Abstract: A method, computer program product, and computing system for processing a query from a user on a website. The query is classified to determine whether the query is associated with a user with a disability. In response to classifying the query as being associated with a user with a disability, the query is processed using a customized large language model (LLM).

Abstract: A method, computer program product, and computing system for determining a storage device saturation value for a workload processed on a storage device within a storage array enclosure of a storage system. A storage controller saturation value may be determined for a workload processed on a storage controller within the storage array enclosure of the storage system. A storage array enclosure saturation value may be determined based upon, at least in part, the storage device saturation value and the storage controller saturation value. One or more IO requests may be processed on the storage device using the storage controller within the storage array enclosure based upon, at least in part, the storage array enclosure saturation value determined for the storage array enclosure.

Abstract: Systems and methods for performance tuning a computer system in scaling domains based on quantified scalability. A system includes a processor configured to: calculate an estimate of parallel fraction and speedup characteristic in a first domain D1 and in a second domain D2 for an application, the estimate being calculated using system performance measurements generated from previous processing iterations of one or more workloads of the application using a number, n, of cores in the first domain and a remaining number, N?n, of cores in the second domain to obtain performance values XD1(n) and XD2(N?n), wherein N represents a total number of cores; calculate the number of cores for the first domain using a quadratic equation generated from the parallel fraction and performance value in each domain; and execute the application in each domain using the number of cores for each domain.

Abstract: A method, computer program product, and computing system for measuring a total storage controller throughput value for a workload processed on a storage controller within a storage array enclosure of a storage system. A maximum storage controller throughput value may be determined for the workload. A storage controller saturation value may be determined for the storage controller based upon, at least in part, the total storage controller throughput value for the workload and the maximum storage controller throughput value for the workload. One or more IO requests may be processed on one or more storage devices associated with the storage controller based upon, at least in part, the storage controller saturation value determined for the storage controller.

Abstract: Techniques are disclosed for performance tuning a computer system in scaling domains based on quantified scalability. For example, a system includes a processor configured to: calculate an estimate of parallel fraction and speedup characteristic in a first domain D1 and in a second domain D2 for an application, the estimate being calculated using system performance measurements generated from previous processing iterations of one or more workloads of the application using a number, n, of cores in the first domain and a remaining number, N?n, of cores in the second domain to obtain performance values XD1(n) and XD2(N?n), wherein N represents a total number of cores; calculate the number of cores for the first domain using a quadratic equation generated from the parallel fraction and performance value in each domain; and execute the application in each domain using the number of cores for each domain.

Abstract: A method, computer program product, and computing system for determining a storage device saturation value for a workload processed on a storage device within a storage array enclosure of a storage system. A storage controller saturation value may be determined for a workload processed on a storage controller within the storage array enclosure of the storage system. A storage array enclosure saturation value may be determined based upon, at least in part, the storage device saturation value and the storage controller saturation value. One or more IO requests may be processed on the storage device using the storage controller within the storage array enclosure based upon, at least in part, the storage array enclosure saturation value determined for the storage array enclosure.

Abstract: A method, computer program product, and computing system for measuring a total storage controller throughput value for a workload processed on a storage controller within a storage array enclosure of a storage system. A maximum storage controller throughput value may be determined for the workload. A storage controller saturation value may be determined for the storage controller based upon, at least in part, the total storage controller throughput value for the workload and the maximum storage controller throughput value for the workload. One or more IO requests may be processed on one or more storage devices associated with the storage controller based upon, at least in part, the storage controller saturation value determined for the storage controller.

Abstract: A method, computer program product, and computing system for measuring a total storage device throughput value for a workload processed on a storage device within a storage array enclosure of a storage system. A maximum storage device throughput value may be determined for the workload. A storage device saturation value for the storage device may be determined based upon, at least in part, the total storage device throughput value for the workload and the maximum storage device throughput value for the workload. One or more IO requests may be processed on the storage device based upon, at least in part, the storage device saturation value determined for the storage device.

Abstract: Different storage platforms and different applications generate different types of traffic. Separate learning processes are trained using live IO operations from different types of reference workloads. The reference workloads are based on multiple types of applications executing in multiple types of environments. Example applications may include Oracle, SAP, SQL, etc. Example environments may include finance/banking, retail, on-line transaction processing, healthcare, etc. Once the learning processes have been trained, trained model checkpoints describing the trained learning processes are stored in a reference workload repository. The trained model checkpoints are used in a testing environment to enable a test server to create a test model configured to generate simulated test IO operations to be applied to a storage system. The generated test IO operations simulate the workload from an intended application and environment where the storage system will be used.

Abstract: A method of tuning performance of a data storage system includes calculating an estimate of parallel fraction and speedup characteristic for a data storage application executed by the data storage system. The estimate is calculated using linear regression of values (1/N, 1/XN) that are generated from trial runs of the data storage application processing a workload using respective different numbers N of CPU cores to obtain corresponding performance values XN. The method further includes configuring the data storage system to execute the data storage application using a number of CPU cores based on the estimate of parallel fraction and speedup characteristic.

Abstract: Different storage platforms and different applications generate different types of traffic. Separate learning processes are trained using live IO operations from different types of reference workloads. The reference workloads are based on multiple types of applications executing in multiple types of environments. Example applications may include Oracle, SAP, SQL, etc. Example environments may include finance/banking, retail, on-line transaction processing, healthcare, etc. Once the learning processes have been trained, trained model checkpoints describing the trained learning processes are stored in a reference workload repository. The trained model checkpoints are used in a testing environment to enable a test server to create a test model configured to generate simulated test IO operations to be applied to a storage system. The generated test IO operations simulate the workload from an intended application and environment where the storage system will be used.

Abstract: A method of tuning performance of a data storage system includes calculating an estimate of parallel fraction and speedup characteristic for a data storage application executed by the data storage system. The estimate is calculated using linear regression of values (1/N, 1/XN) that are generated from trial runs of the data storage application processing a workload using respective different numbers N of CPU cores to obtain corresponding performance values XN. The method further includes configuring the data storage system to execute the data storage application using a number of CPU cores based on the estimate of parallel fraction and speedup characteristic.

Abstract: Described are techniques for controlling performance of a data storage system. As a first part of an interactive dialogue, a policy is selected including one or more I/O classes each representing a logical grouping of I/O requests received at the data storage system. As a second part of the interactive dialogue, performance of said data storage system is monitored for each I/O class included in said policy without enforcing said policy. As a third part of the interactive dialogue, at least one performance goal associated with one of said I/O classes is set. The policy is run as a fourth part of the interactive dialogue causing enforcement of the policy on the data storage system in accordance with the at least one performance goal.