Workload Scheduler Using Historical Data Assessment
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Solution Overview
Problem
Existing distributed computing environments face delays and inefficiencies in workload processing due to inadequate resource management and scheduling techniques, which affect throughput.
Innovation Solution
A method that utilizes a scheduler to assess historical processing data and resource availability across multiple computing platforms, determining the optimal timing for workload processing based on data and process availability, and initiating processing only when sufficient resources are available, leveraging a knowledge base to prioritize and manage workloads efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional scheduling techniques (time-based, sequence-based, or priority-based) are used to process workloads, then workloads can be scheduled and executed, but delays occur and throughput is adversely affected due to inadequate resource management
Solution Approach 1:
The system performs preliminary actions by assessing historical processing data and determining resource availability requirements before initiating workload processing. The scheduler evaluates historical execution statistics and resource availability in advance, allowing workloads to be queued and prepared for execution when conditions are favorable, thereby reducing processing delays and improving throughput.
2Productivity
If workloads are processed without assessing resource availability and historical data, then processing can start immediately, but resource insufficiency causes delays and reduced efficiency
Solution Approach 1:
The system implements feedback by continuously monitoring and assessing historical processing data and current resource availability before initiating workload processing. The scheduler uses this feedback information to make informed decisions about when to start processing, ensuring that resources are sufficient and thereby improving processing efficiency while minimizing delays.
3Productivity
If multiple stages of processing are implemented with historical data assessment, then resource availability is optimized and throughput is improved, but the scheduling complexity increases
Solution Approach 1:
The scheduling system is segmented into distinct functional components: a workload assessment module that evaluates historical processing data, a resource availability assessment module that checks current resource status, and a scheduler that integrates both assessments to make processing decisions. This segmentation manages complexity by dividing the system into specialized modules with clear responsibilities.
Solution Approach 2:
The scheduler acts as an intermediary between workload requests and processing execution. It mediates by assessing both historical data and current resource availability, then deciding when to initiate processing. This intermediary role simplifies the overall system by centralizing the decision-making logic and coordinating between different assessment functions.
Data Source
AI summary
A technique for workload processing includes receiving a request to process a workload by a scheduler. A determination is made whether multiple stages of processing are needed to process data of the workload. If so, a determination is made of a process needed for each stage and historical processing data is accessed to determine historical execution statistics associated with previous processing requests for a corresponding type of the workload. At each stage, a determination is made whether the data of the workload is available for processing and, if so, whether the respective process of the workload is available. If available, a determination is made as to resource availability on a computing platform for processing the workload and whether the resources available are sufficient to process the workload based on the historical execution statistics. If sufficient resources are available, processing of the workload is initiated for the respective stage.