Accenture Global Solutions Limited

Jagaran Das

Teresa Sheausan Tung

Srinivas Yelisetty

Abdul Rahman Zafar

Praveen Jadhav

Daniel Corin

Anuruddha S Khastgir

Emanuele Baldassarre

Murtuza Chitalwala

Luigi Lucca

Hossam E Elsherif

Rino Guglietta

Nikhil S Tanwar

Sekhar A Badugu

Tri Phi Minh Nguyen

Prudhvi Laxmi Narayana Chelluri

Prasun Chandra

Nirmalya Chakraborty

Biju Radhakrishnan

Rizwan Khan

Biswajit Rout

Supratim Bandyopadhyaya

A system of monitoring and optimizing computing resources usage for computing application may include predicting a first performance metric for job load capacity of a computing application for optimal job concurrency and optimal resource utilization. The system may include generating an alerting threshold based on the first performance metric. The system may further include, in response to a difference between the alerting threshold and a job load of the computing application within an interval exceeding a threshold, predicting a second performance metric for job load capacity of the computing application for optimal job concurrency and optimal resource utilization. The system may further include, in response to a difference between the first performance metric and the second performance metric exceeding a difference threshold, updating the alerting threshold with a job load capacity with the optimal resource utilization rate corresponding to the second performance metric.

System for monitoring and optimizing computing resource usage of cloud based computing application

This document describes a framework for measuring and improving the performance of applications, such as distributed applications and web applications. In one aspect, a method includes performing a test on an application. The test includes executing the application on one or more computers and, while executing the application, simulating a set of workload scenarios for which performance of the application is measured during the test. While performing the test, a set of performance metrics that indicate performance of individual components involved in executing the application during the test is obtained. A knowledge graph is queried using the set of performance metrics. The knowledge graph links the individual components to corresponding performance metrics and defines a set of hotspot conditions that are each based on one or more of the corresponding performance metrics for the individual components. A given hotspot condition is detected based on the set of performance metrics.

Enhanced application performance framework

A system and method of automating fault prediction and remediation for a multi-tenant system is disclosed. The system and method offer an intelligent augmentation of a multi-tenant system by automating the harvesting and processing of raw data logs generated by the various aspects of the platform as well as the implementation of an appropriate response. In some embodiments, the proposed system includes a hybrid model that can be configured to offer both assisted and unassisted errors. The incorporation of a dynamic learning algorithm minimizes operation errors for any set of computing units. Potential system faults can be detected with little to no human intervention and allow for an unattended platform that collects performance data across the system from a wide range of sources to provide centralized and automated fault prediction, as well as expedited automated resolutions to such faults that depend on little to no human intervention.

System and method for automating fault detection in multi-tenant environments

A cloud resource control circuitry may provide an automatic, self-managed, and a real-time elasticity in an active cloud stack including a software stack. The cloud resource control circuitry particularly adjusts cloud computing resources allocated to the active cloud stack based on real-time remote monitoring of resource utilization of the software stack. The resource adjustment may be performed in a stepwise fashion, in a pipelined manner as to the various layers of a software stack, and using hierarchical approach as to levels of cloud computing resources (e.g., task/level, server level, virtual machine/cloud level), to provide a more efficient cloud computing environment.

Self-managed intelligent elastic cloud stack

A data pipeline architecture is integrated with an analytics processing stack. The data pipeline architecture may receive incoming data streams from multiple diverse endpoint systems. The data pipeline architecture may include converter interface circuitry with multiple dynamic converters configured to convert the diverse incoming data stream into one or more interchange formats for processing by the analytics processing stack. The analytics processing stack may include multiple layers with insight processing layer circuitry above analysis layer circuitry. The analysis layer circuitry may control analytics models and rule application. The insight processing layer circuitry may monitor output from the analysis layer circuitry and generate insight adjustments responsive to rule changes and analytics model parameter changes produced at the analysis layer circuitry.

Data pipeline architecture for analytics processing stack

A cloud control and management circuitry may provide both static rule based and intelligent dynamic backups of a cloud computing environment. The cloud control and management circuitry may monitor the operating status of the cloud computing environment, perform additional analytics on the monitored status, and provide an intelligent policy that is responsive to various levels of the computing entities of the cloud computing environment, and causes automatic backup to prevent unrecoverable and potentially unexpected failures. The cloud control and management circuitry further provide a uniform interface for automatically and intelligently controlling and predicting backup of the computing environment based on disparate cloud computing platforms.

Intelligent backup and recovery of cloud computing environment

Scalable architectures provide resiliency and redundancy and are suitable for cloud deployment. The architectures support extreme data throughput requirements. In one implementation, the architectures provide a serving layer and an extremely high speed processing lane. With these and other features, the architectures support complex analytics, visualization, rule engines, and centralized pipeline configuration.

Data pipeline architecture for cloud processing of structured and unstructured data

Techniques are described for unified identity and access management (IAM) across multiple applications in a distributed (e.g., cloud) computing environment. Implementations provide for IAM across multiple applications through use of a single, unified IAM tool including an IAM interface. The IAM tool can manage user identity and user access rights for multiple applications in the platform environment. The IAM tool can also employ a unified IAM database, which stores user profiles that each describes user access rights for a user in one or more applications. Through use of the unified IAM tool, an operator can access a single interface to manage user identity and access privileges across multiple applications which may have their own identity management interfaces, with user roles in different applications mapped through use of an equivalency matrix in some cases. The IAM tool can be used to add, remove, or edit user access privileges for multiple applications.

Cross-application identity and access management

An applied intelligence framework may receive log information descriptive of a cloud computing stack. The applied intelligence framework may generate a stack token. The stack token may include a computer resource node representative a computer resource of the cloud computing stack. The applied intelligence framework may access, from an ontology repository, a diagnosis instruction. The diagnosis instruction may determine a fault based on at least one of the log parameters. The applied intelligence framework may execute the diagnosis instruction to determine the fault. The applied intelligence framework may append, to the stack token, a fault node representative of the fault. The applied intelligence framework may query an ontology repository based on the stack token to identify a resolution identifier. The applied intelligence framework may append, to the stack token, a resolution node. The applied intelligence framework may determine, based on stack token and the applied ontology repository, a resolution to the fault.

Enriched self-healing for cloud platforms

A data generation stack executing on data processing circuitry may generate substitute data for provision in place of data sample from sensor circuitry. A configuration layer of the data generation stack may store a configuration file. A data ingestion and generation layer of the data generation stack may access the configuration file to determine static relationships among different sensors within the system. The data ingestion and generation layer may further determine dynamic relationships among the sensors. Based on a hybrid relationship that accounts for the static and dynamic relationships, the data ingestion and generation layer may generate substitute data for a sensor based on sampled data from another sensor. A data export layer of the data generation stack may access the generated substitute data for output.

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Durgapur, India

Jagaran Das