Dell products l.p. (20240126669). MANAGING POWER CONSUMPTION FOR A COMPUTING CLUSTER simplified abstract
Contents
- 1 MANAGING POWER CONSUMPTION FOR A COMPUTING CLUSTER
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 MANAGING POWER CONSUMPTION FOR A COMPUTING CLUSTER - 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 Original Abstract Submitted
MANAGING POWER CONSUMPTION FOR A COMPUTING CLUSTER
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 POWER CONSUMPTION FOR A COMPUTING CLUSTER - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240126669 titled 'MANAGING POWER CONSUMPTION FOR A COMPUTING CLUSTER
Simplified Explanation
The patent application focuses on managing power consumption for a computing cluster by optimizing the allocation of workloads among the individual nodes (IHS) based on their power storage consumption. This involves executing I/O computing workloads, calculating power usage of each node, determining accumulated power consumption of all nodes, calculating power consumption of the disk array, and then determining the power storage consumption of each node based on these factors.
- Executing I/O computing workloads at each node associated with movement of block storage data between the disk array and the node.
- Determining power usage of each node during the execution of I/O computing workloads.
- Calculating accumulated power consumption of all nodes by summing up the power usage of each node.
- Calculating power consumption of the disk array during movement of block storage data.
- Determining power storage consumption of each node based on its power usage, accumulated power consumption, and disk array power consumption.
- Allocating additional workloads among the nodes based on their power storage consumption.
Potential Applications
This technology can be applied in data centers, cloud computing environments, and large-scale computing clusters to optimize power consumption and improve overall efficiency.
Problems Solved
This technology addresses the challenge of managing power consumption in computing clusters, ensuring that resources are allocated efficiently and effectively to minimize energy usage.
Benefits
The benefits of this technology include reduced energy costs, improved performance of computing clusters, and a more sustainable approach to managing power consumption in data-intensive environments.
Potential Commercial Applications
Potential commercial applications of this technology include data center management software, cloud computing platforms, and energy-efficient computing solutions for businesses and organizations.
Possible Prior Art
One potential prior art in this field is the use of power management software in data centers to optimize energy usage and reduce costs. Another example could be research on workload allocation algorithms for computing clusters to improve resource utilization.
Unanswered Questions
How does this technology impact the overall efficiency of computing clusters?
This technology improves the efficiency of computing clusters by optimizing power consumption and workload allocation among the individual nodes, leading to better performance and reduced energy costs.
What are the potential cost savings associated with implementing this technology?
By optimizing power consumption and workload allocation, organizations can achieve significant cost savings in terms of energy bills and operational expenses.
Original Abstract Submitted
managing power consumption for computing cluster, including for each ihs of the computing cluster: executing i/o computing workloads at the ihs associated with movement of block storage data, stored at a disk array in communication with the ihs, between the disk array and the ihs; during execution of the i/o computing workloads, determining an i/o power usage of the ihs; calculating an accumulated i/o power consumption of the plurality of ihs based on a summation of the i/o power usage of each of the ihs; during movement of the block storage data, calculating a power consumption of the disk array; calculating, for each ihs, a power storage consumption of the ihs based on the i/o power usage of the ihs, the accumulated i/o power consumption, and the power consumption of the disk array; allocating additional workloads among the plurality of ihs based on the power storage consumption of each ihs.
