18677326. WORKLOAD MANAGEMENT USING A TRAINED MODEL simplified abstract (Hewlett Packard Enterprise Development LP)

WORKLOAD MANAGEMENT USING A TRAINED MODEL

Organization Name

Hewlett Packard Enterprise Development LP

Inventor(s)

Mayukh Dutta of Bangalore (IN)

Aesha Dhar Roy of Bangalore (IN)

Manoj Srivatsav of Bangalore (IN)

Ganesha Devadiga of Bangalore (IN)

Geethanjali N. Rao of Bangalore (IN)

Prasenjit Saha of Bangalore (IN)

Jharna Aggarwal of Bangalore (IN)

WORKLOAD MANAGEMENT USING A TRAINED MODEL - A simplified explanation of the abstract

This abstract first appeared for US patent application 18677326 titled 'WORKLOAD MANAGEMENT USING A TRAINED MODEL

Simplified Explanation

The patent application describes a system that creates a training data set based on features of sample workloads, with labels associated with load indicators in a computing environment. The system groups workloads into clusters and computes parameters representing their contributions to the overall load. Workload management is then performed based on these parameters.

Key Features and Innovation

• Creation of a training data set based on workload features and load indicators.
• Grouping of workloads into clusters for analysis.
• Computation of parameters to manage workloads effectively.

Potential Applications

This technology could be applied in various industries where workload management is crucial, such as cloud computing, data centers, and network optimization.

Problems Solved

The system addresses the challenge of efficiently managing workloads in a computing environment by analyzing workload clusters and their contributions to the overall load.

Benefits

• Improved workload management efficiency.
• Enhanced performance optimization in computing environments.
• Better resource allocation based on workload characteristics.

Commercial Applications

• Cloud service providers could use this technology to optimize resource allocation and improve service performance.
• Data centers could benefit from more efficient workload management, leading to cost savings and better overall performance.

Prior Art

Readers interested in prior art related to workload management systems in computing environments could explore research papers, patents, and industry publications in the field of cloud computing, data center management, and workload optimization.

Frequently Updated Research

Researchers in the field of workload management and optimization in computing environments are constantly exploring new algorithms and techniques to improve system performance and resource utilization. Stay updated on the latest advancements in this area for potential enhancements to the described technology.

Questions about Workload Management

How does workload clustering improve load management efficiency?

Workload clustering allows for a more granular analysis of different types of workloads, enabling the system to allocate resources more effectively based on their specific characteristics.

What are the potential challenges in implementing this technology in real-world computing environments?

Implementing this technology may require significant computational resources and integration with existing workload management systems, posing challenges in terms of scalability and compatibility.

Original Abstract Submitted

In some examples, a system creates a training data set based on features of sample workloads, the training data set comprising labels associated with the features of the sample workloads, where the labels are based on load indicators generated in a computing environment relating to load conditions of the computing environment resulting from execution of the sample workloads. The system groups selected workloads into a plurality of workload clusters based on features of the selected workloads, and computes, using a model trained based on the training data set, parameters representing contributions of respective workload clusters of the plurality of workload clusters to a load in the computing environment. The system performs workload management in the computing environment based on the computed parameters.