18078343. INTELLIGENT MANAGEMENT OF WORKLOADS IN HETEROGENEOUS COMPUTING ENVIRONMENT simplified abstract (Dell Products L.P.)

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INTELLIGENT MANAGEMENT OF WORKLOADS IN HETEROGENEOUS COMPUTING ENVIRONMENT

Organization Name

Dell Products L.P.

Inventor(s)

Parminder Singh Sethi of Ludhiana (IN)

Nithish Kote of Bangalore (IN)

Durai S. Singh of Bangalore (IN)

INTELLIGENT MANAGEMENT OF WORKLOADS IN HETEROGENEOUS COMPUTING ENVIRONMENT - A simplified explanation of the abstract

This abstract first appeared for US patent application 18078343 titled 'INTELLIGENT MANAGEMENT OF WORKLOADS IN HETEROGENEOUS COMPUTING ENVIRONMENT

Simplified Explanation: The patent application describes intelligent workload management techniques in a heterogeneous computing environment using machine learning algorithms.

  • Key Features and Innovation:
   * Obtaining identifying information for different workload types and server configurations.
   * Mapping workload types to appropriate server configurations.
   * Distributing workloads to servers based on the mapping for execution.
  • Potential Applications:
   * Cloud computing environments
   * Data centers
   * Edge computing systems
  • Problems Solved:
   * Efficient workload distribution in heterogeneous computing environments
   * Optimizing server configurations for different types of workloads
  • Benefits:
   * Improved performance and resource utilization
   * Automated workload management
   * Scalability and flexibility in computing environments
  • Commercial Applications:
   * Cloud service providers
   * IT companies offering data center solutions
   * Companies utilizing edge computing for IoT applications
  • Prior Art:
   Prior art related to workload management in heterogeneous computing environments can be found in research papers, patents, and industry publications related to cloud computing and data center management.
  • Frequently Updated Research:
   Ongoing research in machine learning algorithms for workload optimization and resource allocation in computing environments.
  • Questions about Workload Management in Heterogeneous Computing Environments:
   * How does machine learning improve workload management in heterogeneous computing environments?
       - Machine learning algorithms can analyze patterns in workload types and server configurations to optimize workload distribution.
   * What are the challenges in implementing intelligent workload management techniques in real-world scenarios?
       - Challenges may include data privacy concerns, integration with existing systems, and scalability issues.


Original Abstract Submitted

Intelligent workload management techniques in a heterogenous computing environment are disclosed. For example, a method comprises obtaining first identifying information associated with a set of workload types, and obtaining second identifying information associated with a set of server configurations associated with a computing environment, wherein one or more server configurations in the set of server configurations are different than one or more other server configurations in the set of server configurations. The method then maps individual ones of the set of workload types to individual appropriate ones of the set of server configurations based on the obtained first and second identifying information, and causes distribution of one or more workloads to one or more servers in the computing environment, in accordance with the mapping, for execution thereon. At least a portion of the obtaining and mapping steps are performed using one or more machine learning algorithms.

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