Abstract: This disclosure is directed in part to testing of different versions of software or software components (software versions) and analyzing results of use (e.g., user interaction) of the different software versions. The techniques described herein provide software testing that varies the allocation to enable a ramp up of allocations to/from another software version. The allocation module may use allocation rules to assign requests to each software version based on various factors such as load balancing, user attributes, past user assignment, and/or other rules or logic. An analysis of the different software versions may include an analysis of system performance resulting from operation of each software version. An analysis may determine attributes of each user and then allocate the user to a software version based on at least some of the determined attributes.

Abstract: Computer applications may generate a large volume of different types of record data. In one example, the large volume of record data may represent millions of different processes occurring every second. Described herein are systems, methods and devices for generating parsed data based on the large volume of record data. The parsed data may be consumed by computing nodes within a designated amount of time from the generation of the record data.

Abstract: Examples of systems and methods are described for managing computing capacity by a provider of computing resources. The computing resources may include program execution capabilities, data storage or management capabilities, network bandwidth, etc. Multiple user programs can consume a single computing resource, and a single user program can consume multiple computing resources. Changes in usage and other environmental factors can require scaling of the computing resources to reduce or prevent a negative impact on performance. In some implementations, a fuzzy logic engine can be used to determine the appropriate adjustments to make to the computing resources associated with a program in order to keep a system metric within a desired operating range.

Abstract: Examples of systems and methods are described for managing computing capacity by a provider of computing resources. The computing resources may include program execution capabilities, data storage or management capabilities, network bandwidth, etc. Multiple user programs can consume a single computing resource, and a single user program can consume multiple computing resources. Changes in usage and other environmental factors can require scaling of the computing resources to reduce or prevent a negative impact on performance. In some implementations, a fuzzy logic engine can be used to determine the appropriate adjustments to make to the computing resources associated with a program in order to keep a system metric within a desired operating range.

Abstract: Systems and methods are provided for analyzing operating metrics of monitored metric sources. Aspects of the present disclosure may present for display information associated with the monitored metric source and the analysis of its operating metrics. Analysis comprises determination of reference values and tolerance levels which represent allowable deviations from the reference values. Input data includes a measurement of an operating parameter and a time stamp. Input data may be saved to a data store for using in future analysis of other input data. When input data is determined to be outside the tolerance level, notifications may be issued to alert administrators or systems of the anomaly.

Abstract: Techniques are described for performing automated predictions of program execution capacity or other capacity of computing-related hardware resources that will be used to execute software programs in the future, such as for a group of computing nodes that execute one or more programs for a user. The predictions that are performed may in at least some situations be based on historical data regarding corresponding prior actual usage of execution-related capacity (e.g., for one or more prior years), and may include long-term predictions for particular future time periods that are multiple months or years into the future. In addition, the predictions of the execution-related capacity for particular future time periods may be used in various manners, including to manage execution-related capacity at or before those future time periods, such as to prepare sufficient execution-related capacity to be available at those future time periods.

Abstract: A method is provided for estimating past data by identifying a high frequency data set for a defined time period. A pattern is calculated for the high frequency data set and then the pattern is applied to a low frequency data set in a past time period to estimate a high frequency query point.

Abstract: The techniques described herein provide software testing that may concurrently process a user request using a live version of software and a shadow request, which is based on the user request, using a shadow version of software (e.g., trial or test version, etc.). The live version of software, unlike the shadow version, is user-facing and transmits data back to the users while the shadow request does not output to the users. An allocation module may vary allocation of the shadow requests to enable a ramp up of allocations (or possibly ramp down) of the shadow version of software. The allocation module may use allocation rules to dynamically initiate the shadow request based on various factors such as load balancing, user attributes, and/or other rules or logic. Thus, not all user requests may be issued as shadow requests.

Abstract: Computer applications may generate event data based on a large volume of different types of record data. Described herein are systems, methods, and devices for enabling a computing node to implement new functions for dynamically consuming the event data. In one example, the computing node may implement a new function using an expression language, without modifying predefined hard coded functions.

Abstract: Systems and methods are provided for analyzing operating metrics of monitored metric sources. Aspects of the present disclosure may present for display information associated with the monitored metric source and the analysis of its operating metrics. Analysis comprises determination of reference values and tolerance levels which represent allowable deviations from the reference values. Input data includes a measurement of an operating parameter and a time stamp. Input data may be saved to a data store for using in future analysis of other input data. When input data is determined to be outside the tolerance level, notifications may be issued to alert administrators or systems of the anomaly.

Abstract: Examples of systems and methods are described for managing computing capacity by a provider of computing resources. The computing resources may include program execution capabilities, data storage or management capabilities, network bandwidth, etc. Multiple user programs can consume a single computing resource, and a single user program can consume multiple computing resources. Changes in usage and other environmental factors can require scaling of the computing resources to reduce or prevent a negative impact on performance. In some implementations, a fuzzy logic engine can be used to determine the appropriate adjustments to make to the computing resources associated with a program in order to keep a system metric within a desired operating range.

