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 increased work backlogs, decreased operational performance, and potential violations of service level agreements (SLAs).
Innovation Solution
The implementation of smart job scheduling techniques that utilize both custom and generic backlog indicators associated with enqueued pipelines. These indicators help optimize workload scheduling by measuring time in unscheduled states and resource utilization, thereby minimizing late executing jobs and excessive computational burdens.
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 transforms the static fixed run frequency scheduling into a dynamic system that adapts to real-time workload conditions. The scheduler continuously monitors backlog indicators and adjusts scheduling decisions based on current system state, allowing the schedule to be flexible rather than rigid. This dynamic approach resolves the contradiction by maintaining operational efficiency through adaptation while preserving scheduling simplicity through automated decision-making.
Solution Approach 2:
The patent implements feedback mechanisms by monitoring backlog indicators (generic and custom) and using this information to adjust scheduling decisions. The system continuously gathers data about workload status, resource utilization, and execution delays, then feeds this information back into the scheduling algorithm. This closed-loop feedback system enables the scheduler to respond to changing conditions and optimize operational efficiency without complex manual intervention.
2Speed
If more computational resources are allocated to running tasks, then task execution speed improves, but work backlog increases due to resource contention
Solution Approach 1:
The patent changes the scheduling parameters from fixed time intervals to dynamic backlog-based metrics. Instead of allocating resources based on predetermined schedules, the system adjusts resource allocation parameters based on real-time backlog indicators. This allows the system to prioritize workloads with higher backlog values, dynamically changing the scheduling parameters to balance execution speed and backlog reduction.
Solution Approach 2:
The patent applies partial action by allocating computational resources selectively rather than uniformly. The scheduler identifies specific workloads that need immediate attention based on their backlog indicators and allocates resources to those particular tasks, rather than distributing resources evenly across all workloads. This targeted approach reduces overall backlog more efficiently while maintaining execution speed for critical tasks.
3Adaptability or versatility
If manual intervention is used to address end user concerns, then customization capability improves, but system automation deteriorates due to inability to systematically handle each concern
Solution Approach 1:
The patent implements self-service automation where the scheduling system automatically monitors its own performance metrics, detects backlog conditions, and adjusts scheduling decisions without human intervention. The system serves itself by continuously evaluating backlog indicators and making autonomous scheduling decisions, eliminating the need for manual attendance to end user concerns while maintaining high adaptability through automated response to changing conditions.
Solution Approach 2:
The patent uses feedback loops to enable the system to automatically respond to workload conditions and end user needs. By monitoring backlog indicators and feeding this information back into the scheduling algorithm, the system can automatically adapt to changing requirements and prioritize workloads accordingly, maintaining customization capability through automated decision-making based on real-time system state.
Data Source
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AI summary
Techniques are described for configuring a data platform to schedule workloads using backlog indicators. For instance, processing means 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 means may obtain a custom backlog indicator for at least a subset of the workloads. Processing means 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.