Abstract: In one aspect, a method for automated discovery of business flows comprising: business transaction or trace data collected from requests being submitted from and to an application; and parsing each incoming trace data based on different service operations within the trace data to find unique business flows; and aggregating and compressing traces that have same repeated services and or operations into the same business flow to reduce the number of business flows for more efficient collection and monitoring by the business flow processing controller.

Abstract: A computerized method for optimizing cloud application performance, including the step of monitoring of a cloud application. The method includes the step of building a full-stack view of the cloud application. The method includes the step of providing an application model. The method includes the step of mapping one or more cloud application performance needs to a set of cloud-resources based on the application model. The method includes the step of detecting a performance problem with the cloud application. The method includes the step of dynamically adjusting a specified layer of the cloud application to meet an application performance SLO. The method includes the step of, as cloud resources are consumed, determining a real-time aggregate cost for a specified application operation.

Abstract: A computerized method for optimizing cloud application performance, including the step of monitoring of a cloud application. The method includes the step of building a full-stack view of the cloud application. The method includes the step of providing an application model. The method includes the step of mapping one or more cloud application performance needs to a set of cloud-resources based on the application model. The method includes the step of detecting a performance problem with the cloud application. The method includes the step of dynamically adjusting a specified layer of the cloud application to meet an application performance SLO. The method includes the step of, as cloud resources are consumed, determining a real-time aggregate cost for a specified application operation.

Abstract: In one aspect, a method for dynamic provisioning storage for virtual machines by meeting the service level objectives (SLOB) set in the service level agreement (SLA) is provided. The SLA pertains to the operation of a first virtual machine, the method includes the step of monitoring the workload of the first virtual machine. The method includes the step of establishing at least one SLO, typically on performance, in response to the workload. The SLO comprises a set of specific performance targets requirements for a service level of the workload of the first virtual machine that are designed to be met by the provisioned resource so as to comply with the SLA by meeting the SLO. The provisioned resource is associated with the first virtual machine. The method determines an SLA that specifies the first SLO. The SLA comprises a contract that includes consequences of meeting or missing the SLO.

Abstract: In one aspect, a computerized method for managing autonomous cloud application operations includes the step of providing a cloud-based application. The method includes the step of implementing a discovery phase on the cloud-based application. The discovery phase comprises ingesting data from the cloud-based application and building an application graph of the cloud-based application. The application graph represents a structural topology and a set of directional dependencies within and across the layers of the cloud-based application. The method includes the step of, with the application graph, implementing anomaly detection on the cloud-based application by building a set of predictive behavior models from a predictive understanding of the complete application using a priori curated knowledge and one or more machine learning (ML) models. The set of predictive behavior models fingerprints a behavior of the cloud-based application behavior. The method predicts expected values of key indicators.

Abstract: In one aspect, a computerized method for managing autonomous cloud application operations includes the step of providing a cloud-based application. The method includes the step of implementing a discovery phase on the cloud-based application. The discovery phase comprises ingesting data from the cloud-based application and building an application graph of the cloud-based application. The application graph represents a structural topology and a set of directional dependencies within and across the layers of the cloud-based application. The method includes the step of, with the application graph, implementing anomaly detection on the cloud-based application by building a set of predictive behavior models from a predictive understanding of the complete application using a priori curated knowledge and one or more machine learning (ML) models. The set of predictive behavior models fingerprints a behavior of the cloud-based application behavior. The method predicts expected values of key indicators.

Abstract: A computerized method for optimizing cloud application performance, including the step of monitoring of a cloud application. The method includes the step of building a full-stack view of the cloud application. The method includes the step of providing an application model. The method includes the step of mapping one or more cloud application performance needs to a set of cloud-resources based on the application model. The method includes the step of detecting a performance problem with the cloud application. The method includes the step of dynamically adjusting a specified layer of the cloud application to meet an application performance SLO. The method includes the step of, as cloud resources are consumed, determining a real-time aggregate cost for a specified application operation.

Abstract: Methods for provisioning storage for virtual machines by meeting a service level agreement (SLA) are disclosed. The SLA pertains to the operation of a virtual machine. An example of the method includes monitoring the workload of the first virtual machine; establishing at least one service level objective (SLO) in response to the observed workload; determining an SLA that meets the at least one SLO, wherein the SLA defines the time the SLO is satisfied; and provisioning at least one resource used by the first virtual machine in response to the SLA not being satisfied, wherein the provisioning causes the SLA to be satisfied.

Abstract: Techniques for power analysis for data centers are disclosed. In one particular exemplary embodiment, the techniques may be realized as a method for power analysis for a plurality of computing platform components comprising receiving information associated with a component, retrieving, using a computer processor, electronically stored data associated with the component, estimating power usage of the component based at least in part on the stored data, and outputting an indicator of power usage.

Abstract: Methods and systems described herein implement an SLA-based dynamic provisioning of storage for virtualized applications or virtual machines (VMs) on shared storage. The shared storage can be located behind a storage area network (SAN) or on a virtual distributed storage system that aggregates storage across direct attached storage in the server or host, or behind the SAN or a WAN.