Abstract: Techniques are described for performing automated predictions of program execution capacity or other capacity of computing-related hardware resources that will be used to execute software programs in the future, such as for a group of computing nodes that execute one or more programs for a user. The predictions that are performed may in at least some situations be based on historical data regarding corresponding prior actual usage of execution-related capacity (e.g., for one or more prior years), and may include long-term predictions for particular future time periods that are multiple months or years into the future. In addition, the predictions of the execution-related capacity for particular future time periods may be used in various manners, including to manage execution-related capacity at or before those future time periods, such as to prepare sufficient execution-related capacity to be available at those future time periods.

Abstract: Examples of systems and methods are described for managing computing capacity by a provider of computing resources. The computing resources may include program execution capabilities, data storage or management capabilities, network bandwidth, etc. Multiple user programs can consume a single computing resource, and a single user program can consume multiple computing resources. Changes in usage and other environmental factors can require scaling of the computing resources to reduce or prevent a negative impact on performance. In some implementations, a fuzzy logic engine can be used to determine the appropriate adjustments to make to the computing resources associated with a program in order to keep a system metric within a desired operating range.

Abstract: Technologies are described herein for incorporating streaming and/or sampling in real-time log analysis. Representative samples of log data are extracted from the log files on a number of monitored hosts and streamed in real-time to log processors for processing. The log processors accumulate and process the representative samples of log data, and track a data completeness value representing an indication of a proportion of total log data represented by the representative samples received. The representative samples of log data are merged and collated. Estimated metrics are calculated from the merged and collated representative samples and the data completeness, and the estimated metrics are published to consumers in near real-time.

Abstract: Techniques are described for performing automated predictions of program execution capacity or other capacity of computing-related hardware resources that will be used to execute software programs in the future, such as for a group of computing nodes that execute one or more programs for a user. The predictions that are performed may in at least some situations be based on historical data regarding corresponding prior actual usage of execution-related capacity (e.g., for one or more prior years), and may include long-term predictions for particular future time periods that are multiple months or years into the future. In addition, the predictions of the execution-related capacity for particular future time periods may be used in various manners, including to manage execution-related capacity at or before those future time periods, such as to prepare sufficient execution-related capacity to be available at those future time periods.

Abstract: Techniques are described for managing program execution capacity or other capacity of computing-related hardware resources used to execute software programs, such as for a group of computing nodes that is in use executing one or more programs for a user. Dynamic modifications to the program execution capacity of the group may include adding or removing computing nodes, such as in response to automated determinations that previously specified triggers are currently satisfied, and may be automatically governed at particular times based on automatically generated predictions of program execution capacity that will be used at those times by the group, such as to verify that requested dynamic execution capacity modifications at a time are within the predicted execution capacity values for that time. In some situations, the techniques are used in conjunction with a fee-based program execution service that executes multiple programs on behalf of multiple users of the service.

Abstract: Techniques are described for performing automated predictions of program execution capacity or other capacity of computing-related hardware resources that will be used to execute software programs in the future, such as for a group of computing nodes that execute one or more programs for a user. The predictions that are performed may in at least some situations be based on historical data regarding corresponding prior actual usage of execution-related capacity (e.g., for one or more prior years), and may include long-term predictions for particular future time periods that are multiple months or years into the future. In addition, the predictions of the execution-related capacity for particular future time periods may be used in various manners, including to manage execution-related capacity at or before those future time periods, such as to prepare sufficient execution-related capacity to be available at those future time periods.

Abstract: Techniques are described for managing program execution capacity or other capacity of computing-related hardware resources used to execute software programs, such as for a group of computing nodes that is in use executing one or more programs for a user. Dynamic modifications to the program execution capacity of the group may include adding or removing computing nodes, such as in response to automated determinations that previously specified triggers are currently satisfied, and may be automatically governed at particular times based on automatically generated predictions of program execution capacity that will be used at those times by the group, such as to verify that requested dynamic execution capacity modifications at a time are within the predicted execution capacity values for that time. In some situations, the techniques are used in conjunction with a fee-based program execution service that executes multiple programs on behalf of multiple users of the service.

Abstract: An improved system and method of data partitioning for parallel processing of dynamically generated application data is provided. An application may send a request to partition the application data specified by a data partitioning policy and to process each of the data partitions according to processing instructions. The data partitioning policy may be flexibly defined by an application for partitioning data any number of ways, including balancing the data volume across each of the partitions or partitioning the data by data type. Asynchronous data partition processors may be instantiated to perform parallel processing of the partitioned data. The data may be partitioned according to the data partitioning policy and processed according to the processing instructions. And the results may be returned to the application.

Abstract: A real-time click system to process advertisement (ad) clicks includes a real-time listener operative to listen for, and store in a memory, a plurality of click event packets emitted by an ad server when corresponding ads are clicked by web users. A sequencer stores the click event packets in a database. A collector is coupled with the real-time listener and the sequencer and is operative to retrieve the plurality of click event packets, upon request, from the memory at a predetermined time interval. The collector also determines a partition number associated with each of the plurality of click event packets and sends to the sequencer the click event packets having a partition number corresponding to the sequencer.