Abstract: Performance analysis for test automation frameworks is described. A test is executed of an application. Performance indicators are logged during execution of the test, wherein the performance indicators include a first performance indicator. A correlation is identified between the first performance indicator and a second performance indicator recorded during the execution of the test or an execution of a previous test of the application. An output device outputs the correlation as a performance issue.

Abstract: In one example, a method for determining an optimized data backup throughput rate is provided. The example method includes calculating a total saveset size DS(total) for a backup by summing sizes DS(n) of each of ‘n’ savesets, each of the savesets being associated with a respective savestream, and then calculating a total throughput rate PTP(total) for the backup by summing respective throughput rates TP of each of the ‘n’ savesets. The method further includes calculating a total backup window length PTIME(total) using the following formula: PTIME(total)=DS(total)/PTP (total). Finally, an optimized savestream throughput rate OTP(n) is calculated for each of the ‘n’ savesets using the following formula: OTP(n)=DS(n)/PTIME(total).

Abstract: Concurrently backing up data streams based on backup time estimates is described. A system identifies multiple data streams scheduled for backup. The system identifies multiple backup time estimates corresponding to the multiple data streams. The system creates a ranked relational sequence based on the multiple backup time estimates. The system concurrently backs up the multiple data streams based on a concurrency limit and the ranked relational sequence.

Abstract: Concurrently backing up data streams of multiple computers based on backup time estimates is described. A system queries multiple computers to identify multiple data streams scheduled for backup. The system queries the multiple computers to identify multiple backup time estimates corresponding to the multiple data streams. The system creates a ranked relational sequence based on the multiple backup time estimates. The system instructs the multiple computers to provide the multiple data streams based on the ranked relational sequence. The system concurrently backs up the multiple data streams based on a concurrency limit and the ranked relational sequence.

Abstract: This disclosure relates to a method, article of manufacture, and apparatus of adaptive policy generating for storage system performance optimization. In some embodiments, this includes inspecting a storage system to obtain resources information, wherein the resources information includes attributes associated with a workload of the storage system and corresponding values, wherein the storage system includes an application configured to run a plurality of processes concurrently in an operating system producing a portion of the workload, obtaining one or more percentages, wherein the one or more percentages specifies an optimum proportion of the resources allocated to the application, obtaining amounts of the resources allocated to a process within the plurality of processes, and generating policies as a function of the resources information, the one or more percentages, and the amounts of the resources allocated to the process.

Abstract: A system and method for controlling a backup of a plurality of computing devices to a distinct storage is provided. The method comprises: determining, based at least on a write speed parameter and backup data parameter of each computing device, whether a throughput and/or a speed of a backup of computer devices can be improved; calculating, in response to a positive result of the determining, new write speeds for the individual computing devices; and sending the new write speeds to at least the corresponding ones of the computing devices. The new write speeds of individual ones of the plurality of computers are balanced such that each computing device completes backup at substantially the same time.

Abstract: This disclosure relates to a method, article of manufacture, and apparatus of adaptive storage system management for optimizing performance. In some embodiments, this includes polling attributes associated with a workload of a storage system to obtain corresponding values at a time instance, analyzing the values and the attributes to determine a need for modifying a set of the values corresponding to a set of the attributes when the workload is not at an optimized level, modifying the set of the values in response to the determination, and storing the time instance, the attributes, the values, and the modification.