Batch Job Resource Allocation via Multi-Dimensional Statistical Representation
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Solution Overview
Problem
Managing the efficient allocation of cloud resources to fulfill varying computational workloads in distributed systems is challenging, as existing technologies struggle to precisely match job resource requirements with available resources, often leading to over-allocation or under-allocation.
Innovation Solution
A batch job management service that implements event-driven schedulers and a resource controller to generate multi-dimensional statistical representations of job resource requirements, allowing for precise allocation decisions by separating resource allocation from job scheduling, and utilizing programmatic interfaces for clients to specify resource constraints and dependencies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If cloud resources are allocated to fulfill varying computational workloads, then resource capacity and flexibility are improved, but resource allocation precision deteriorates leading to over-allocation or under-allocation
Solution Approach 1:
The patent segments resource allocation into two independent components: a scheduler that manages job queues and resource requests, and a resource controller that handles actual resource allocation decisions. This segmentation allows each component to specialize in its function, improving overall allocation precision while maintaining flexibility. The scheduler breaks down workload management into individual job entities with specific resource requirements, enabling precise tracking and allocation.
Solution Approach 2:
The patent introduces an intermediary layer between job submission and resource allocation through the batch job management service. This intermediary receives job requests, analyzes resource requirements, coordinates with both the scheduler and resource controller, and manages the allocation process. This intermediary ensures precise matching of job requirements with available resources while maintaining system flexibility and adaptability.
2Device complexity
If resource allocation is integrated with job scheduling, then system simplicity is maintained, but allocation efficiency deteriorates due to inability to separately optimize resource management
Solution Approach 1:
The system segments the traditionally integrated scheduling and allocation functions into separate modular components: a scheduler for job queue management and a resource controller for allocation decisions. This segmentation enables independent optimization of each function while maintaining clear interfaces between them, thus improving allocation efficiency without creating excessive complexity.
Solution Approach 2:
The batch job management service acts as a universal coordinator that handles multiple functions: receiving job submissions, managing job queues, analyzing resource requirements, coordinating with schedulers, and controlling resource allocation. This multi-functional service simplifies the overall system architecture while enabling efficient separate optimization of scheduling and allocation processes.
3Measurement precision
If statistical representations of job requirements are generated, then allocation precision is improved, but computational overhead and processing time increase
Solution Approach 1:
The scheduler performs preliminary analysis of job requirements when jobs are submitted or queued, generating statistical representations of resource needs in advance. This preliminary action includes analyzing job characteristics, estimating resource requirements, and preparing allocation data before actual resource allocation is needed. By performing this analysis beforehand, the system achieves precise allocation without adding significant processing time during the critical allocation moment.
Data Source
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AI summary
A scheduler of a batch job management service determines that a set of resources a client is insufficient to execute one or more jobs. The scheduler prepares a multi-dimensional statistical representation of resource requirements of the jobs, and transmits it to a resource controller. The resource controller uses the multi-dimensional representation and resource usage state information to make resource allocation change decisions.