{"id":15134,"date":"2021-01-18T22:02:14","date_gmt":"2021-01-18T16:32:14","guid":{"rendered":"https:\/\/cigniti.com\/blog\/?p=15134"},"modified":"2022-07-28T10:40:18","modified_gmt":"2022-07-28T05:10:18","slug":"use-of-predictive-analytics-apm","status":"publish","type":"post","link":"https:\/\/www.cigniti.com\/blog\/use-of-predictive-analytics-apm\/","title":{"rendered":"Use of Predictive Analytics in Application Performance Management"},"content":{"rendered":"<p><span data-contrast=\"auto\">According to Forrester \u201cYou\u00a0<\/span><span data-contrast=\"auto\">c<\/span><span data-contrast=\"auto\">an\u2019t\u00a0<\/span><span data-contrast=\"auto\">s<\/span><span data-contrast=\"auto\">ee into the future (yet), but with predictive analytics you can make an educated guess.\u201d<\/span><span data-ccp-props=\"{&quot;134233117&quot;:true,&quot;134233118&quot;:true,&quot;201341983&quot;:0,&quot;335551550&quot;:6,&quot;335551620&quot;:6,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><b><i><span data-contrast=\"auto\">Predictive models utilize the past data to determine the likelihood of certain future outcomes. With predictive analytics businesses can take informed decisions to plan in an optimized manner.<\/span><\/i><\/b><span data-ccp-props=\"{&quot;134233117&quot;:true,&quot;134233118&quot;:true,&quot;201341983&quot;:0,&quot;335551550&quot;:6,&quot;335551620&quot;:6,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">Predictive analytics can benefit\u00a0<\/span><span data-contrast=\"auto\">your\u00a0<\/span><span data-contrast=\"auto\">IT teams\u202fin many ways. It gives the ability to monitor the health or status of an application so that you can predict and respond\u202fto application outages. The ability to prevent failures before they happen is a big win. It saves time and\u202fmoney\u202fand creates a more resilient IT infrastructure.\u00a0<\/span><span data-contrast=\"auto\">Predictive analytics\u202f<\/span><span data-contrast=\"auto\">tools\u00a0<\/span><span data-contrast=\"auto\">can\u00a0<\/span><span data-contrast=\"auto\">also\u00a0<\/span><span data-contrast=\"auto\">be<\/span><span data-contrast=\"auto\">\u00a0used to\u00a0<\/span><span data-contrast=\"auto\">predict the user experience at a\u00a0<\/span><span data-contrast=\"auto\">projected user load and to evaluate if the current infrastructure can support the projected\u00a0<\/span><span data-contrast=\"auto\">user growth.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335551550&quot;:6,&quot;335551620&quot;:6,&quot;335559739&quot;:390,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">Application Performance Monitoring (APM)\u00a0<\/span><span data-contrast=\"auto\">help<\/span><span data-contrast=\"auto\">s<\/span><span data-contrast=\"auto\">\u00a0<\/span><span data-contrast=\"auto\">monitor the user experience and leverage<\/span><span data-contrast=\"auto\">s<\/span><span data-contrast=\"auto\">\u00a0predictive analytics to improve the\u00a0<\/span><a href=\"https:\/\/www.cigniti.com\/services\/performance-testing\/\"><span data-contrast=\"none\">application performance<\/span><\/a><span data-contrast=\"auto\">\u00a0whenever there is a sudden spike in number of user\u2019s accessing the application\/whenever there is a sudden degradation in application performance.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335551550&quot;:6,&quot;335551620&quot;:6,&quot;335559739&quot;:390,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p aria-level=\"1\"><b><span data-contrast=\"none\">Predictive Analytics<\/span><\/b><b><span data-contrast=\"none\">\u00a0Usage<\/span><\/b><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335551550&quot;:6,&quot;335551620&quot;:6,&quot;335559738&quot;:240,&quot;335559739&quot;:0,&quot;335559740&quot;:259}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">Organizations\u00a0<\/span><span data-contrast=\"auto\">can<\/span><span data-contrast=\"auto\">\u00a0leverag<\/span><span data-contrast=\"auto\">e<\/span><span data-contrast=\"auto\">\u00a0predictive analytics\u00a0<\/span><span data-contrast=\"auto\">to improve