{"id":15134,"date":"2021-01-18T22:02:14","date_gmt":"2021-01-18T16:32:14","guid":{"rendered":"https:\/\/coforge.site\/cigniti\/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:\/\/coforge.site\/cigniti\/blog\/use-of-predictive-analytics-apm\/","title":{"rendered":"Use of Predictive Analytics in Application Performance Management"},"content":{"rendered":"\n\n\n
According to Forrester, \u201cYou\u00a0can\u2019t\u00a0see into the future (yet), but with predictive analytics, you can make an educated guess.\u201d<\/p>\n

Predictive models utilize past data to determine the likelihood of certain future outcomes. With predictive analytics, businesses can make informed decisions to optimize their planning.<\/em><\/strong><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

What is Predictive Analytics?<\/h2>\n

Predictive analytics uses statistical techniques and algorithms to analyze historical data and forecast future events. Predictive analytics can benefit\u00a0your\u00a0IT 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.<\/p>\n

Predictive analytics\u202ftools\u00a0can\u00a0also\u00a0be\u00a0used to\u00a0predict the user experience at a\u00a0projected user load and evaluate whether the current infrastructure can support the projected\u00a0user growth.\u00a0They also aid in APM to boost overall application efficiency.<\/p>\n

What is APM?<\/h2>\n

Application performance monitoring (APM) is a subset of Application Performance Management that focuses specifically on the real-time tracking and reporting of an application’s performance metrics. It\u00a0helps\u00a0monitor the user experience and leverages\u00a0predictive analytics to improve application performance\u00a0whenever there is a sudden spike in the number of users accessing the application or whenever there is a sudden degradation in application performance.<\/p>\n

Predictive Analytics\u00a0Usage<\/h2>\n

Organizations\u00a0can\u00a0leverage\u00a0predictive analytics\u00a0to improve their application performance\u00a0in the\u00a0following\u00a0areas:<\/p>\n

Identify root causes for application performance\u00a0issues<\/h3>\n

By identifying root causes for application performance using\u202fmachine learning\u00a0techniques,\u00a0organizations\u00a0can focus on the right set of areas in which\u202fto take action. Predictive analytics\u00a0can then\u202fstudy the characteristics of the various attributes within each cluster\u00a0that\u00a0can provide deep insights into what\u202fchanges need to\u00a0be\u00a0made to achieve ideal performance and avoid specific bottlenecks.<\/p>\n

Monitor\u202fapplication health in real\u2013time<\/h3>\n

Performing real-time monitoring of application health via multi-variate machine-learning (ML) techniques allows\u00a0organizations\u00a0to catch and respond to\u00a0the\u202fdegradation of application health on time. 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.\u00a0Predictive analytics models can\u00a0analyze past data during a time in which the application was in a good state\u00a0and subsequently\u00a0identify whether the incoming data exhibits normal behavior or not.<\/p>\n

Predict\u00a0user load<\/h3>\n

Predictive analytics can help predict the user load by analyzing past data. Organizations can use this data to better prepare to handle the predicted user load and provide experience\u00a0assurance, which would help reduce customer churn. This data can help organizations better plan their future IT infrastructure requirements and capacity utilization.<\/p>\n

Predict application outages before they happen<\/h3>\n

Predicting application downtime or outages before they happen helps perform\u00a0the\u00a0needed maintenance on the\u00a0application without any downtime. This can save an organization time,\u00a0money, and much more. Before an application outage, the IT infrastructure leaves many indirect clues hours, or even days, before it dies. The\u00a0predictive analytics\u00a0model\u00a0can\u00a0learn those patterns and continue to monitor for similar occurrences, predicting future failures before they happen. With this predictive model in place, preventive action\u00a0can be taken\u00a0at the right time.<\/p>\n

Applying predictive analytics to forecast application performance<\/h2>\n

Predictive analytics in application performance improvement focuses on three main areas \u2013\u00a0Forecasted\u00a0User Load,\u00a0Response\u00a0Time prediction,\u00a0and\u00a0Infrastructure\u00a0Assessment.<\/p>\n

