Abstract: Embodiments relate to early detection of information technology (IT) failures in a computing system. A technique is executed by one or more processors and includes receiving multiple IT records including past and recent historical data, extracting first and second time series sections from the past and recent historical data, respectively, training a failure detection model to correlate metric patterns in the first time series sections with at least one of other metric patterns and previous IT failures and, in response to the training, using the failure detection model to predict at least one of upcoming metric patterns and upcoming IT failures from metric patterns in the second time series sections.

Abstract: Embodiments relate to providing automated text summarization techniques for capturing and conveying information technology (IT) records with numerical data. A technique is executed by one or more processors and includes receiving an IT record comprising text and numerical data, normalizing the numerical data into normalized numerical data, transforming the normalized numerical data into comparative and superlative adjectival terms and rewriting the text to include the comparative and superlative adjectival terms for output as a rewritten IT record.

Abstract: Embodiments relate to providing self-learning automated information technology change risk prediction. A processor inputs a change request to a first machine learning model, the first machine learning model determining at least one word pair in the change request, the change request being a modification in an IT environment. The processor classifies the at least one word pair into a change category for the IT environment using a second machine learning model, the change category identifying a type of the modification to be executed in the IT environment. The processor determines a likelihood of causing a problem in the IT environment as a result of executing the modification. The processor automatically performs an action to prevent the modification of the change request in the IT environment.

Abstract: Embodiments relate to providing explainable classifications with abstention using client agnostic machine learning models. A technique includes classifying, by a processor, a record with a label using a machine learning model, the machine learning model abstaining from classifying a given record in response to the given record being outside of a scope of an information technology (IT) domain. The processor generates an explanation of a decision by the machine learning model to classify the record with the label and displays the explanation in a human readable form.

Abstract: Embodiments relate to providing explainable classifications with abstention using client agnostic machine learning models. A technique includes inputting, by a processor, records to a machine learning model, the records being associated with an information technology (IT) domain. The technique includes classifying, by the processor, the records with labels using the machine learning model, the machine learning model abstaining from classifying a given record in response to the given record being outside of a scope of the IT domain.

Abstract: A method, system and computer program product for selecting virtual machines to be migrated to a public cloud. The current resource usage for virtual machine instances running in the private cloud is determined. Furthermore, any scaling policies attached to the virtual machine instances running in the private cloud are obtained. Additional resource usages for any of the virtual machine instances with a scaling policy are computed for when these virtual machine instances are scaled out. A cost of running a virtual machine instance in the public cloud is then determined using its current resource usage as well as any additional resource usage if a scaling policy is attached to the virtual machine instance based on the cost for running virtual machine instances in a public cloud. If the cost is less than a threshold cost, then the virtual machine instance is selected to be migrated to the public cloud.

Abstract: A method, system and computer program product for selecting virtual machines to be migrated to a public cloud. The current resource usage for virtual machine instances running in the private cloud is determined. Furthermore, any scaling policies attached to the virtual machine instances running in the private cloud are obtained. Additional resource usages for any of the virtual machine instances with a scaling policy are computed for when these virtual machine instances are scaled out. A cost of running a virtual machine instance in the public cloud is then determined using its current resource usage as well as any additional resource usage if a scaling policy is attached to the virtual machine instance based on the cost for running virtual machine instances in a public cloud. If the cost is less than a threshold cost, then the virtual machine instance is selected to be migrated to the public cloud.

Abstract: A method, system and computer program product for selecting virtual machines to be migrated to a public cloud. The current resource usage for virtual machine instances running in the private cloud is determined. Furthermore, any scaling policies attached to the virtual machine instances running in the private cloud are obtained. Additional resource usages for any of the virtual machine instances with a scaling policy are computed for when these virtual machine instances are scaled out. A cost of running a virtual machine instance in the public cloud is then determined using its current resource usage as well as any additional resource usage if a scaling policy is attached to the virtual machine instance based on the cost for running virtual machine instances in a public cloud. If the cost is less than a threshold cost, then the virtual machine instance is selected to be migrated to the public cloud.

Abstract: A method, system and computer program product for selecting virtual machines to be migrated to a public cloud. The current resource usage for virtual machine instances running in the private cloud is determined. Furthermore, any scaling policies attached to the virtual machine instances running in the private cloud are obtained. Additional resource usages for any of the virtual machine instances with a scaling policy are computed for when these virtual machine instances are scaled out. A cost of running a virtual machine instance in the public cloud is then determined using its current resource usage as well as any additional resource usage if a scaling policy is attached to the virtual machine instance based on the cost for running virtual machine instances in a public cloud. If the cost is less than a threshold cost, then the virtual machine instance is selected to be migrated to the public cloud.

Abstract: A method, system and computer program product for optimizing runtime performance of an application workload. Network input/output (I/O) operations between virtual machines of a pattern of virtual machines servicing the application workload in a private cloud are measured over a period of time and depicted in a histogram. A score is generated for each virtual machine or group of virtual machines in the pattern of virtual machines based on which range in the ranges of I/O operations per seconds (IOPS) depicted in the histogram has the largest sample size and the number of virtual machines in the same pattern that are allowed to be in the public cloud. In this manner, the runtime performance of the application workload is improved by minimizing the network input/output communications between the two cloud environments by migrating those virtual machine(s) or group(s) of virtual machines with a score that exceeds a threshold value.

Abstract: A method, system and computer program product for optimizing runtime performance of an application workload. Network input/output (I/O) operations between virtual machines of a pattern of virtual machines servicing the application workload in a private cloud are measured over a period of time and depicted in a histogram. A score is generated for each virtual machine or group of virtual machines in the pattern of virtual machines based on which range in the ranges of I/O operations per seconds (IOPS) depicted in the histogram has the largest sample size and the number of virtual machines in the same pattern that are allowed to be in the public cloud. In this manner, the runtime performance of the application workload is improved by minimizing the network input/output communications between the two cloud environments by migrating those virtual machine(s) or group(s) of virtual machines with a score that exceeds a threshold value.