their application performance<\/span><span data-contrast=\"auto\">\u00a0<\/span><span data-contrast=\"auto\">in the\u00a0<\/span><span data-contrast=\"auto\">following<\/span><span data-contrast=\"auto\">\u00a0areas:<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335551550&quot;:6,&quot;335551620&quot;:6,&quot;335559739&quot;:160,&quot;335559740&quot;:259}\">\u00a0<\/span><\/p>\n<ul>\n<li data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"12\" aria-setsize=\"-1\" data-aria-posinset=\"1\" data-aria-level=\"1\"><b><span data-contrast=\"none\">Identify root causes for application performance<\/span><\/b><b><span data-contrast=\"none\">\u00a0issues<\/span><\/b><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335551550&quot;:6,&quot;335551620&quot;:6,&quot;335559685&quot;:360,&quot;335559738&quot;:40,&quot;335559739&quot;:0,&quot;335559740&quot;:259}\">\u00a0<\/span><\/li>\n<li style=\"list-style-type: none;\" data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"12\" aria-setsize=\"-1\" data-aria-posinset=\"1\" data-aria-level=\"1\">By identifying root causes for application performance using\u202f<span data-contrast=\"auto\">machine learning<\/span><span data-contrast=\"auto\">\u00a0techniques<\/span><span data-contrast=\"auto\">,\u00a0<\/span><span data-contrast=\"auto\">organizations\u00a0<\/span><span data-contrast=\"auto\">can focus on the right set of areas in which\u202fto take action. Predictive analytics\u00a0<\/span><span data-contrast=\"auto\">can then<\/span><span data-contrast=\"auto\">\u202fstudy the characteristics of the various attributes within each cluster<\/span><span data-contrast=\"auto\">\u00a0that\u00a0<\/span><span data-contrast=\"auto\">can provide deep insights into what\u202f<\/span><span data-contrast=\"auto\">changes need to<\/span><span data-contrast=\"auto\">\u00a0<\/span><span data-contrast=\"auto\">be\u00a0<\/span><span data-contrast=\"auto\">ma<\/span><span data-contrast=\"auto\">d<\/span><span data-contrast=\"auto\">e to achieve ideal performance and avoid specific bottlenecks.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:true,&quot;134233118&quot;:true,&quot;201341983&quot;:0,&quot;335551550&quot;:6,&quot;335551620&quot;:6,&quot;335559685&quot;:360,&quot;335559739&quot;:390,&quot;335559740&quot;:240}\">\u00a0<\/span><\/li>\n<\/ul>\n<ul>\n<li data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"12\" aria-setsize=\"-1\" data-aria-posinset=\"2\" data-aria-level=\"1\"><b><span data-contrast=\"none\">Monitor\u202fapplication health in real<\/span><\/b><b><span data-contrast=\"none\">&#8211;<\/span><\/b><b><span data-contrast=\"none\">time<\/span><\/b><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335551550&quot;:6,&quot;335551620&quot;:6,&quot;335559685&quot;:360,&quot;335559738&quot;:40,&quot;335559739&quot;:0,&quot;335559740&quot;:259}\">\u00a0<\/span><\/li>\n<li style=\"list-style-type: none;\" data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"12\" aria-setsize=\"-1\" data-aria-posinset=\"2\" data-aria-level=\"1\">Performing real-time monitoring of application health via multi-variate machine-learning (ML) techniques allows\u00a0<span data-contrast=\"auto\">organizations<\/span><span data-contrast=\"auto\">\u00a0to catch and respond to\u00a0<\/span><span data-contrast=\"auto\">the<\/span><span data-contrast=\"auto\">\u202fdegradation of application health in a timely manner. Most applications rely on multiple services to\u202fcapture the true health of the application. The data might consist of configuration data, application logs, network logs, error logs, performance logs and more.\u00a0<\/span><span data-contrast=\"auto\">Predictive analytics models can<\/span><span data-contrast=\"auto\">\u00a0analyze the past data during a time in which the application was in a good state<\/span><span data-contrast=\"auto\">\u00a0and subsequently\u00a0<\/span><span data-contrast=\"auto\">identify whether the incoming data exhibits normal behavior or not.