Accenture Global Solutions Limited

Arun Purushothaman

Madhan Kumar Srinivasan

Guruprasad Pv

Manish Sharma Kolachalam

Michael S Eisenstein

Vijay Desai

Vijaya Tapaswi Achanta

Kishore Kumar Gajula

Mythili Krishnan

Kunal Bhowmick

Vinita V Nair

Dnyaneshwar Ambhore

Anand Janardhanan

Lalitha Sundar

Bhavana Rao

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

A system may support multiple tier serverless data foundation creation to support large data set processing. At a data ingestion tier, data ingestion serverless tasks may receive source data for processing. The data integration serverless tasks may filter and group the source data into file-object stored items. Further, data integration serverless tasks may capture metadata that, when paired with the file-object stored items, establishes the data foundation. The data foundation facilitates database-like performance in data operations in a database-less system. At the processing tier, the processing serverless tasks access the data foundation by iterating across the file-object stored items to generate output-object stored items. At the directed storage tier, directed storage serverless tasks capture metadata for the output-object stored items to establish an output data foundation or prepare the output data for storage in a data warehouse.

Cloud-based database-less serverless framework using data foundation

A multi-layer compute sizing correction stack may generate prescriptive compute sizing correction tokens for controlling sizing adjustments for computing resources. The input layer of the compute sizing correction stack may generate cleansed utilization data based on historical utilization data received via network connection. A prescriptive engine layer may generate a compute sizing correction trajectory detailing adjustments to sizing for the computing resources. Based on the compute sizing correction trajectory, the prescriptive engine layer may generate the compute sizing correction tokens that that may be used to control compute sizing adjustments prescriptively.

Prescriptive analytics based compute sizing correction stack for cloud computing resource scheduling

A multi-layer resource aggregation (RA) stack may generate prescriptive activation timetables for controlling activation states for computing resources. To facilitate operator control and adjustment, the RA stack may, at an aggregation engine layer, aggregate the computing resource into one or more resource aggregates. The computing resources within the resource aggregates may have similar individual activation prescription patterns. Machine learning techniques may be used by the RA stack to identify these related individual activation prescription patterns and aggregate the computing resources accordingly. Once aggregated, the RA stack may make a uniform activation determination for the aggregates as single units. Therefore, the computing resources within the aggregate may be controlled and/or adjust together. Thus, the RA stack increases the scalability of implementation of prescriptive computing resource activation state determinations.

Machine-learning-based aggregation of activation prescriptions for scalable computing resource scheduling

A multi-layer committed compute reservation stack may generate prescriptive reservation matrices for controlling static reservation for computing resources. A transformation layer of the committed compute reservation stack may generate a time-mapping based on historical utilization and tagging data. An iterative analysis layer may determine a consumption-constrained committed compute state of a distribution of static reservation and dynamic requisition that achieves one or more consumption efficiency goals. Once the consumption-constrained committed compute state is determined, the prescriptive engine layer may generate a reservation matrix that may be used to control computing resource static reservation prescriptively.

Prescriptive analytics based committed compute reservation stack for cloud computing resource scheduling

A multi-layer rate sizing stack may generate prescriptive operation-rate tokens for controlling sizing adjustments for operation-rates. The input layer of the rate sizing stack may generate operation pattern data based on operation-rate data received via network connection. A prescriptive engine layer may determine a prescriptive allowed operation-rate based on the operation pattern data. Based on the prescriptive allowed operation-rate, the prescriptive engine layer may generate the operation-rate tokens that that may be used to control rate sizing adjustments prescriptively.

Prescriptive analytics based operation-rate sizing stack for cloud storage

A cloud migration tool manages and monitors a cloud migration project that migrates data from a legacy environment to a target data center environment. The cloud migration tool includes an analytics engine that applies data regression models to generate a delay risk prediction for activities that are scheduled during the cloud migration project.

Hybrid cloud migration delay risk prediction engine

A multi-layer storage class placement stack may generate a token containing storage class placement prescriptions for controlling the placement of stored items within a selection of classes for storage. An input layer of the storage class placement stack may generate time-collated activity data based on historical access data, volume metric data, and/or tagging data. The time-collated activity data may include data groupings using timestamps or other timing indicators. A transformation layer may further process the time-collated activity data to generate defined-period summation data that provides summary detail for defined durations across a period of analysis. The defined-period summation data may be used by a prescriptive engine layer to generate prescriptions for placement of individual stored items by associating the prescriptions with storage identifiers for the individual items. Using the prescriptions, the prescriptive engine layer may generate the token to send to a host interface for management of the stored items.

Prescriptive analytics based storage class placement stack for cloud computing

A system may support distributed multiple tier multi-node serverless analytics task execution. At a data ingestion tier, data ingestion serverless tasks may receive detail data for analytic processing. data integration serverless tasks, executing at a data integration and consolidation tier and initiated by the data ingestion serverless tasks, may sort the detail data and identify patterns within the detail data to generate grouped pre-processed data. The data integration serverless tasks may initiate partitioning serverless tasks which may divide the grouped pre-processed data into data chunks. Multi-node analytic serverless tasks at an analytic tier, at least some of which being initiated by the partitioning serverless tasks, may analyze the data chunks and generate prescriptive outputs.

Distributed multiple tier multi-node serverless framework for complex analytics task execution

A system for elastic volume type selection and optimization is provided. The system may detect that a block storage volume was provisioned by a public cloud computing platform based on a first volume type identifier of a first volume type. The system may determine, based on a normalization model, a baseline operation rate and a baseline throughput rate for the provisioned block storage volume. The system may determine, based on a selected transition mode and historical performance measurements, a simulated operation rate and a simulated throughput rate. The system may communicate, in response to the simulated throughput being greater than the baseline throughput rate or the simulated operation rate being greater than the baseline operation rate, a provisioning instruction to re-provision the provisioned block storage volume on the cloud computing platform.

Elastic storage volume type selection and optimization engine for public cloud environments

A multi-layer compute sizing correction stack may generate prescriptive compute sizing correction tokens for controlling sizing adjustments for computing resources. The input layer of the compute sizing correction stack may generate cleansed utilization data based on historical utilization data received via network connection. The input layer may receive one or more resource configurations that may be applied to implement the sizing correction. A prescriptive engine layer may generate a compute sizing correction trajectory indicative of a sizing adjustment to a computing resource. The compute sizing correction trajectory may account of historic processor, network, and memory utilization. Based on the compute sizing correction trajectory and a selected resource configuration, the prescriptive engine layer may generate the compute sizing correction tokens that that may be used to control compute sizing adjustments prescriptively.

Prescriptive cloud computing resource sizing based on multi-stream data sources

A multi-layer activation timetable stack may generate prescriptive activation timetables for controlling activation states for computing resources. An input layer of the activation timetable stack may generate time-scaled pattern data. A transformation layer may identify trends and variables at era timescales. A data treatment layer may flag activation states based on the trends identified at the era timescales. Once the activation states a flagged, the prescriptive engine layer may generate an activation timetable that may be used to control computing resource activation prescriptively.

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Chennai, India

Arun Purushothaman