Capital One Services, LLC

Santosh Bardwaj

Subodh Kumar

Tao Tao

Jeremy Gerstle

Yihui Tang

Il Sun Yoo

Brian Eugley

Ii Sun Yoo

Systems and methods are disclosed for managing workload among server clusters is disclosed. According to certain embodiments, the system may include a memory storing instructions and a processor. The processor may be configured to execute the instructions to determine historical behaviors of the server clusters in processing a workload. The processor may also be configured to execute the instructions to construct cost models for the server clusters based at least in part on the historical behaviors. The cost model is configured to predict a processor utilization demand of a workload. The processor may further be configured to execute the instructions to receive a workload and determine efficiencies of processing the workload by the server clusters based at least in part on at least one of the cost models or an execution plan of the workload.

Automated server workload management using machine learning

Systems and methods are provided for managing server loads that accounts for various measures of risk associated with different workloads assigned to servers. The systems and methods may include a memory storing instructions for server load management operations, and a processor configured to execute the stored instructions. The processor may receive a workload, determine a value associated with the workload indicating a predetermined importance of the workload, receive information for a plurality of active servers in a server cluster associated with the processor, determine risk levels associated with the active servers based on the received information, and assign the received workload to one of the active servers based on the determined value and the determined risk levels.

System and method for risk-aware server load balancing

Certain disclosed embodiments provide improved systems and methods for processing jobs. The method comprises steps including receiving, from a client device over a network, information representing a job and generating at least two tasks representative of the job. The method further comprises, for each task, assigning, by a processor, a signature to the task representative of whether the task has been processed, determining at least one dataset related to the task, and assigning a signature to the determined at least one dataset. The method further comprises searching, by the processor, a data structure for the task signature, and based on the searching, sending the task over a network to a task executor for processing or locating results associated with the task. The method further comprises sending, over a network, a job result to the client device. Systems and computer-readable media are also provided.

Systems and methods for caching task execution

Growing community of inventors

Glen Allen, VA, United States of America

Santosh Bardwaj