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:true,&quot;134233118&quot;:true,&quot;201341983&quot;:0,&quot;335551550&quot;:6,&quot;335551620&quot;:6,&quot;335559685&quot;:360,&quot;335559739&quot;:300,&quot;335559740&quot;:240}\">\u00a0<\/span><\/li>\n<\/ul>\n<ul>\n<li data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"12\" aria-setsize=\"-1\" data-aria-posinset=\"1\" data-aria-level=\"1\"><b><span data-contrast=\"none\">Predict\u00a0<\/span><\/b><b><span data-contrast=\"none\">user load<\/span><\/b><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335551550&quot;:6,&quot;335551620&quot;:6,&quot;335559685&quot;:360,&quot;335559738&quot;:40,&quot;335559739&quot;:0,&quot;335559740&quot;:259}\">\u00a0<\/span><\/li>\n<li style=\"list-style-type: none;\" data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"12\" aria-setsize=\"-1\" data-aria-posinset=\"1\" data-aria-level=\"1\">Predictive analytics can help in predicting the user load by analyzing the past data. Organizations can use this data to better prepare in order to handle the predicted user load and provide experience\u00a0<span data-contrast=\"auto\">assurance\u00a0<\/span><span data-contrast=\"auto\">which would help in reducing customer churn. This data can also help the organizations better plan their future IT infrastructure requirements and capacity utilization.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:true,&quot;134233118&quot;:true,&quot;201341983&quot;:0,&quot;335551550&quot;:6,&quot;335551620&quot;:6,&quot;335559685&quot;:360,&quot;335559739&quot;:300,&quot;335559740&quot;:240}\">\u00a0<\/span><\/li>\n<\/ul>\n<ul>\n<li data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"12\" aria-setsize=\"-1\" data-aria-posinset=\"2\" data-aria-level=\"1\"><b><span data-contrast=\"none\">Predict application outages before they happen<\/span><\/b><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335551550&quot;:6,&quot;335551620&quot;:6,&quot;335559685&quot;:360,&quot;335559738&quot;:40,&quot;335559739&quot;:0,&quot;335559740&quot;:259}\">\u00a0<\/span><\/li>\n<li style=\"list-style-type: none;\" data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"12\" aria-setsize=\"-1\" data-aria-posinset=\"2\" data-aria-level=\"1\">Predicting application downtime or outages before they happen helps perform\u00a0<span data-contrast=\"auto\">the\u00a0<\/span><span data-contrast=\"auto\">needed maintenance on th<\/span><span data-contrast=\"auto\">e<\/span><span data-contrast=\"auto\">\u00a0application without any downtime. This can save an organization time<\/span><span data-contrast=\"auto\">,<\/span><span data-contrast=\"auto\">\u00a0money<\/span><span data-contrast=\"auto\">, and much more<\/span><span data-contrast=\"auto\">. Before an application outage, the IT infrastructure leaves lots of indirect clues hours, or even days, before it dies. The\u00a0<\/span><span data-contrast=\"auto\">predictive analytics\u00a0<\/span><span data-contrast=\"auto\">model\u00a0<\/span><span data-contrast=\"auto\">can\u00a0<\/span><span data-contrast=\"auto\">learn those patterns and continue to monitor for similar occurrences, predicting future failures before they happen. With this type of predictive model in place, preventive action\u00a0<\/span><span data-contrast=\"auto\">can be taken\u00a0<\/span><span data-contrast=\"auto\">at the right time.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:true,&quot;134233118&quot;:true,&quot;201341983&quot;:0,&quot;335551550&quot;:6,&quot;335551620&quot;:6,&quot;335559685&quot;:360,&quot;335559739&quot;:300,&quot;335559740&quot;:240}\">\u00a0<\/span><\/li>\n<\/ul>\n<p aria-level=\"1\"><b><span data-contrast=\"none\">Applying predictive analytics to forecast application performance<\/span><\/b><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335551550&quot;:6,&quot;335551620&quot;:6,&quot;335559738&quot;:240,&quot;335559739&quot;:0,&quot;335559740&quot;:259}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">P<\/span><span data-contrast=\"auto\">redictive analytics in the area of application performance improvement focuses on three main areas \u2013\u00a0<\/span><span data-contrast=\"auto\">Forecasted<\/span><span data-contrast=\"auto\">\u00a0User Load,\u00a0<\/span><span data-contrast=\"auto\">Response<\/span><span data-contrast=\"auto\">\u00a0<\/span><span data-contrast=\"auto\">Time prediction<\/span><span data-contrast=\"auto\">,<\/span><span data-contrast=\"auto\">\u00a0<\/span><span data-contrast=\"auto\">and\u00a0<\/span><span data-contrast=\"auto\">I<\/span><span data-contrast=\"auto\">nfrastructure\u00a0<\/span><span data-contrast=\"auto\">A<\/span><span data-contrast=\"auto\">ssessment.