User Load\u00a0Prediction<\/h3>\n

Traditionally organizations have relied on peak user traffic in the past 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 the emergence of\u00a0new technologies, changes in user behavior, and other disruptive\u00a0factors. By using\u00a0AI\/ML\u00a0predictive analytics,\u00a0businesses can avoid these pitfalls, as\u00a0forecasting\u00a0models can be built\u00a0by analyzing user behavior in real-time.<\/p>\n

Using the data provided by the APM we\u00a0have built\u00a0a model to predict the number of users who will access the application\u00a0six\u00a0months\u00a0into the future.\u00a0We used\u00a0the hourly monitoring data from production monitoring for the past 2 years\u00a0as input to the model.\u00a0We found that the accuracy provided by the model was low\u00a0when we used classification and regression models\u00a0due to missing data and data imbalance.<\/p>\n

The data\u00a0was normalized\u00a0to handle missing data and data wrangling\u00a0performed\u00a0to mitigate the data imbalance and fed to a neural network. This increased\u00a0the accuracy of the model to approx.\u00a075\u201380%.\u00a0The model\u00a0was then converted\u00a0to\u00a0a\u00a0self-learning\u00a0algorithm\u00a0by retraining the model on\u00a0live data\u00a0which\u00a0further\u00a0increased the\u00a0model accuracy by\u00a08-10%.<\/p>\n

Such\u00a0accurate\u00a0forecast helps\u00a0decrease\u00a0the\u00a0customer churn rate.<\/p>\n

Response Time Prediction<\/h3>\n

APM tools\u00a0capture\u00a0a lot of\u00a0application\u00a0data\u00a0and hardware utilization details.\u00a0This data can be\u00a0leveraged\u00a0to predict the application\u00a0response time.<\/p>\n

To predict the application response time for a given user load, Exploratory Data Analysis using the user load as the input\u00a0provides low\u00a0accuracy and correlation.\u00a0Even\u00a0adding\u00a0the application server hardware utilization details to the inputs and using\u00a0multilinear regression models\u00a0does\u00a0not\u00a0help\u00a0achieve the required accuracy.<\/p>\n

However, when we\u00a0add the\u00a0database server hardware utilization details, network\u00a0telemetry, and middleware utilization details to the inputs and use\u00a0the\u00a0ARIMA models,\u00a0we find\u00a0that the model accuracy\u00a0may\u00a0increase to more than 75%.<\/p>\n\n\n\n
This model helped our customers\u00a0tweak the application architecture and server hardware to improve the application performance for the anticipated user load. This\u00a0also\u00a0helped in improving the user experience and better customer retention during the peak season.<\/em><\/strong><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u00a0<\/strong><\/p>\n

Infrastructure\u00a0Capacity\u00a0Prediction<\/h3>\n

Apart from\u00a0building the models to predict the user load and response times, we\u00a0have also\u00a0developed a model that estimates whether a given piece of hardware can handle a given user load.\u00a0By tweaking the input parameters to the model, we\u00a0can also\u00a0predict the infrastructure required to handle a\u00a0particular user\u00a0load. To achieve this, a model\u00a0was built\u00a0by leveraging the APM data from production for a period of two years.<\/p>\n

With a 75%\u00a0accurate\u00a0model,\u00a0we\u00a0then modified\u00a0the hardware configuration to predict the application’s health.<\/p>\n\n\n\n
This model helped\u00a0our customers plan for cloud migration by evaluating the infrastructure requirements and the costs associated with infrastructure upgrades versus cloud migration.<\/em><\/strong><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u00a0<\/strong><\/p>\n

Data is\u00a0the new oil \u2013 are you\u00a0analyzing\u00a0it?<\/h2>\n

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 the reality is sobering: Only 7% of firms report advanced, insights-driven practices.\u201d<\/p>\n

Businesses need to spend some time to realize the benefits. The predictive analytics implementation needs time as data has to be prepared, cleansed, and correlated,\u00a0the\u00a0right algorithm must be chosen to process the data, the algorithmic output\u00a0must be understood,\u00a0and\u00a0the algorithm\u00a0must be retrained\u00a0with new sets of data and optimized\u00a0until the required success factors are met. We can accelerate the deployment time by leveraging our experience with a similar application or domain.<\/p>\n

