18046050. MANAGING COMPUTING WORKLOADS WITHIN A COMPUTING ENVIRONMENT simplified abstract (Dell Products L.P.)
Contents
- 1 MANAGING COMPUTING WORKLOADS WITHIN A COMPUTING ENVIRONMENT
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 MANAGING COMPUTING WORKLOADS WITHIN A COMPUTING ENVIRONMENT - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 How does this technology handle dynamic changes in computing workloads within the environment?
- 1.11 What security measures are in place to protect sensitive data within the computing environment?
- 1.12 Original Abstract Submitted
MANAGING COMPUTING WORKLOADS WITHIN A COMPUTING ENVIRONMENT
Organization Name
Inventor(s)
RISHI Mukherjee of Bangalore (IN)
RAVISHANKAR N. Kanakapura of Bangalore (IN)
PRASOON KUMAR Sinha of Bangalore (IN)
RAVEENDRA BABU Madala of Bangalore (IN)
MANAGING COMPUTING WORKLOADS WITHIN A COMPUTING ENVIRONMENT - A simplified explanation of the abstract
This abstract first appeared for US patent application 18046050 titled 'MANAGING COMPUTING WORKLOADS WITHIN A COMPUTING ENVIRONMENT
Simplified Explanation
The patent application focuses on managing computing workloads within a computing environment by assessing various parameters of data center elements, computing clusters, and computing nodes. The goal is to optimize the allocation of computing resources based on factors such as power device health, processing load, and computing cost.
- Identifying computing parameters of data center elements, computing clusters, and computing nodes
- Determining the health of the power device for each computing cluster
- Assessing the processing load of each computing node within a cluster
- Calculating the computing cost associated with the geo-location of each computing node
- Evaluating the availability of computing resources within each cluster based on the above parameters
- Generating a ranking of each computing cluster based on resource availability
Potential Applications
This technology could be applied in cloud computing environments, data centers, and distributed computing systems to optimize resource allocation and improve overall system performance.
Problems Solved
This technology addresses the challenge of efficiently managing computing workloads within complex computing environments, ensuring that resources are allocated effectively based on various parameters.
Benefits
The benefits of this technology include improved system performance, optimized resource allocation, reduced computing costs, and enhanced scalability in computing environments.
Potential Commercial Applications
Potential commercial applications of this technology include cloud service providers, data center operators, and companies with large-scale computing infrastructure looking to optimize resource allocation and improve overall efficiency.
Possible Prior Art
One possible prior art in this field could be related to resource management algorithms in cloud computing or data center environments, focusing on optimizing resource allocation based on various parameters.
Unanswered Questions
How does this technology handle dynamic changes in computing workloads within the environment?
The patent application does not specifically address how the system adapts to fluctuations in computing workloads or how it dynamically reallocates resources in real-time.
What security measures are in place to protect sensitive data within the computing environment?
The patent application does not mention any security measures or protocols to safeguard sensitive data within the computing environment.
Original Abstract Submitted
Managing computing workloads within a computing environment including identifying computing parameters of datacenter elements of each computing cluster of a computing environment; for each computing cluster of the computing environment: determining a health of the power device of the computing cluster; for each computing node of the computing cluster: determining a processing load of the computing node; determining a computing cost associated with a geo-location of the computing node; calculating, for each computing cluster, an availability of computing resources of the computing cluster based on the computing parameters of the data center elements of the computing cluster, the health of the power device of the computing cluster, the processing load of each computing node of the computing cluster, and the computing cost of each computing node of the computing cluster; generating a ranking of each computing cluster based on the availability of the computing resources of the computing cluster.