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:true,&quot;134233118&quot;:true,&quot;201341983&quot;:0,&quot;335551550&quot;:6,&quot;335551620&quot;:6,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p aria-level=\"2\"><b><span data-contrast=\"none\">User Load<\/span><\/b><b><span data-contrast=\"none\">\u00a0Prediction<\/span><\/b><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335551550&quot;:6,&quot;335551620&quot;:6,&quot;335559738&quot;:40,&quot;335559739&quot;:0,&quot;335559740&quot;:259}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">Traditionally organizations have relied on peak user traffic in the past in order to come up with the number of users that might access the application in the future. This model has its own limitations as it does not consider factors such as emergence o<\/span><span data-contrast=\"auto\">f<\/span><span data-contrast=\"auto\">\u00a0new technologies, change in user behavior and other disrupti<\/span><span data-contrast=\"auto\">ve<\/span><span data-contrast=\"auto\">\u00a0factors. By using\u00a0<\/span><span data-contrast=\"auto\">AI\/ML\u00a0<\/span><span data-contrast=\"auto\">predictive analytics<\/span><span data-contrast=\"auto\">,<\/span><span data-contrast=\"auto\">\u00a0businesses can avoid these pitfalls as\u00a0<\/span><span data-contrast=\"auto\">forecasting\u00a0<\/span><span data-contrast=\"auto\">models can be built\u00a0<\/span><span data-contrast=\"auto\">by analyzing the user behavior in real time<\/span><span data-contrast=\"auto\">.\u00a0<\/span><span data-ccp-props=\"{&quot;134233117&quot;:true,&quot;134233118&quot;:true,&quot;201341983&quot;:0,&quot;335551550&quot;:6,&quot;335551620&quot;:6,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">Using the data provided by the APM we\u00a0<\/span><span data-contrast=\"auto\">have built<\/span><span data-contrast=\"auto\">\u00a0a model to predict the number of users who will access the application\u00a0<\/span><span data-contrast=\"auto\">six<\/span><span data-contrast=\"auto\">\u00a0months<\/span><span data-contrast=\"auto\">\u00a0into the future.\u00a0<\/span><span data-contrast=\"auto\">We used\u00a0<\/span><span data-contrast=\"auto\">the hourly monitoring data from production monitoring for the past 2 years<\/span><span data-contrast=\"auto\">\u00a0as input to the model.\u00a0<\/span><span data-contrast=\"auto\">We found that the accuracy provided by the model was low\u00a0<\/span><span data-contrast=\"auto\">when we used classification and regression models\u00a0<\/span><span data-contrast=\"auto\">due to missing data and data imbalance.\u00a0<\/span><span data-ccp-props=\"{&quot;134233117&quot;:true,&quot;134233118&quot;:true,&quot;201341983&quot;:0,&quot;335551550&quot;:6,&quot;335551620&quot;:6,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">T<\/span><span data-contrast=\"auto\">he data\u00a0<\/span><span data-contrast=\"auto\">was normalized\u00a0<\/span><span data-contrast=\"auto\">to handle missing data and data wrangling\u00a0<\/span><span data-contrast=\"auto\">performed\u00a0<\/span><span data-contrast=\"auto\">to mitigate the data imbalance and fed to a neural network<\/span><span data-contrast=\"auto\">. This increased\u00a0<\/span><span data-contrast=\"auto\">the accuracy of the model to approx.\u00a0<\/span><span data-contrast=\"auto\">7<\/span><span data-contrast=\"auto\">5<\/span><span data-contrast=\"auto\">&#8211;<\/span><span data-contrast=\"auto\">8<\/span><span data-contrast=\"auto\">0%.