# US 2021/0342245 A1 · Method and Apparatus for Adjusting Host QOS Metrics Based on Storage System Performance > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2021/0342245 A1 - Document ID: US20210342245A1 - Status: Published application - Kind: A1 - Application number: 16/865,458 - Filed: 2020-05-04 - Published: 2021-11-04 - Earliest claimed: 2020-05-04 - Assignees: EMC IP Holding Company LLC - Applicants: Emc IP Holding Company LLC - Inventors: Ramesh Doddaiah - Web page: https://patentgrep.com/patent/US20210342245A1 ## CPC - G06F 11/3034: {where the computing system component is a storage system, e.g. DASD based or network based (digital input from or digital output to record carriers G06F3/06; digital recording or reproducing G11B20/18; for distributed storage of data in networks, e.g. transport arrangements for network file system [NFS], storage area networks [SAN] or network attached storage [NAS], H04L67/1097)} - G06F 11/3409: {for performance assessment} - G06F 9/4881: {Scheduling strategies for dispatcher, e.g. round robin, multi-level priority queues} - G06F 11/3447: {Performance evaluation by modeling} - G06F 11/3466: {Performance evaluation by tracing or monitoring} - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) ## Abstract A storage system has a QOS recommendation engine that monitors storage system operational parameters and generates recommended changes to host QOS metrics (throughput, bandwidth, and response time requirements) based on differences between the host QOS metrics and storage system operational parameters. The recommended host QOS metrics may be automatically implemented to adjust the host QOS metrics. By reducing host QOS metrics during times where the storage system is experiencing high volumes of workload, it is possible to throttle workload at the host computer rather than requiring the storage system to expend processing resources associated with queueing the workload prior to processing. This can enable the overall throughput of the storage system to increase. When the workload on the storage system is reduced, updated recommended host QOS metrics are provided to enable the host QOS metrics to increase. Historical analysis is also used to generate recommended host QOS metrics. ## Claims ### Claim 1 A non-transitory tangible computer readable storage medium having stored thereon a computer program for adjusting host QOS metrics based on storage system performance, the computer program including a set of instructions which, when executed by a computer, cause the computer to perform a method comprising the steps of: collecting storage system performance data for each storage group during a time window; comparing storage system performance data with host QOS metrics for each storage group during the time window; and generating recommended host QOS metrics for each storage group based on the storage system performance data and the host QOS metrics during the time window. ### Claim 2 (depends on 1) The non-transitory tangible computer readable storage medium of claim 1, wherein the host QOS metrics comprise throughput, bandwidth, and response time requirements per storage group. ### Claim 3 (depends on 1) The non-transitory tangible computer readable storage medium of claim 1, further comprising using the recommended host QOS metrics to change the host QOS metrics for a subsequent time window. ### Claim 4 (depends on 1) The non-transitory tangible computer readable storage medium of claim 1, further comprising using the storage system performance data to learn historical storage system usage patterns, and using the historical storage system usage patterns in the step of generating recommended host QOS metrics. ### Claim 5 (depends on 1) The non-transitory tangible computer readable storage medium of claim 1, wherein the step of generating recommended host QOS metrics is implemented, for each storage group, for each time window, using a composite recommendation function. ### Claim 6 (depends on 5) The non-transitory tangible computer readable storage medium of claim 5, wherein the host QOS metrics comprise throughput, bandwidth, and response time requirements per storage group; wherein the composite recommendation function has a first function that determines, for each storage group for each time window, a respective difference between the storage system throughput for that storage group and the host QOS metric for throughput for that storage group; wherein the composite recommendation function has a second function that determines, for each storage group for each time window, a respective difference between the storage system bandwidth for that storage group and the host QOS metric for bandwidth for that storage group; and wherein the composite recommendation function has a third function that determines, for each storage group for each time window, a respective difference between the storage system response time for that storage group and the host QOS metric for response time for that storage group. ### Claim 7 (depends on 6) The non-transitory tangible computer readable storage medium of claim 6, wherein the composite recommendation function separately weights each of the first, second, and third functions, to enable greater emphasis to be placed on one of the functions of the composite function when determining recommended host QOS metrics. ### Claim 8 (depends on 6) The non-transitory tangible computer readable storage medium of claim 6, wherein the composite recommendation function includes additional functions based on whether prefetch on a cache has been turned on, and CPU usage levels for storage system functions. ### Claim 9 (depends on 8) The non-transitory tangible computer readable storage medium of claim 8, wherein the storage system functions include data replication to other storage systems, scrubbers, and low priority tasks. ### Claim 10 A method of adjusting host QOS metrics based on storage system performance, comprising the steps of: collecting storage system performance data for each storage group during a time window; comparing storage system performance data with host QOS metrics for each storage group during the time window; and generating recommended host QOS metrics for each storage group based on the storage system performance data and the host QOS metrics during the time window. ### Claim 11 (depends on 10) The method of claim 10, wherein the host QOS metrics comprise throughput, bandwidth, and response time requirements per storage group. ### Claim 12 (depends on 10) The method of claim 10, further comprising using the recommended host QOS metrics to change the host QOS metrics for a subsequent time window. ### Claim 13 (depends on 10) The method of claim 10, further comprising using the storage system performance data to learn historical storage system usage patterns, and using the historical storage system usage patterns in the step of generating recommended host QOS metrics. ### Claim 14 (depends on 10) The method of claim 10, wherein the step of generating recommended host QOS metrics is implemented, for each storage group, for each time window, using a composite recommendation function. ### Claim 15 (depends on 14) The method of claim 14, wherein the host QOS metrics comprise throughput, bandwidth, and response time requirements per storage group; wherein the composite recommendation function has a first function that determines, for each storage group for each time window, a respective difference between the storage system throughput for that storage group and the host QOS metric for throughput for that storage group; wherein the composite recommendation function has a second function that determines, for each storage group for each time window, a respective difference between the storage system bandwidth for that storage group and the host QOS metric for bandwidth for that storage group; and wherein the composite recommendation function has a third function that determines, for each storage group for each time window, a respective difference between the storage system response time for that storage group and the host QOS metric for response time for that storage group. ### Claim 16 (depends on 15) The method of claim 15, wherein the composite recommendation function separately weights each of the first, second, and third functions, to enable greater emphasis to be placed on one of the functions of the composite function when determining recommended host QOS metrics. ### Claim 17 (depends on 15) The method of claim 15, wherein the composite recommendation function includes additional functions based on whether prefetch on a cache has been turned on, and CPU usage levels for storage system functions. ### Claim 18 (depends on 17) The method of claim 17, wherein the storage system functions include data replication to other storage systems, scrubbers, and low priority tasks.