“Server Performance And Application Health Management System And Method” in Patent Application Approval Process (USPTO 20230124387): Patent Application

2023 MAY 10 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- A patent application by the inventors Cohen, Adi (Woodinville, WA, US); DeBow, Ben (Fort Mill, SC, US), filed on October 18, 2021, was made available online on April 20, 2023, according to news reporting originating from Washington, D.C., by NewsRx correspondents.

This patent application has not been assigned to a company or institution.

The following quote was obtained by the news editors from the background information supplied by the inventors: “Servers are an integral part of a modern economy and society. Servers provide the backbone of the internet and major business networks, handling requests for and returns of information, and providing clients access to a number of services, such as accessing a webpage, sending an email, and downloading a file. Therefore, the health and performance of server systems are important.

“Analysis of the health and performance of a server system is necessary to manage a server system and often involves in-depth and complex monitoring of numerous complicated server-resource indicators over time. Correct and efficient analyses can lead to more efficient and productive use of server resources, optimizing client costs and providing system reliability and stability. However, the complexity of monitoring itself often prevents correct, consistent, and efficient analyses-particularly by non-experts. In particular, this complexity makes it difficult to know if a problem with or improvement to a server system exists, which resource indicators are important to a particular problem or improvement, and how a particular problem or improvement changes over time-particularly when one or many changes are made to a server system.

“The number and variety of resource indicators often contributes to the complexity of an analysis through information overload. For example, non-experts can often have issues attempting to identify which resource indicators are important to their system and when they might be important or understanding their relation of one resource indicator to another over time or in response to changes. Due to the complexity, analysis is often cursory and inconsistent or requires expert monitoring of a server system over a period of time. For example, it is common for a server performance analysis to only consider the amount of processor utilized over a given period. Moreover, as a hedge to possible issues related to the health and performance of a server system, a client might select and use resources well in excess of those necessary to ensure a healthy service because it is overly complex to determine how many resources are required to carry out processes on a server system. However, oversizing of resources often results in substantially higher costs and likely masks and is ineffectual in preventing health and performance issues.

“Currently, server performance and application health are supposedly monitored, to the extent they can be, using visual dashboards showing resource indicators for server systems. However, these visual dashboards often merely show the results of many resource indicators graphically and how they relate to independent thresholds and do not provide recommendations on or show what actions need to be implemented to solve an issue with or increase the health of a server system. Therefore, these visual dashboards leave it up to an individual, expert or not, to interpret and correlate those many resource indicators to determine the health and performance of a server system and determine any actions to take. Accordingly, there can be significant variability in the determination of the health and performance of a server system and what actions may make a server system better amongst individuals, even experts.

“Moreover, most of these visual dashboards and analyses related to server systems do not consider or concern the health of the applications actually running on server machines, instead being only concerned with server machine resource capacities. Indeed, in cases where an application is running in a serverless architecture, where the server machines are operated and provisioned by a third-party provider, these visual dashboards might be close to useless as they are unable to give any accounting separate from the server machine itself. Accordingly, visual dashboards do not provide insight or actionable assistance when unhealthy applications on a server negatively affect that server’s performance or when an application is running in a serverless architecture. Consequently, it would be advantageous to have a system and method that produces metrics to simplify measurement of the capacity and health of a server and the capacity and health of an application on a server, identifying potential problems, improvements, and solutions to increase performance, capacity and/or health and lower costs for operation of a server and application.”

In addition to the background information obtained for this patent application, NewsRx journalists also obtained the inventors’ summary information for this patent application: “The present invention is directed to a server performance, capacity and application health management system and method that, in one or more aspects, improves performance, efficiency, and health and lowers costs for the operation of a server and application by producing simplified metrics showing the health, capacity and performance of a server and application system at any time and providing the means to identify potential problems, improvements, solutions, and recommended actions with respect to one or both of a server and application. In accordance with various embodiments, an application-such as SQL Server, MySQL, Oracle, Windows, PostgreSQL, Linux, and others-is executed on a server requiring server and application-allotted resources. Information about those resources and the application behavior is collected and transmitted through a network to a processing station to be accessed by a user through a computer, smartphone, or other electronic device.