\u00a0<\/span><span data-contrast=\"auto\">T<\/span><span data-contrast=\"auto\">he model\u00a0<\/span><span data-contrast=\"auto\">was then converted\u00a0<\/span><span data-contrast=\"auto\">to\u00a0<\/span><span data-contrast=\"auto\">a\u00a0<\/span><span data-contrast=\"auto\">self-learning<\/span><span data-contrast=\"auto\">\u00a0algorithm\u00a0<\/span><span data-contrast=\"auto\">by retraining the model on\u00a0<\/span><span data-contrast=\"auto\">live data<\/span><span data-contrast=\"auto\">\u00a0which\u00a0<\/span><span data-contrast=\"auto\">further\u00a0<\/span><span data-contrast=\"auto\">increased the\u00a0<\/span><span data-contrast=\"auto\">model accuracy by\u00a0<\/span><span data-contrast=\"auto\">8<\/span><span data-contrast=\"auto\">-1<\/span><span data-contrast=\"auto\">0<\/span><span data-contrast=\"auto\">%.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:true,&quot;134233118&quot;:true,&quot;201341983&quot;:0,&quot;335551550&quot;:6,&quot;335551620&quot;:6,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">Such\u00a0<\/span><span data-contrast=\"auto\">accurate\u00a0<\/span><span data-contrast=\"auto\">forecast help<\/span><span data-contrast=\"auto\">s\u00a0<\/span><span data-contrast=\"auto\">decrease<\/span><span data-contrast=\"auto\">\u00a0<\/span><span data-contrast=\"auto\">the\u00a0<\/span><span data-contrast=\"auto\">customer churn rate.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:true,&quot;134233118&quot;:true,&quot;201341983&quot;:0,&quot;335551550&quot;:6,&quot;335551620&quot;:6,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p aria-level=\"2\"><b><span data-contrast=\"none\">Response Time Prediction<\/span><\/b><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335551550&quot;:6,&quot;335551620&quot;:6,&quot;335559738&quot;:40,&quot;335559739&quot;:0,&quot;335559740&quot;:259}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">APM tools\u00a0<\/span><span data-contrast=\"auto\">capture<\/span><span data-contrast=\"auto\">\u00a0a lot of\u00a0<\/span><span data-contrast=\"auto\">application\u00a0<\/span><span data-contrast=\"auto\">data\u00a0<\/span><span data-contrast=\"auto\">and hardware utilization details.\u00a0<\/span><span data-contrast=\"auto\">This data can be<\/span><span data-contrast=\"auto\">\u00a0leverage<\/span><span data-contrast=\"auto\">d<\/span><span data-contrast=\"auto\">\u00a0to predict the application\u00a0<\/span><span data-contrast=\"auto\">response time<\/span><span data-contrast=\"auto\">.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:true,&quot;134233118&quot;:true,&quot;201341983&quot;:0,&quot;335551550&quot;:6,&quot;335551620&quot;:6,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">In order to predict the application response time for a given user load<\/span><span data-contrast=\"auto\">,<\/span><span data-contrast=\"auto\">\u00a0<\/span><span data-contrast=\"auto\">using\u00a0<\/span><span data-contrast=\"auto\">Exploratory Data Analysis using the user load as the input\u00a0<\/span><span data-contrast=\"auto\">provides low\u00a0<\/span><span data-contrast=\"auto\">accuracy and correlation.<\/span><b><span data-contrast=\"auto\">\u00a0<\/span><\/b><span data-contrast=\"auto\">Even<\/span><b><span data-contrast=\"auto\">\u00a0<\/span><\/b><span data-contrast=\"auto\">add<\/span><span data-contrast=\"auto\">ing<\/span><span data-contrast=\"auto\">\u00a0the application server hardware utilization details to the inputs and using\u00a0<\/span><span data-contrast=\"auto\">multi linear regression models<\/span><span data-contrast=\"auto\">,<\/span><span data-contrast=\"auto\">\u00a0d<\/span><span data-contrast=\"auto\">oes\u00a0<\/span><span data-contrast=\"auto\">not\u00a0<\/span><span data-contrast=\"auto\">help\u00a0<\/span><span data-contrast=\"auto\">achieve the required accuracy.