Since predictive analytics depend on the data to predict 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.<\/p>\n

To simplify the\u00a0implementation of predictive analytics<\/a>, we\u00a0at Cigniti\u00a0are developing\u00a0a process that\u00a0will\u00a0monitor\u00a0key\u00a0performance indicators and develop its own unique system for setting\u00a0dynamic\u00a0thresholds. Essentially, the machine-based learning process\u00a0will\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 aggregating historical data.\u00a0It\u00a0will\u00a0allow for thresholds based on seasonality and history rather than a single static threshold set by a human.<\/p>\n\n\n\n
With these algorithm-set thresholds, an APM typically will send fewer alerts, but these alerts will be far more intelligent, actionable, and valuable.<\/em><\/strong><\/p>\n

\u00a0<\/em><\/strong><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Conclusion<\/h2>\n

Predictive analytics can predict future behavior and provide certain recommendations, but it cannot provide a complete course of action to handle the various scenarios.\u00a0Organizations\u00a0will\u00a0have to plan\u00a0how\u00a0to handle the various outcomes coming out of the Predictive analytics models.<\/p>\n

Connect<\/a>\u202fwith our experts today to resolve your\u00a0Performance Engineering<\/a>\u00a0and Management\u00a0needs.<\/p>\n","protected":false},"excerpt":{"rendered":"

According to Forrester, \u201cYou\u00a0can\u2019t\u00a0see into the future (yet), but with predictive analytics, you can make an educated guess.\u201d Predictive models utilize past data to determine the likelihood of certain future outcomes. With predictive analytics, businesses can make informed decisions to optimize their planning. What is Predictive Analytics? Predictive analytics uses statistical techniques and algorithms to […]<\/p>\n","protected":false},"author":50,"featured_media":15136,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[2559],"tags":[3379,833,2029,204,2032,432,214,2031,81],"ppma_author":[3768],"class_list":["post-15134","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-performance-engineering","tag-application-performance-management","tag-application-performance-testing","tag-big-data-and-analytics","tag-digital-transformation","tag-enterprise-predictive-analytics","tag-performance-engineering","tag-performance-testing","tag-predictive-analytics-in-qa","tag-software-performance-testing"],"authors":[{"term_id":3768,"user_id":50,"is_guest":0,"slug":"venkateswarlu","display_name":"Venkateswarlu Guttena","avatar_url":"https:\/\/secure.gravatar.com\/avatar\/1e31c4c9975f255608dab51b2f213f84c669627760f6ed3eb9fd4a81e1081689?s=96&d=mm&r=g","author_category":"","user_url":"","last_name":"Guttena","first_name":"Venkateswarlu","job_title":"","description":"Venkateswarlu Guttena is currently working as an architect in Cigniti Technologies. He has 15+ years of experience in Software Industry with different Domains like Airlines, Travel, Product Lifecycle Management. He has more than 10 years of experience in performance testing. He is well versed in the area of Application Performance Management (APM) using monitoring tools like AppDynamics and NewRelic."}],"_links":{"self":[{"href":"https:\/\/coforge.site\/cigniti\/blog\/wp-json\/wp\/v2\/posts\/15134","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/coforge.site\/cigniti\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/coforge.site\/cigniti\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/coforge.site\/cigniti\/blog\/wp-json\/wp\/v2\/users\/50"}],"replies":[{"embeddable":true,"href":"https:\/\/coforge.site\/cigniti\/blog\/wp-json\/wp\/v2\/comments?post=15134"}],"version-history":[{"count":0,"href":"https:\/\/coforge.site\/cigniti\/blog\/wp-json\/wp\/v2\/posts\/15134\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/coforge.site\/cigniti\/blog\/wp-json\/wp\/v2\/media\/15136"}],"wp:attachment":[{"href":"https:\/\/coforge.site\/cigniti\/blog\/wp-json\/wp\/v2\/media?parent=15134"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/coforge.site\/cigniti\/blog\/wp-json\/wp\/v2\/categories?post=15134"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/coforge.site\/cigniti\/blog\/wp-json\/wp\/v2\/tags?post=15134"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/coforge.site\/cigniti\/blog\/wp-json\/wp\/v2\/ppma_author?post=15134"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}