Accenture Global Solutions Limited

Kunal Bhowmick

Mythili Krishnan

Vinita V Nair

Vijay Desai

Arun Purushothaman

Mriga Bansal

Dnyaneshwar Ambhore

Anand Janardhanan

Chriss Acca Mathews

Lalitha Sundar

Madhusudhan D T

Bhavana Rao

Narayanaswamy Thimmappa

A resource data modeling, forecasting, and simulation system analyzes data pertaining to the data processing tasks and the resources assigned to the data processing tasks to generate short-term forecasts and long-term forecasts of task volumes. The forecasted task volumes are further optimized based on different factors to determine the resources required to handle the forecasted task volume. Various simulations of hypothetical what-if scenarios are also generated based on the forecasts and the resource requirements. The resource data modeling, forecasting and simulation system is based on multi-algorithmic ensemble models for forecasting, automated model selection and the unique simulation methodology based on multiple parameters.

Resource data modeling, forecasting and simulation

All modern enterprises rely completely on the continual correct execution of hardware and software resources that constitute the information technology (IT) infrastructure environment for the business. At the same time, hardware and software resources continually evolve, and the enterprise must often make changes to its infrastructure to incorporate the new or updated hardware and software resources. These changes are risky, and failure to properly execute the changes can result in infrastructure roll-backs and other failure modes that are often very disruptive to the enterprise. A guidance system for changes in infrastructure increases the likelihood of successful implementation of the changes. In some implementations, for example, the guidance system provides clear guidelines and checklists that depict success criteria, along with a scoring interface that pro-actively indicates the factors that can go wrong, and how to mitigate and plan for the factors in advance.

Guidance system for enterprise infrastructure change

A device may process text included in an electronic issue ticket to identify a set of terms or a pattern of terms included in the text. The electronic issue ticket may be related to an issue associated with a computing device. The device may classify the electronic issue ticket into an issue category of a set of issue categories. The set of issue categories may be related to various issues associated with various computing devices. The device may monitor a counter for a set of thresholds associated with the issue category. The device may detect a possible underlying problem among the various computing devices. The device may generate an electronic problem ticket for the possible underlying problem. The device may append the electronic problem ticket with information related to the electronic issue ticket or one or more other electronic issue tickets. The device may perform one or more actions.

Detecting a possible underlying problem among computing devices

Data from an application is analyzed periodically in real-time using a predictive data model to predict potential application failures. Application logs are used to build the predictive data model. The predictive data model includes features created from the application logs which are good predictors of application failures. The predictive data model analyzes and provides anomaly scores for various anomalies for the real-time data collected over a time period. The anomaly scores are compared with a threshold score to predict the probability of occurrences of application failures. If there is a higher probability of occurrence of an incident of application failure, corrective actions to mitigate or reduce the probability of occurrence of the application failure are implemented.

Application monitoring and failure prediction

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Bengaluru, India

Kunal Bhowmick