“The processing station uses a data analyzer, including a capacity algorithm logic unit and workload health algorithm logic unit, to generate capacity and workload health metrics from the information about a server. To generate the metrics, each of the capacity and workload health algorithm logic units include algorithms directed at producing a metric for a particular resource or behavior.

“Capacity metrics may be generated for both the server and the application. The server capacity metrics can include those related to the use of server storage memory, server volatile memory, and a server processor. The application capacity metrics can include those related to application resource contention, the application processor, application storage memory, and application volatile memory. Metrics generated score the presence of resource pressure for the resource the metric concerns. For example, a particular score for a server volatile memory metric can indicate the use of a large amount of server volatile memory, reflecting that memory as unavailable to other applications and potentially hampering performance of the server.

“Workload health metrics generated can include those related to code stability, resource predictability, and process predictability. The scores of these metrics indicate behavior related to the health of the application on the server. For example, an application with a particular process predictability score might have numerous instances of abnormal behavior or lack consistent patterned behavior all together over a period. As another example, an application with a particular code stability metric may indicate a particular segment of code is executing in an inefficient manner, leading to longer run times, such as in instances of code regression. Code regression can happen when code is executed based on reused cached data which is sub-optimal. For example, a navigation application might provide directions during rush hour based on a previously generated route that is sub-optimal at rush hour.

“The metrics generated are displayed to a user providing simplified indicators of the performance and capacity of the server and the health of the application at a particular point, over time, including during changes to the server, application, or both, without complicated analysis of the server system, which may be difficult or impossible for non-experts. Additionally, the metrics can also consider changes over time to help identify, not only the performance, efficiency, and health of a server or application at a given instance, but over a period of time. Thereby, a user might not only identify specific instances of resource pressure but also how often those instances occur. Moreover, the capacity metrics generated for the server and application can also be used by the data analyzer to generate sizing recommendations, further simplifying an analysis. Additional recommendations related to specific actions or automatic actions can also be generated or initiated based on the metrics to improve server performance, server capacity, application capacity, or application health.

“From the generated metrics and recommendations, a user may identify potential problems, improvements, and solutions regarding the system and application. The metrics and recommendations also provide for consistency among the conclusions regarding the performance and health of the server and application. Additionally, the metrics-particularly the capacity metrics-identify resource pressure, assisting with right sizing of a server and application. Moreover, the metrics assist users with seeing the effects of changes in a server or application, which may be useful when attempting to improve server and application operation. Graphical representations of the metrics over time may also be generated and presented to a user demonstrating the performance, efficiency, and health of a server and application over time, including the frequency of any resource pressure. Moreover, machine learning may be utilized with the generated metrics to find anomalies, patterns, and help predict future activities and requirements.”

The claims supplied by the inventors are:

“1. A management system, comprising: a first server comprising a first storage memory, a first working memory, a first processor, and a first communications module; at least one resident application stored in said first storage memory and executed through said first processor and said first working memory on said first server; a processing station comprising a second storage memory, a second working memory, a second processor, and a second communications module, wherein said first and second communications modules are networked together; operation resource information generated by said first server and said at least one resident application during execution and transmitted through said first communications module to said second communications module; a data analyzer stored in said second storage memory comprising a capacity algorithm logic unit; server capacity metrics generated by said capacity algorithm logic unit from said operation resource information comprising metrics for at least one of said first storage memory, said first working memory, and said first processor; application capacity metrics generated by said capacity algorithm logic unit from said operation resource information comprising metrics regarding at least one of said resident application resource contention, application processor, storage, and memory; and whereby said server and application capacity metrics provide data on said first server and said resident application to allow for an analysis regarding potential problems, improvements, and solutions related to said first server and said resident application and a simplified presentation of said analysis results.

“2. The management system of claim 1, further comprising: a second server comprising a third storage memory including an additional resident application, a third working memory, a third processor, and a third communications module; operation resource information transmitted by said third communications module to said second communications module; and additional server capacity metrics and additional application capacity metrics generated by said capacity algorithm logic unit.

