre-computing multiple potential schedules and their associated costs, the system avoids performing complex analysis during real-time operation, thereby reducing processor load and analysis time during critical scheduling moments.
2Adaptability or versatility
If traditional scheduling processes are used to manage resource allocation and data transfers, then all resource types are managed, but device complexity increases
Solution Approach 1:
The patent introduces an intermediary scheduling layer that sits between resource requests and actual resource allocation. This intermediary component handles the complexity of managing multiple resource types and paths by translating high-level job requirements into detailed allocation plans, thereby maintaining adaptability while managing system complexity through abstraction.
Solution Approach 2:
The system changes parameters by representing scheduling decisions as cost-associated schedules with multiple attributes (execution time, resource usage, data transfer paths). By transforming the scheduling problem into a parameter-based optimization task, the system can manage diverse resource types through unified parameter manipulation rather than complex conditional logic.
3Manufacturing precision
If comprehensive scheduling optimization is performed for multiple resource types and paths, then resource allocation quality improves, but processor load increases
Solution Approach 1:
The patent divides comprehensive scheduling optimization into offline candidate generation and online selection stages. The computationally intensive optimization work is performed offline to generate pre-evaluated candidate schedules with associated costs, while online execution simply selects from these pre-computed options. This segmentation maintains high scheduling optimization quality while dramatically reducing real-time processor load.
Solution Approach 2:
The system generates multiple candidate schedules offline (excessive action) to ensure high-quality optimization coverage, but only evaluates and selects from these candidates during online operation (partial action). This approach allows comprehensive optimization to be performed in advance when processor load is less critical, while maintaining low real-time processor requirements.
Data Source
AI summary
Disclosed are systems, methods, computer readable media, and compute environments for establishing a schedule for processing a job in a distributed compute environment. The method embodiment comprises converting a topology of a compute environment to a plurality of endpoint-to-endpoint paths, based on the plurality of endpoint-to-endpoint paths, mapping each replica resource of a plurality of resources to one or more endpoints where each respective resource is available, iteratively identifying schedule costs associated with a relationship between endpoints and resources, and committing a selected schedule cost from the identified schedule costs for processing a job in the compute environment.