Automated Workload Profiling for Distributed Computing Bottlenecks

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Solution Overview

Problem

Optimizing workload performance in distributed computing environments is challenging due to complex resource consumption analysis and the need for manual scheduling and tuning, which requires expertise and privileges often lacking in regular users.

Innovation Solution

Automated workload profiling and management software adjusts properties based on performance profiles to identify and mitigate bottlenecks by selecting appropriate tuning profiles and migrating workloads to nodes with sufficient resources.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If manual workload analysis and tuning is performed, then performance optimization can be achieved, but it requires expertise and privileges that regular users lack

Engineering Contradiction:

Improveperformance optimizationVSAvoiduser accessibility

Core Design Contradiction:

ReliabilityVSEase of operation

Solution Approach 1:

The system enables workloads to automatically profile themselves and identify performance bottlenecks without requiring external expert intervention. The workload autonomously collects resource consumption data, analyzes performance issues, and applies tuning parameters, making the optimization process self-service rather than requiring specialized user knowledge or privileges.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

An automated workload profiling system acts as an intermediary between the workload and the distributed computing environment resources. This intermediary automatically analyzes resource consumption patterns, identifies bottlenecks, and adjusts tuning parameters, eliminating the need for users to directly manage complex performance optimization tasks.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Ease of operation

If automated workload profiling and tuning is implemented, then ease of operation improves, but system complexity increases

Engineering Contradiction:

Improveautomated tuningVSAvoidsystem architecture

Core Design Contradiction:

Ease of operationVSDevice complexity

Solution Approach 1:

The workload profiling system is designed to be universally applicable across different types of workloads and distributed computing environments. By creating a multi-functional automated system that can handle various resource types (CPU, memory, storage, network) and workload characteristics, the solution manages complexity through standardization rather than requiring separate mechanisms for each scenario.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The system manages complexity by focusing on adjusting tuning parameters rather than fundamentally changing system architecture. The automated profiler analyzes resource consumption and modifies operational parameters such as scheduling priorities, resource allocation settings, and configuration values, thereby achieving performance optimization through parameter tuning rather than structural complexity.

Inventive Principle:

Principle #35Parameter changes

3Manufacturing precision

If detailed resource consumption analysis is performed, then performance tuning accuracy improves, but the time and computational overhead increases

Engineering Contradiction:

Improvetuning accuracyVSAvoidprofiling time

Core Design Contradiction:

Manufacturing precisionVSLoss of time

Solution Approach 1:

The workload profiling system implements partial monitoring by focusing on collecting resource consumption data for specific parameters and time periods relevant to performance bottlenecks rather than continuously monitoring all system metrics. This selective approach maintains tuning accuracy while reducing the time and computational overhead associated with comprehensive profiling.

Inventive Principle:

Principle #16Partial or excessive action

Solution Approach 2:

The system performs preliminary profiling during workload execution to gather resource consumption data before performance issues become critical. By proactively collecting and analyzing resource usage patterns in advance, the system can identify bottlenecks early and apply tuning parameters preventively, reducing the need for time-consuming reactive analysis later.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS11561843B2Automated performance tuning using workload profiling in a distributed computing environment

Publication Date: 2023.01.24 RED HAT INC

AI summary

Workload profiling can be used in a distributed computing environment for automatic performance tuning. For example, a computing device can receive a performance profile for a workload in a distributed computing environment. The performance profile can indicate resource usage by the workload in the distributed computing environment. The computing device can determine a performance bottleneck associated with the workload based on the resource usage specified in the performance profile. A tuning profile can be selected to reduce the performance bottleneck associate with the workload. The computing device can output a command to adjust one or more properties of the workload in accordance with the tuning profile to reduce the performance bottleneck associated with the workload.