Centralized Randomized Scheduling for Hypothesis-Based Workloads

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Existing workflow engines lack the ability to dynamically schedule hypothesis-based workloads across multiple environments, considering time and environment-specific constraints, leading to inefficiencies in chaos engineering and other applications.

Innovation Solution

A centralized scheduler that utilizes a rule registry and historic data to determine the exact time, environment, and target for hypothesis-based workload execution, filtering out unsuitable workloads based on environment rules and KPIs, and adjusting execution parameters for optimal scheduling.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If workflow engines use time-based scheduling, then scheduling capability is provided, but dynamic environment-based scheduling is not achieved

Engineering Contradiction:

Improveenvironment-based scheduling capabilityVSAvoidscheduling system complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The scheduling system is segmented into independent components: environment rules registry, workload definitions, selection algorithm, and execution queue. This modular architecture enables dynamic environment-based scheduling while maintaining manageable system complexity through clear separation of concerns.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

An environment rules registry acts as an intermediary layer between workflow engines and execution environments. This registry stores and manages environment-specific rules, allowing the scheduler to dynamically adapt workload execution to environment constraints without hardcoding complex scheduling logic in the workflow engine.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Reliability

If workloads are scheduled without environment constraints, then scheduling simplicity is maintained, but workload suitability is compromised

Engineering Contradiction:

Improveworkload suitabilityVSAvoidscheduling operation simplicity

Core Design Contradiction:

ReliabilityVSEase of operation

Solution Approach 1:

Environment rules and workload definitions are registered and validated in advance before execution. The selection algorithm retrieves pre-defined environment constraints and workload requirements, ensuring workload suitability is determined before scheduling decisions are made, rather than during execution.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The selection algorithm implements feedback mechanisms by continuously querying the environment rules registry to check whether scheduled workloads comply with current environment constraints. This feedback loop ensures workload suitability while maintaining operational simplicity through automated compliance verification.

Inventive Principle:

Principle #23Feedback

3Productivity

If manual scheduling is used, then scheduling control is achieved, but time and resource efficiency are reduced

Engineering Contradiction:

Improvescheduling efficiencyVSAvoidscheduling automation level

Core Design Contradiction:

ProductivityVSExtent of automation

Solution Approach 1:

The scheduling system performs self-service by automatically selecting appropriate workloads for execution environments based on pre-registered environment rules and workload definitions. The selection algorithm autonomously matches workloads to environments without manual intervention, significantly improving scheduling efficiency while maintaining full automation.

Inventive Principle:

Principle #25Self-service

4Reliability

If hypothesis-based workloads are executed without randomized scheduling, then execution consistency is maintained, but system robustness testing is limited

Engineering Contradiction:

Improvesystem robustnessVSAvoidscheduling algorithm complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The selection algorithm dynamically determines workload execution parameters including randomization of hypothesis-based workload execution. This dynamic capability allows the system to switch between consistent and randomized execution modes as needed, enhancing system robustness testing while managing algorithmic complexity through context-aware decision-making.

Inventive Principle:

Principle #15Dynamics

Data Source

PatentUS12405820B2Central randomized scheduler for hypothesis-based workloads

Publication Date: 2025.09.02 INTERNATIONAL BUSINESS MACHINE CORPORATION

AI summary

A system may include a memory and a processor in communication with the memory. The processor may be configured to perform operations. The operations may include loading environment rules and available workloads and retrieving workload data for the available workloads, execution environment data of an execution environment, and historic workload data. The operations may include selecting a workload for the execution environment and determining execution settings for the workload. The operations may include submitting the workload to an execution queue for execution in the execution environment according to the execution settings.