Abstract: A computer-implemented method for placement of a plurality of application objects of an application within a network architecture is disclosed. The method includes generating during runtime of the application, an application topology model for the application, based on application metrics for the plurality of application objects. A resource topology model of a plurality of network nodes within the network architecture is generated based on resource metrics for the network nodes. A recommendation is generated for migrating an application object of the plurality of application objects to a network node of the plurality of network nodes using the application topology model and the resource topology model, the recommendation identifying the application object and the network node. The application object is migrated to the network node identified by the recommendation.

Abstract: Consistency metadata, including a parameter for a pseudo-random number source, are determined for training-and-evaluation iterations of a machine learning model. Using the metadata, a first training set comprising records of at least a first chunk is identified from a plurality of chunks of a data set. The first training set is used to train a machine learning model during a first training-and-evaluation iteration. A first test set comprising records of at least a second chunk is identified using the metadata, and is used to evaluate the model during the first training-and-evaluation iteration.

Abstract: Consistency metadata, including a parameter for a pseudo-random number source, are determined for training-and-evaluation iterations of a machine learning model. Using the metadata, a first training set comprising records of at least a first chunk is identified from a plurality of chunks of a data set. The first training set is used to train a machine learning model during a first training-and-evaluation iteration. A first test set comprising records of at least a second chunk is identified using the metadata, and is used to evaluate the model during the first training-and-evaluation iteration.

Abstract: Pattern based detection of data usage is facilitated using data injection. Data values are injected in one or more storage locations accessible to a plurality of services or included in service requests. Service interactions among the services are compared to a set of patterns. The set of patterns are configured to match the data values. By comparing the service interactions to the patterns, one or more of the service interactions are determined to include individual ones of the data values. Data are generated indicating a presence of the data values in the services.

Abstract: A capacity expansion method includes obtaining a measured workload of a service of an application, obtaining an application model of the application, and obtaining a measured workload of each upper-level service of the service; determining a predicted workload of the service based on the measured workload of the service, determining the measured workload of each upper-level service of the first service, and determining a first workload ratio corresponding to a first calling relationship; and determining a predicted workload of each lower-level service based on the predicted workload of the service and determining a second workload ratio corresponding to a second calling relationship.

Abstract: A record extraction request for a data set is received at a machine learning service. A plan to perform one or more chunk-level operations (such as sampling, shuffling, splitting or partitioning for parallel computation) on chunks of the data set is generated. A set of data transfers that results in a particular chunk being stored in a particular server's memory is initiated to implement the first chunk-level operation of the sequence. A second operation such as another filtering operation or a feature processing operation is performed on a result set of the first chunk-level operation.

Abstract: A computer-implemented method for placement of a plurality of application objects of an application within a network architecture is disclosed. The method includes generating during runtime of the application, an application topology model for the application, based on application metrics for the plurality of application objects. A resource topology model of a plurality of network nodes within the network architecture is generated based on resource metrics for the network nodes. A recommendation is generated for migrating an application object of the plurality of application objects to a network node of the plurality of network nodes using the application topology model and the resource topology model, the recommendation identifying the application object and the network node. The application object is migrated to the network node identified by the recommendation.

Abstract: Pattern based detection of data usage is facilitated using data injection. Data values are injected in one or more storage locations accessible to a plurality of services or included in service requests. Service interactions among the services are compared to a set of patterns. The set of patterns are configured to match the data values. By comparing the service interactions to the patterns, one or more of the service interactions are determined to include individual ones of the data values. Data are generated indicating a presence of the data values in the services.

Abstract: Consistency metadata, including a parameter for a pseudo-random number source, are determined for training-and-evaluation iterations of a machine learning model. Using the metadata, a first training set comprising records of at least a first chunk is identified from a plurality of chunks of a data set. The first training set is used to train a machine learning model during a first training-and-evaluation iteration. A first test set comprising records of at least a second chunk is identified using the metadata, and is used to evaluate the model during the first training-and-evaluation iteration.

Abstract: Consistency metadata, including a parameter for a pseudo-random number source, are determined for training-and-evaluation iterations of a machine learning model. Using the metadata, a first training set comprising records of at least a first chunk is identified from a plurality of chunks of a data set. The first training set is used to train a machine learning model during a first training-and-evaluation iteration. A first test set comprising records of at least a second chunk is identified using the metadata, and is used to evaluate the model during the first training-and-evaluation iteration.

Abstract: Pattern based detection of data usage is facilitated using data injection. Data values are injected in one or more storage locations accessible to a plurality of services or included in service requests. Service interactions among the services are compared to a set of patterns. The set of patterns are configured to match the data values. By comparing the service interactions to the patterns, one or more of the service interactions are determined to include individual ones of the data values. Data are generated indicating a presence of the data values in the services.

Abstract: Embodiments of this application relate to a capacity expansion method. In this method, a measured workload of a service of an application and an application model of the application a measured workload of each upper-level service of the service is obtained. Then, a predicted workload of the service based on the measured workload of the service, the measured workload of each upper-level service of the first service, and a first workload ratio corresponding to a first calling relationship are determined. Then, a predicted workload of each lower-level service is determined based on the predicted workload of the service and a second workload ratio corresponding to a second calling relationship.

Abstract: Methods, systems, and computer-readable media for implementing pattern-based detection are disclosed. A plurality of services monitor a plurality of service interactions comprising data or metadata. The services compare the data or metadata to a set of patterns and identify one or more matched patterns among the set of patterns. The services send data indicative of the matched patterns to a central recording service. The central recording service aggregates the data indicative of the matched patterns and generates one or more data flow visualizations indicating one or more data flows between individual ones of the services for the matched patterns.

Abstract: Consistency metadata, including a parameter for a pseudo-random number source, are determined for training-and-evaluation iterations of a machine learning model. Using the metadata, a first training set comprising records of at least a first chunk is identified from a plurality of chunks of a data set. The first training set is used to train a machine learning model during a first training-and-evaluation iteration. A first test set comprising records of at least a second chunk is identified using the metadata, and is used to evaluate the model during the first training-and-evaluation iteration.

Abstract: A record extraction request for a data set is received at a machine learning service. A plan to perform one or more chunk-level operations (such as sampling, shuffling, splitting or partitioning for parallel computation) on chunks of the data set is generated. A set of data transfers that results in a particular chunk being stored in a particular server's memory is initiated to implement the first chunk-level operation of the sequence. A second operation such as another filtering operation or a feature processing operation is performed on a result set of the first chunk-level operation.