Cloudera, Inc. (20240378088). APPROACHES TO OPTIMIZING COMPUTE RESOURCE ALLOCATION FOR HEAVY WORKLOADS IN ELASTIC ENVIRONMENTS AND CLOUD DATA PLATFORM FOR IMPLEMENTING THE SAME simplified abstract
APPROACHES TO OPTIMIZING COMPUTE RESOURCE ALLOCATION FOR HEAVY WORKLOADS IN ELASTIC ENVIRONMENTS AND CLOUD DATA PLATFORM FOR IMPLEMENTING THE SAME
Organization Name
Inventor(s)
Sunil Govindan of Santa Clara CA (US)
Zhankun Tang of Santa Clara CA (US)
Siddharth Seth of Santa Clara CA (US)
Tarun Parimi of Santa Clara CA (US)
Vinod K. Vavilapalli of Santa Clara CA (US)
Tanu Ajmera of Santa Clara CA (US)
APPROACHES TO OPTIMIZING COMPUTE RESOURCE ALLOCATION FOR HEAVY WORKLOADS IN ELASTIC ENVIRONMENTS AND CLOUD DATA PLATFORM FOR IMPLEMENTING THE SAME - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240378088 titled 'APPROACHES TO OPTIMIZING COMPUTE RESOURCE ALLOCATION FOR HEAVY WORKLOADS IN ELASTIC ENVIRONMENTS AND CLOUD DATA PLATFORM FOR IMPLEMENTING THE SAME
Simplified Explanation: The resource management platform described in the abstract dynamically allocates compute resources to workloads to efficiently meet the resource requirements of a tenant, especially in scenarios with elastic compute resource availability.
Key Features and Innovation:
- Dynamic allocation of compute resources to workloads
- Scheduling engine and recommending engine for optimizing resource scaling
- Communication with external entities responsible for implementing changes on cloud infrastructure
Potential Applications: The technology can be applied in cloud computing environments, data centers, and any scenario where efficient resource allocation is crucial.
Problems Solved: The technology addresses the challenges of efficiently managing compute resources in dynamic and elastic environments, ensuring optimal resource utilization.
Benefits:
- Improved efficiency in resource allocation
- Cost-effective management of compute resources
- Enhanced scalability and flexibility in resource provisioning
Commercial Applications: Optimized Resource Management Platform for Cloud Computing Environments: This technology can be utilized by cloud service providers to enhance resource allocation efficiency and reduce operational costs.
Prior Art: Readers can explore prior research on resource management platforms, cloud computing optimization, and dynamic resource allocation algorithms to gain a deeper understanding of the technology.
Frequently Updated Research: Researchers are constantly developing new algorithms and strategies for resource management in dynamic environments, which can further enhance the efficiency and effectiveness of the described technology.
Questions about Resource Management Platforms: 1. How does the resource management platform optimize resource scaling in different scenarios? 2. What are the potential challenges in implementing a resource management platform in a cloud computing environment?
Original Abstract Submitted
introduced here is a resource management platform (also called a “resource manager”) that is able to dynamically allocate compute resources to workloads to accommodate resource requirements of a tenant in a more efficient and cost-effective manner, especially in scenarios where compute resource availability is elastic in nature. the resource manager can include a scheduling engine and a recommending engine that together are able to optimize the scaling up and down of compute resources in different scenarios. normally, the resource manager can communicate with a resource-aware, external entity that may be responsible for implementing appropriate changes on a cloud infrastructure. for example, the external entity may be responsible for adding or removing nodes assigned to a given tenant, as well as obtaining relevant attributes of those compute resources from a provider of the cloud infrastructure.