\u00a0<\/span><span data-ccp-props=\"{&quot;134233117&quot;:true,&quot;134233118&quot;:true,&quot;201341983&quot;:0,&quot;335551550&quot;:6,&quot;335551620&quot;:6,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">However, when w<\/span><span data-contrast=\"auto\">e<\/span><span data-contrast=\"auto\">\u00a0<\/span><span data-contrast=\"auto\">add the<\/span><span data-contrast=\"auto\">\u00a0database server hardware utilization details, network\u00a0<\/span><span data-contrast=\"auto\">telemetry and middle ware utilization details to the inputs and us<\/span><span data-contrast=\"auto\">e<\/span><span data-contrast=\"auto\">\u00a0<\/span><span data-contrast=\"auto\">the\u00a0<\/span><span data-contrast=\"auto\">ARIMA models<\/span><span data-contrast=\"auto\">,\u00a0<\/span><span data-contrast=\"auto\">we f<\/span><span data-contrast=\"auto\">ind\u00a0<\/span><span data-contrast=\"auto\">that the model accuracy\u00a0<\/span><span data-contrast=\"auto\">may\u00a0<\/span><span data-contrast=\"auto\">increase to more than 75%<\/span><span data-contrast=\"auto\">.\u00a0<\/span><span data-ccp-props=\"{&quot;134233117&quot;:true,&quot;134233118&quot;:true,&quot;201341983&quot;:0,&quot;335551550&quot;:6,&quot;335551620&quot;:6,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><b><i><span data-contrast=\"auto\">This model helped our customer<\/span><\/i><\/b><b><i><span data-contrast=\"auto\">s<\/span><\/i><\/b><b><i><span data-contrast=\"auto\">\u00a0to tweak the application architecture and server hardware to improve the application performance for the anticipated user load. This\u00a0<\/span><\/i><\/b><b><i><span data-contrast=\"auto\">also\u00a0<\/span><\/i><\/b><b><i><span data-contrast=\"auto\">helped in improving the user experience and better customer retention during the peak season.<\/span><\/i><\/b><span data-ccp-props=\"{&quot;134233117&quot;:true,&quot;134233118&quot;:true,&quot;201341983&quot;:0,&quot;335551550&quot;:6,&quot;335551620&quot;:6,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p aria-level=\"2\"><b><span data-contrast=\"none\">Infrastructure\u00a0<\/span><\/b><b><span data-contrast=\"none\">Capacity\u00a0<\/span><\/b><b><span data-contrast=\"none\">Prediction<\/span><\/b><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335551550&quot;:6,&quot;335551620&quot;:6,&quot;335559738&quot;:40,&quot;335559739&quot;:0,&quot;335559740&quot;:259}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">A<\/span><span data-contrast=\"auto\">part from\u00a0<\/span><span data-contrast=\"auto\">building the models to predict the user load and response times, we\u00a0<\/span><span data-contrast=\"auto\">have also\u00a0<\/span><span data-contrast=\"auto\">come up with a model that would estimate whether a given hardware will be able to handle a given user load<\/span><span data-contrast=\"auto\">.<\/span><span data-contrast=\"auto\">\u00a0By tweaking the input parameters to the model, we\u00a0<\/span><span data-contrast=\"auto\">can also\u00a0<\/span><span data-contrast=\"auto\">predict the infrastructure required to handle a\u00a0<\/span><span data-contrast=\"auto\">particular user<\/span><span data-contrast=\"auto\">\u00a0load. In order to achieve this, a model\u00a0<\/span><span data-contrast=\"auto\">was built\u00a0<\/span><span data-contrast=\"auto\">by leveraging the APM data from production for a period of two years.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:true,&quot;134233118&quot;:true,&quot;201341983&quot;:0,&quot;335551550&quot;:6,&quot;335551620&quot;:6,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">W<\/span><span data-contrast=\"auto\">ith a 75%\u00a0<\/span><span data-contrast=\"auto\">accura<\/span><span data-contrast=\"auto\">te\u00a0<\/span><span data-contrast=\"auto\">model<\/span><span data-contrast=\"auto\">,<\/span><span data-contrast=\"auto\">\u00a0we\u00a0<\/span><span data-contrast=\"auto\">then modified\u00a0<\/span><span data-contrast=\"auto\">the hardware configuration to predict the application health.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:true,&quot;134233118&quot;:true,&quot;201341983&quot;:0,&quot;335551550&quot;:6,&quot;335551620&quot;:6,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><b><i><span data-contrast=\"auto\">This model help<\/span><\/i><\/b><b><i><span data-contrast=\"auto\">ed<\/span><\/i><\/b><b><i><span data-contrast=\"auto\">\u00a0our customers to plan for cloud migration by evaluating the infrastructure requirement and the costs associated with infrastructure upgrade\u00a0<\/span><\/i><\/b><b><i><span data-contrast=\"auto\">requirement\u00a0<\/span><\/i><\/b><b><i><span data-contrast=\"auto\">versus cloud migration<\/span><\/i><\/b><b><i><span data-contrast=\"auto\">.