Accenture Global Services Gmbh

Accenture Global Solutions Limited

Qian Zhu

Teresa Sheausan Tung

Qing Xie

Jaeyoung Christopher Kang

Desmond R Duggan

Wenjia Sun

Shaw-Yi Chaw

Benjamin Grimmer

A machine provides a system and interface to allow domain experts and other users to develop, deploy, and iterate on analytical models. The machine facilitates building, deploying, and/or training analytical models by, e.g., exposing analytical model configuration parameters to a user while abstracting model building and model deployment activities. The machine can also determine resource loads or execution times for various analytical models and can schedule model execution accordingly. The machine also provides a dynamically reconfigurable user interface for controlling the system.

Machine for development and deployment of analytical models

A machine provides a system and interface to allow domain experts and other users to develop, deploy, and iterate on analytical models. The system facilitates building, deploying, and/or training analytical models, by, e.g., exposing analytical model configuration parameters to a user while abstracting model building and model deployment activities. The system can also determine resource loads or execution times for various analytical models and can schedule model execution accordingly. The system also provides a dynamically reconfigurable user interface for controlling the system.

A machine provides a system and interface to deploy and manage pre-defined analytical models across various compute engines or run time environments, e.g., by exposing analytical model deployment and management parameters to a user while abstracting model deployment activities. The machine also determines proper run time environments for the pre-defined analytical model and verifies the pre-defined analytical model. The machine also provides a dynamically reconfigurable user interface for controlling the machine.

Machine for development of analytical models

The differentiated service-based graceful degradation layer (DSGDL) allows cloud-based architectures to operate through and recover from periods of limited capability. The DSGDL protects and continues serving higher priority requests with the best possible response even as the underlying cloud-based services deteriorate. The DSGDL offloads lower priority requests to lower-grade secondary capability that can be dynamically provisioned in order to reserve the best capability for maintaining high priority service (e.g., by re-directing lower priority requests to a slightly out-of-date cached dataset, and reserve the primary consistent database for higher priority requests). The DSGDL 1) implements an overlay network over existing cloud services to route and enforce priority requests, and 2) provisions on-demand computing nodes and sites to provide secondary capability for service requests as needed.

Differentiated service-based graceful degradation layer

API analytics systems and methods are provided. Frequently occurring API usage patterns may be determined from API call data collected from across an API ecosystem. Alternatively or in addition, a classification structure, such as a decision tree, may be generated from the API usage patterns. A type of activity that resulted in a set of API calls being invoked may be determined from the classification structure. A similarity or difference between the set of API calls and the frequently occurring API usage patterns may also be determined and/or identified.

Analytics for application programming interfaces

According to an example, an adaptive fault diagnosis system may include a memory storing machine readable instructions to receive metrics and events from an enterprise system, and use a substitution graph to determine if a received metric or a received event belongs to a cluster that includes one or more correlated metrics and/or events grouped based on similarity. If the received metric or the received event belongs to the cluster, the memory may further store machine readable instructions to use a detection graph to determine if the received metric or the received event is identifiable to form a fault pattern by traversing a fault path of the detection graph. Further, the memory may further store machine readable instructions to diagnose a fault based on the traversal of the fault path of the detection graph. The system may include a processor to implement the machine readable instructions.

Adaptive fault diagnosis

The workload profiler and performance interference (WPPI) system uses a test suite of recognized workloads, a resource estimation profiler and influence matrix to characterize un-profiled workloads, and affiliation rules to identify optimal and sub-optimal workload assignments to achieve consumer Quality of Service (QoS) guarantees and/or provider revenue goals. The WPPI system uses a performance interference model to forecast the performance impact to workloads of various consolidation schemes (e.g., consolidation strategies) usable to achieve cloud provider and/or cloud consumer goals, and uses the test suite of recognized workloads, the resource estimation profiler and influence matrix, affiliation rules, and performance interference model to perform off-line modeling to determine the initial assignment selections and consolidation strategy to use to deploy the workloads. The WPPI system uses an online consolidation algorithm, the offline models, and online monitoring to determine virtual machine to physical host assignments responsive to real-time conditions to meet cloud provider and/or cloud consumer goals.

Performance interference model for managing consolidated workloads in QOS-aware clouds

Performance interference model for managing consolidated workloads in QoS-aware clouds

The workload profiler and performance interference (WPPI) system uses a test suite of recognized workloads, a resource estimation profiler and influence matrix to characterize un-profiled workloads, and affiliation rules to identify optimal and sub-optimal workload assignments to achieve consumer Quality of Service (QoS) guarantees and/or provider revenue goals. The WPPI system uses a performance interference model to forecast the performance impact to workloads of various consolidation schemes usable to achieve cloud provider and/or cloud consumer goals, and uses the test suite of recognized workloads, the resource estimation profiler and influence matrix, affiliation rules, and performance interference model to perform modeling to determine the initial assignment selections and consolidation strategy to use to deploy the workloads. The WPPI system uses an online consolidation algorithm, the offline models, and online monitoring to determine virtual machine to physical host assignments responsive to real-time conditions to meet cloud provider and/or cloud consumer goals.

The workload profiler and performance interference (WPPI) system uses a test suite of recognized workloads, a resource estimation profiler and influence matrix to characterize un-profiled workloads, and affiliation rules to identify optimal and sub-optimal workload assignments to achieve consumer Quality of Service (QoS) guarantees and/or provider revenue goals. The WPPI system uses a performance interference model to forecast the performance impact to workloads of various consolidation schemes usable to achieve cloud provider and/or cloud consumer goals, and uses the test suite of recognized workloads, the resource estimation profiler and influence matrix, affiliation rules, and performance interference model to perform off-line modeling to determine the initial assignment selections and consolidation strategy to use to deploy the workloads. The WPPI system uses an online consolidation algorithm, offline models, and online monitoring to determine virtual machine to physical host assignments responsive to real-time conditions to meet cloud provider and/or cloud consumer goals.

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Mountain View, CA, United States of America

Qian Zhu