Automatically tuning middleware in a mobilefirst platform running in a docker container infrastructure

Patent metadata

Field	Value
Publication number	US-2018006886-A1
Application number	US-201615196699-A
Country	US
Kind code	A1
Filing date	Jun 29, 2016
Priority date	Jun 29, 2016
Publication date	Jan 4, 2018
Grant date	—

Abstract

An approach is provided for tuning middleware. Performance-related settings are loaded. Performance data of the middleware of a MobileFirst Platform (MFP) running in a docker container infrastructure is received. The performance data is collected by agents installed in container groups. Based on the performance data, a performance issue in one of the container groups is identified and a server included in the one container group is identified as a source of the performance issue. Recommendations are generated for tuning the middleware by modifying one or more of the performance-related settings. While the middleware is running in the docker container infrastructure, one of the recommendations is applied to modify one of the performance-related settings which dynamically tunes the middleware, thereby resolving the performance issue.

First claim

What is claimed is:

1 . A method of tuning middleware, the method comprising the steps of:
loading, by a computer, performance-related settings; receiving, by the computer, performance data specifying a performance of the middleware of a MobileFirst platform (MFP) running in a docker container infrastructure, the performance data having been collected by agents installed in container groups included in the docker container infrastructure, and the agents having collected the performance data from multiple servers included in the container groups; based on the received performance data, identifying, by the computer, a performance issue in one of the container groups and identifying, by the computer, a server included in the one container group as being a source of the identified performance issue; generating, by the computer, a set of recommendations for tuning the middleware by modifying one or more of the performance-related settings; and while the middleware is running in the docker container infrastructure, applying, by the computer, one of the recommendations in the set of recommendations, which modifies one of the performance-related settings which is associated with the identified server, the modified performance-related setting dynamically tuning the middleware, which resolves the identified performance issue.

2 . The method of claim 1 , wherein the step of loading the performance-related settings includes loading a Java Virtual Machine (JVM) heap size of the identified server, and wherein the step of applying one of the recommendations includes increasing the JVM heap size of the identified server, which tunes the middleware.

3 . The method of claim 1 , wherein the step of loading the performance-related settings includes loading settings specifying HyperText Transfer Protocol (HTTP) connections of the identified server, and wherein the step of applying one of the recommendations includes modifying the HTTP connections of the identified server by modifying a number of execution threads or modifying a number of back-end connection threads of the identified server, the modified number of execution threads or back-end connection threads resulting in a tuning of the middleware.

4 . The method of claim 1 , wherein the step of loading the performance-related settings includes loading settings specifying an internal configuration of the identified server which is included in a MFP container group, and wherein the step of applying one of the recommendations includes modifying a rate of session time-outs or heartbeat time-outs of the identified server, the modified rate of session time-outs or the heartbeat time-outs resulting in a tuning of the middleware.

5 . The method of claim 1 , wherein the step of loading the performance-related settings includes loading settings specifying a queue size and a number of queues of the identified server which is an analytics server, and wherein the step of applying one of the recommendations includes modifying the queue size or the number of queues, the modified queue size or the number of queues resulting in a tuning of the middleware.

6 . The method of claim 1 , further comprising the steps of:
determining, by the computer, that a rate of the identified server processing requests from a mobile app to the middleware decreases below a predetermined threshold rate; in response to the step of determining that the rate of the processing the requests has decreased below the predetermined threshold rate and based on the received performance data, determining, by the computer, whether an amount of remaining space in a Java Virtual Machine (JVM) heap of the identified server is less than a predetermined threshold amount; and if the amount of the remaining space in the JVM heap is not less than the predetermined threshold amount, then increasing, by the computer, a number of execution threads and a number of back-end connection threads of the identified server, or if the amount of the remaining space in the JVM heap is less than the predetermined threshold amount, then increasing, by the computer, a JVM heap size of the identified server and restarting the mobile app.

7 . The method of claim 1 , further comprising the steps of:
determining, by the computer, that a frequency of session time-outs of the identified server exceeds a predetermined threshold amount; in response to the step of determining that the frequency of the session time-outs of the identified server exceeds the predetermined threshold amount and based on the received performance data, determining, by the computer, whether a rate of processing requests by a back-end server is causing the frequency of the session time-outs to exceed the threshold amount; and if the rate of processing requests by the back-end server is causing the frequency of the session time-outs to exceed the threshold amount, then adjusting, by the computer, a timeout value associated with the session timeouts so that the session timeouts occur less frequently.

8 . The method of claim 1 , further comprising the step of:
providing at least one support service for at least one of creating, integrating, hosting, maintaining, and deploying computer-readable program code in the computer, the program code being executed by a processor of the computer to implement the steps of loading the performance-related settings, receiving the performance data, identifying the performance issue, identifying the server, generating the set of recommendations, and applying one of the recommendations.

9 . A computer program product, comprising:
a computer-readable storage medium; and a computer-readable program code stored in the computer-readable storage medium, the computer-readable program code containing instructions that are executed by a central processing unit (CPU) of a computer system to implement a method of tuning middleware, the method comprising the steps of:
loading, by a computer system, performance-related settings;
receiving, by the computer system, performance data specifying a performance of the middleware of a MobileFirst platform (MFP) running in a docker container infrastructure, the performance data having been collected by agents installed in container groups included in the docker container infrastructure, and the agents having collected the performance data from multiple servers included in the container groups;
based on the received performance data, identifying, by the computer system, a performance issue in one of the container groups and identifying, by the computer system, a server included in the one container group as being a source of the identified performance issue;
generating, by the computer system, a set of recommendations for tuning the middleware by modifying one or more of the performance-related settings; and
while the middleware is running in the docker container infrastructure, applying, by the computer system, one of the recommendations in the set of recommendations, which modifies one of the performance-related settings which is associated with the identified server, the modified performance-related setting dynamically tuning the middleware, which resolves the identified performance issue.

10 . The computer program product of claim 9 , wherein the step of loading the performance-related settings includes loading a Java Virtual Machine (JVM) heap size of the identified server, and wherein the step of applying one of the recommendations includes increasing the JVM heap size of the identified server, which tunes the middleware.

11 . The computer program product of claim 9 , wherein the step of loading the performance-related settings includes loading settings specifying HyperText Tr

Assignees

• IBM

Inventors

• Dhanakshirur Girish
• Justin Josephine E
• Zacharias Shinoj

Classifications

• H04L43/10

  Active monitoring, e.g. heartbeat, ping or trace-route · CPC title

• H04L47/125

  by balancing the load, e.g. traffic engineering · CPC title

• H04L41/0816 Primary

  the condition being an adaptation, e.g. in response to network events · CPC title

• G06F9/44521

  Dynamic linking or loading; Link editing at or after load time, e.g. Java class loading · CPC title

• G06F9/45504

  Abstract machines for programme code execution, e.g. Java virtual machine [JVM], interpreters, emulators · CPC title