<\/span><\/i><\/b><span data-ccp-props=\"{&quot;134233117&quot;:true,&quot;134233118&quot;:true,&quot;201341983&quot;:0,&quot;335551550&quot;:6,&quot;335551620&quot;:6,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p aria-level=\"1\"><b><span data-contrast=\"none\">Data i<\/span><\/b><b><span data-contrast=\"none\">s<\/span><\/b><b><span data-contrast=\"none\">\u00a0the new oil \u2013 are you\u00a0<\/span><\/b><b><span data-contrast=\"none\">analyzing<\/span><\/b><b><span data-contrast=\"none\">\u00a0it?<\/span><\/b><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335551550&quot;:6,&quot;335551620&quot;:6,&quot;335559738&quot;:240,&quot;335559739&quot;:0,&quot;335559740&quot;:259}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">According to Forrester \u201cThey say data is the new oil. They say data is the new currency. They say data is the key competitive differentiator. All true. But reality is sobering: Only 7% of firms report advanced, insights-driven practices.\u201d<\/span><span data-ccp-props=\"{&quot;134233117&quot;:true,&quot;134233118&quot;:true,&quot;201341983&quot;:0,&quot;335551550&quot;:6,&quot;335551620&quot;:6,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">Businesses need to spend some time to realize the benefits. The predictive analytics implementation needs time as data has to be prepared, cleansed, correlated,\u00a0<\/span><span data-contrast=\"auto\">the\u00a0<\/span><span data-contrast=\"auto\">right algorithm has to be chosen to process the data, the algorithmic output<\/span><span data-contrast=\"auto\">\u00a0to be understood<\/span><span data-contrast=\"auto\">,\u00a0<\/span><span data-contrast=\"auto\">and\u00a0<\/span><span data-contrast=\"auto\">the algorithm\u00a0<\/span><span data-contrast=\"auto\">has to be retrained\u00a0<\/span><span data-contrast=\"auto\">with new sets of data and optimize<\/span><span data-contrast=\"auto\">d<\/span><span data-contrast=\"auto\">\u00a0till the required success factors are met. We can accelerate the deployment time by leveraging our experience with similar type of application or domain.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:true,&quot;134233118&quot;:true,&quot;201341983&quot;:0,&quot;335551550&quot;:6,&quot;335551620&quot;:6,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">Since predictive analytics depend on the data to predict the future outcomes, any problems with the data quantity or quality will adversely impact the results. We can mitigate this problem by integrating our models with the APM tool in production.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:true,&quot;134233118&quot;:true,&quot;201341983&quot;:0,&quot;335551550&quot;:6,&quot;335551620&quot;:6,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">In order to simplify the\u00a0<\/span><a href=\"https:\/\/www.cigniti.com\/services\/bigdata-testing\/\"><span data-contrast=\"none\">implementation of predictive analytics<\/span><\/a><span data-contrast=\"auto\">, w<\/span><span data-contrast=\"auto\">e\u00a0<\/span><span data-contrast=\"auto\">at Cigniti\u00a0<\/span><span data-contrast=\"auto\">are d<\/span><span data-contrast=\"auto\">evelop<\/span><span data-contrast=\"auto\">ing<\/span><span data-contrast=\"auto\">\u00a0a process that\u00a0<\/span><span data-contrast=\"auto\">will\u00a0<\/span><span data-contrast=\"auto\">monitor\u00a0<\/span><span data-contrast=\"auto\">key\u00a0<\/span><span data-contrast=\"auto\">performance indicators and develop its own unique system for setting\u00a0<\/span><span data-contrast=\"auto\">dynamic\u00a0<\/span><span data-contrast=\"auto\">thresholds. Essentially, the machine-based learning process\u00a0<\/span><span data-contrast=\"auto\">will<\/span><span data-contrast=\"auto\">\u00a0figure out high and low thresholds for when it\u2019s appropriate to alert the operations team. The new model would shift these thresholds over time as it learns from the aggregation of historical data.