“3. The management system of claim 1, further comprising: sizing recommendations generated by said data analyzer to determine if sizing changes in memory would benefit said first server.

“4. The management system of claim 1, wherein said data analyzer further comprises a workload health algorithm logic unit and workload health metrics generated by said workload health algorithm logic unit include one or more of code stability, resource predictability, process predictability, and server uptime.

“5. The management system of claim 1, further comprising: recommendations generated by said data analyzer based on one or more of patterns and anomalies identified through machine learning analysis of one or more metrics generated by said capacity algorithm logic unit.

“6. The management system of claim 4, further comprising: recommendations generated by said data analyzer based on one or more patterns and anomalies identified through machine learning analysis of one or more workload health metrics.

“7. The management system of claim 1, further comprising: recommendations generated by said data analyzer based on one or more metrics generated by said data analyzer.

“8. A management system, comprising: a first server comprising a first storage memory, a first working memory, a first processor, and a first communications module; a processing station comprising a second storage memory, a second working memory, a second processor, and a second communications module, wherein said first and second communications modules are networked together; operation resource information generated by said first server and transmitted through said first communications module to said second communications module; a data analyzer stored in said second storage memory comprising a capacity algorithm logic unit; server capacity metrics generated by said capacity algorithm logic unit from said operation resource information comprising metrics for at least one of said first storage memory, said first working memory, and said first processor; and whereby said server capacity metrics provide data on said first server to allow for an analysis regarding potential problems, improvements, and solutions related to said first server and a simplified presentation of said analysis results.

“9. The management system of claim 8, further comprising: sizing recommendations generated by said data analyzer to determine if sizing changes in memory would benefit said first server.

“10. The management system of claim 8, wherein said data analyzer further comprises a workload health algorithm logic unit and workload health metrics generated by said workload health algorithm logic unit include one or more of resource predictability and server uptime.

“11. The management system of claim 8, further comprising: recommendations generated by said data analyzer based on one or more of patterns and anomalies identified through machine learning analysis of one or more metrics generated by said capacity algorithm logic unit.

“12. The management system of claim 10, further comprising: recommendations generated by said data analyzer based on one or more of patterns and anomalies identified through machine learning analysis of one or more workload health metrics.

“13. The management system of claim 8, further comprising: recommendations generated by said data analyzer based on one or more metrics generated by said data analyzer.

“14. A management system, comprising: an application stored in a remote network accessible location; a processing station comprising a communications module, wherein said communications module can transmit and receive data from said application through said network; operation resource information generated by said application during execution and transmitted through said network to said communications module; a data analyzer stored in said second storage memory comprising a capacity algorithm logic unit; application capacity metrics generated by said capacity algorithm logic unit from said operation resource information comprising metrics regarding at least one of application resource contention, application processor, storage, and memory; and whereby said server and application capacity metrics provide data on said first server and said resident application to allow for an analysis regarding potential problems, improvements, and solutions related to said first server and said resident application and a simplified presentation of said analysis results.

“15. The management system of claim 14, further comprising: sizing recommendations generated by said data analyzer to determine if sizing changes in memory would benefit said application.

“16. The management system of claim 14, wherein said data analyzer further comprises a workload health algorithm logic unit and workload health metrics generated by said workload health algorithm logic unit include one or more of code stability and process predictability.

“17. The management system of claim 14, further comprising: recommendations generated by said data analyzer based on one or more of patterns and anomalies identified through machine learning analysis of one or more metrics generated by said capacity algorithm logic unit.

“18. The management system of claim 16, further comprising: recommendations generated by said data analyzer based on one or more of patterns and anomalies identified through machine learning analysis of one or more workload health metrics.

“19. The management system of claim 14, further comprising: recommendations generated by said data analyzer based on one or more metrics generated by said data analyzer.”

URL and more information on this patent application, see: Cohen, Adi; DeBow, Ben. Server Performance And Application Health Management System And Method. U.S. Patent Application Number 20230124387, filed October 18, 2021 and posted April 20, 2023. Patent URL (for desktop use only): https://ppubs.uspto.gov/pubwebapp/external.html?q=(20230124387)&db=US-PGPUB&type=ids

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