Smart Job Scheduling with Backlog Indicators
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Solution Overview
Problem
Data platforms face challenges in efficiently scheduling tasks due to limited computational resources, leading to delayed or missed critical job executions, increased work backlogs, and decreased operational performance.
Innovation Solution
The implementation of smart job scheduling techniques that utilize both custom and generic backlog indicators associated with enqueued pipelines, allowing for optimized scheduling and improved resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If workloads are scheduled based on fixed run frequency, then scheduling simplicity is maintained, but operational efficiency deteriorates due to excess utilization rates and excessive execution delays
Solution Approach 1:
The patent implements dynamic workload scheduling by replacing fixed run frequency with a backlog indicator-based scheduling mechanism. The scheduler continuously monitors backlog indicators and adjusts scheduling decisions in real-time, transforming the static scheduling system into a dynamic one that adapts to changing system conditions and resource availability.
Solution Approach 2:
The patent introduces feedback loops where the scheduler receives backlog indicator values from previous workload executions and uses this feedback to inform future scheduling decisions. This closed-loop control mechanism allows the system to learn from past performance and continuously optimize scheduling to improve operational efficiency.
2Productivity
If computational resources are limited, then resource allocation efficiency is improved, but workload execution timeliness deteriorates causing critical jobs to miss deadlines
Solution Approach 1:
The patent introduces backlog indicators as an intermediary metric that mediates between limited computational resources and workload execution requirements. This intermediary provides a quantifiable measure of workload urgency and resource demand, enabling the scheduler to make informed decisions that balance resource allocation efficiency with execution timeliness for critical jobs.
Solution Approach 2:
The patent changes the scheduling parameter from fixed run frequency to dynamic backlog indicator values. By using configurable weights and customizable backlog indicators that reflect actual system state, the scheduler can prioritize critical workloads and adjust resource allocation dynamically, ensuring timeliness while maintaining overall efficiency.
3Productivity
If work backlogs increase, then system capacity utilization is improved, but operational performance deteriorates and service level agreements are missed
Solution Approach 1:
The patent implements preliminary action by calculating and monitoring backlog indicators before workloads are scheduled. This advance measurement allows the system to proactively identify and prioritize workloads that are at risk of missing deadlines, enabling preemptive scheduling decisions that prevent performance deterioration before it occurs.
Data Source
AI summary
Techniques are described for configuring a data platform to schedule workloads using backlog indicators. For instance, processing circuitry of a data platform may obtain a generic backlog indicator for workloads to execute via the data platform. Each of the workloads may specify one or more storage system maintenance operations. Processing circuitry may obtain a custom backlog indicator for at least a subset of the workloads. A priority manager may calculate a single weighted backlog indicator value for each of the workloads by applying configurable weights to the generic backlog indicators and the custom backlog indicators. The data platform may schedule the workloads for execution on the data platform based on the single weighted backlog indicator value calculated for each workload. In some examples, the data platform processes the workloads according to the scheduling.