<\/span><span data-contrast=\"auto\">\u00a0It\u00a0<\/span><span data-contrast=\"auto\">will<\/span><span data-contrast=\"auto\">\u00a0allow for thresholds that are based on seasonality and history rather than a single static threshold set by a human.\u00a0<\/span><span data-ccp-props=\"{&quot;134233117&quot;:true,&quot;134233118&quot;:true,&quot;201341983&quot;:0,&quot;335551550&quot;:6,&quot;335551620&quot;:6,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><b><i><span data-contrast=\"auto\">With these algorithm-set thresholds, an APM typically will send fewer alerts, but these alerts will be far more intelligent, actionable, and valuable.<\/span><\/i><\/b><span data-ccp-props=\"{&quot;134233117&quot;:true,&quot;134233118&quot;:true,&quot;201341983&quot;:0,&quot;335551550&quot;:6,&quot;335551620&quot;:6,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">Predictive analytics can predict the future behavior and it can provide certain recommendations, but it cannot provide a complete course of action to handle the various scenarios.\u00a0<\/span><span data-contrast=\"auto\">Organizations<\/span><span data-contrast=\"auto\">\u00a0<\/span><span data-contrast=\"auto\">will\u00a0<\/span><span data-contrast=\"auto\">have to plan\u00a0<\/span><span data-contrast=\"auto\">how<\/span><span data-contrast=\"auto\">\u00a0to handle the various outcomes coming out of the Predictive analytics models.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:true,&quot;134233118&quot;:true,&quot;201341983&quot;:0,&quot;335551550&quot;:6,&quot;335551620&quot;:6,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><a href=\"https:\/\/www.cigniti.com\/contact-us\/?utm_source=blog&amp;utm_medium=hyperlink&amp;utm_campaign=ContactUs\"><span data-contrast=\"none\">Connect<\/span><\/a><span data-contrast=\"auto\">\u202fwith our experts today to resolve your\u00a0<\/span><a href=\"https:\/\/www.cigniti.com\/services\/performance-engineering\/\"><span data-contrast=\"none\">Performance Engineering<\/span><\/a><span data-contrast=\"auto\">\u00a0and Management<\/span><span data-contrast=\"auto\">\u00a0needs.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:true,&quot;134233118&quot;:true,&quot;201341983&quot;:0,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>According to Forrester \u201cYou\u00a0can\u2019t\u00a0see into the future (yet), but with predictive analytics you can make an educated guess.\u201d\u00a0 Predictive models utilize the past data to determine the likelihood of certain future outcomes. With predictive analytics businesses can take informed decisions to plan in an optimized manner.\u00a0 Predictive analytics can benefit\u00a0your\u00a0IT teams\u202fin many ways. It gives [&hellip;]<\/p>\n","protected":false},"author":50,"featured_media":15136,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[2559],"tags":[3379,833,2029,204,2032,432,214,2031,81],"ppma_author":[3768],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v19.8 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Use of Predictive Analytics in Application Performance Management<\/title>\n<meta name=\"description\" content=\"Application Performance Monitoring (APM) helps monitor the user experience and leverages predictive analytics to improve the application performance.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.cigniti.com\/blog\/use-of-predictive-analytics-apm\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Use of Predictive Analytics in Application Performance Management\" \/>\n<meta property=\"og:description\" content=\"According to Forrester \u201cYou can\u2019t see into the future (yet), but with predictive analytics you can make an educated guess.\u201d Application Performance Monitoring (APM) helps monitor the user experience and leverages predictive analytics to improve the application performance, identify root causes for application performance issues, monitor application health in real-time, and predict user load &amp; application outages before they happen. 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