Abstract: A method, system and computer program product for optimizing runtime performance of an application workload. Network input/output (I/O) operations between virtual machines of a pattern of virtual machines servicing the application workload in a private cloud are measured over a period of time and depicted in a histogram. A score is generated for each virtual machine or group of virtual machines in the pattern of virtual machines based on which range in the ranges of I/O operations per seconds (IOPS) depicted in the histogram has the largest sample size and the number of virtual machines in the same pattern that are allowed to be in the public cloud. In this manner, the runtime performance of the application workload is improved by minimizing the network input/output communications between the two cloud environments by migrating those virtual machine(s) or group(s) of virtual machines with a score that exceeds a threshold value.

Abstract: A method, system and computer program product for optimizing runtime performance of an application workload. Network input/output (I/O) operations between virtual machines of a pattern of virtual machines servicing the application workload in a private cloud are measured over a period of time and depicted in a histogram. A score is generated for each virtual machine or group of virtual machines in the pattern of virtual machines based on which range in the ranges of I/O operations per seconds (IOPS) depicted in the histogram has the largest sample size and the number of virtual machines in the same pattern that are allowed to be in the public cloud. In this manner, the runtime performance of the application workload is improved by minimizing the network input/output communications between the two cloud environments by migrating those virtual machine(s) or group(s) of virtual machines with a score that exceeds a threshold value.

Abstract: A solution descriptor comprises a set of component workload units, a workload unit describing a deployable application component with application binary, configuration parameters and dependency declarations. An environment descriptor specifies a set of target platforms and plugins in an execution environment. A deployer interprets the solution descriptor and the environment descriptor, and generates a list of tuples comprising compatible workload-plugin-platform combinations. The deployer determines an execution order for the list of tuples, and invokes the plugins in the list of tuples in the execution order, wherein each of the plugins executes a corresponding compatible workload on a corresponding compatible target platform specified in the list of tuples.

Abstract: A solution descriptor comprises a set of component workload units, a workload unit describing a deployable application component with application binary, configuration parameters and dependency declarations. An environment descriptor specifies a set of target platforms and plugins in an execution environment. A deployer interprets the solution descriptor and the environment descriptor, and generates a list of tuples comprising compatible workload-plugin-platform combinations. The deployer determines an execution order for the list of tuples, and invokes the plugins in the list of tuples in the execution order, wherein each of the plugins executes a corresponding compatible workload on a corresponding compatible target platform specified in the list of tuples.

Abstract: A solution descriptor comprises a set of component workload units, a workload unit describing a deployable application component with application binary, configuration parameters and dependency declarations. An environment descriptor specifies a set of target platforms and plugins in an execution environment. A deployer interprets the solution descriptor and the environment descriptor, and generates a list of tuples comprising compatible workload-plugin-platform combinations. The deployer determines an execution order for the list of tuples, and invokes the plugins in the list of tuples in the execution order, wherein each of the plugins executes a corresponding compatible workload on a corresponding compatible target platform specified in the list of tuples.

Abstract: A solution descriptor comprises a set of component workload units, a workload unit describing a deployable application component with application binary, configuration parameters and dependency declarations. An environment descriptor specifies a set of target platforms and plugins in an execution environment. A deployer interprets the solution descriptor and the environment descriptor, and generates a list of tuples comprising compatible workload-plugin-platform combinations. The deployer determines an execution order for the list of tuples, and invokes the plugins in the list of tuples in the execution order, wherein each of the plugins executes a corresponding compatible workload on a corresponding compatible target platform specified in the list of tuples.

Abstract: A method, system and computer program product for selecting virtual machines to be migrated to a public cloud. The current resource usage for virtual machine instances running in the private cloud is determined. Furthermore, any scaling policies attached to the virtual machine instances running in the private cloud are obtained. Additional resource usages for any of the virtual machine instances with a scaling policy are computed for when these virtual machine instances are scaled out. A cost of running a virtual machine instance in the public cloud is then determined using its current resource usage as well as any additional resource usage if a scaling policy is attached to the virtual machine instance based on the cost for running virtual machine instances in a public cloud. If the cost is less than a threshold cost, then the virtual machine instance is selected to be migrated to the public cloud.

Abstract: A method, system and computer program product for selecting virtual machines to be migrated to a public cloud. The current resource usage for virtual machine instances running in the private cloud is determined. Furthermore, any scaling policies attached to the virtual machine instances running in the private cloud are obtained. Additional resource usages for any of the virtual machine instances with a scaling policy are computed for when these virtual machine instances are scaled out. A cost of running a virtual machine instance in the public cloud is then determined using its current resource usage as well as any additional resource usage if a scaling policy is attached to the virtual machine instance based on the cost for running virtual machine instances in a public cloud. If the cost is less than a threshold cost, then the virtual machine instance is selected to be migrated to the public cloud.

Abstract: A method, system and computer program product for selecting virtual machines to be migrated to a public cloud. The current resource usage for virtual machine instances running in the private cloud is determined. Furthermore, any scaling policies attached to the virtual machine instances running in the private cloud are obtained. Additional resource usages for any of the virtual machine instances with a scaling policy are computed for when these virtual machine instances are scaled out. A cost of running a virtual machine instance in the public cloud is then determined using its current resource usage as well as any additional resource usage if a scaling policy is attached to the virtual machine instance based on the cost for running virtual machine instances in a public cloud. If the cost is less than a threshold cost, then the virtual machine instance is selected to be migrated to the public cloud.