Abstract: A method, operating model, system, method, computer program, application, online service, or application program interface (API) Application Program Interface (API), and computer program product for analyzing any email message or text, online post, online web pages, social media sites, and online news sites to detect predefined and actionable events and intent. A method for detecting important emails or messages, and actionable emails or messages that signify intent including questions or promises. A method for detecting past or possible future events in any online posts where the event is defined a priori.

Abstract: Various embodiments of the present invention disclose a method for Business Intelligence (BI) metrics on unstructured data. Unstructured data is collected from numerous data sources that include unstructured data as ingested data. The ingested data is indexed and represents hyperlink and extracted data and metadata for each document. Thereafter, the ingested data is automatically classified into one or more relevance classes. Further, numerous analytics are performed on the classified data to generate business intelligence metrics that may be presented on an access device operated by a user.

Abstract: A method and system for adaptive classification during information retrieval from unstructured data are provided. The method includes receiving input from a user defining a classification. A sample set of unstructured data based on the user defined classification defined is determined. The sample set of unstructured data is analyzed to determine a classification mapping that maps attributes of the sample set of unstructured data to class labels for the classification. The attributes of a set of data objects in a second set of unstructured data are indexed and one or more data objects in the set of data objects are mapped to the class label based on the classification mapping. Feedback based on the user's response to an interaction with results is determined using the class label. Finally, adaptive classification mapping is performed based on analysis of feedback by adjusting the sample set of data objects.

Abstract: In one embodiment, input is received from a user defining a classification and an analytic for the classification. Multiple classifications and analytics may be defined by a user. A definition of relevance parameters is determined that characterize the classification and a set of analytics measures associated with the analytic. The definition may be for the classification. Unstructured data and structured data are analyzed based on the definition of the relevance parameters to determine relevant data in the unstructured data and the structured data. The relevant data being data that is determined to be relevant to the classification defined by the user. An index of the terms from the relevant data is determined. The index is useable by an analytics tool to provide results for queries of the unstructured data and structured data. The query may be used within the classification such that targeted results are provided using the index and the relevant data to the classification.

Abstract: In one embodiment, input is received from a user defining a classification and an analytic for the classification. Multiple classifications and analytics may be defined by a user. A definition of relevance parameters is determined that characterize the classification and a set of analytics measures associated with the analytic. The definition may be for the classification. Unstructured data and structured data are analyzed based on the definition of the relevance parameters to determine relevant data in the unstructured data and the structured data. The relevant data being data that is determined to be relevant to the classification defined by the user. An index of the terms from the relevant data is determined. The index is useable by an analytics tool to provide results for queries of the unstructured data and structured data. The query may be used within the classification such that targeted results are provided using the index and the relevant data to the classification.

Abstract: In one embodiment, input is received from a user defining a classification and an analytic for the classification. Multiple classifications and analytics may be defined by a user. A definition of relevance parameters is determined that characterize the classification and a set of analytics measures associated with the analytic. The definition may be for the classification. Unstructured data and structured data are analyzed based on the definition of the relevance parameters to determine relevant data in the unstructured data and the structured data. The relevant data being data that is determined to be relevant to the classification defined by the user. An index of the terms from the relevant data is determined. The index is useable by an analytics tool to provide results for queries of the unstructured data and structured data. The query may be used within the classification such that targeted results are provided using the index and the relevant data to the classification.

Abstract: Methods for preventing the failure of disk drives in storage systems are disclosed. A system and a computer program product for preventing the failure are also disclosed. Factors relating to the aging or early onset of errors in a disk drive are monitored. These factors are then compared to thresholds. In case the thresholds are exceeded, an indication for the replacement of the disk drive is given. Sudden rises in the factors are also used to indicate the impeding failure of disk drives.

Abstract: Techniques for power analysis for data centers are disclosed. In one particular exemplary embodiment, the techniques may be realized as a method for power analysis for a plurality of computing platform components comprising receiving information associated with a component, retrieving, using a computer processor, electronically stored data associated with the component, estimating power usage of the component based at least in part on the stored data, and outputting an indicator of power usage.

Abstract: Various embodiments of the present invention disclose a method for Business Intelligence (BI) metrics on unstructured data. Unstructured data is collected from numerous data sources that include unstructured data as ingested data. The ingested data is indexed and represents hyperlink and extracted data and metadata for each document. Thereafter, the ingested data is automatically classified into one or more relevance classes. Further, numerous analytics are performed on the classified data to generate business intelligence metrics that may be presented on an access device operated by a user.

Abstract: A method and system for adaptive classification during information retrieval from unstructured data are provided. The method includes receiving input from a user defining a classification. A sample set of unstructured data based on the user defined classification defined is determined. The sample set of unstructured data is analyzed to determine a classification mapping that maps attributes of the sample set of unstructured data to class labels for the classification. The attributes of a set of data objects in a second set of unstructured data are indexed and one or more data objects in the set of data objects are mapped to the class label based on the classification mapping. Feedback based on the user's response to an interaction with results is determined using the class label. Finally, adaptive classification mapping is performed based on analysis of feedback by adjusting the sample set of data objects.