Abstract: A system, apparatus, and methods are provided for dynamically adjusting or modifying the operating frequency of a CPU or other processor component, based on a current performance of an application (or applications) executed by the CPU and a target performance, such as a performance level identified in a service level agreement (SLA) associated with the application(s). Multiple thresholds or zones of performance are defined, which may be altered during operation (e.g., based on a profile or predicted CPU workload), and each threshold/zone is associated with a frequency adjustment to implement when the application's performance triggers the threshold or zone. Illustrative adjustments include maximizing the operating frequency, minimizing it, and scaling it up or down one or more discrete levels within a range of frequencies at which the CPU can operate. Thus, CPU operation is adjusted with a primary goal of satisfying application SLAs and a secondary goal of conserving energy.

Abstract: A system, methods and apparatus are provided for determining the locus of a bottleneck in a data transfer between a data receiver (e.g., a client device) and a data sender (e.g., a computer server). The locus may be one of a receiver realm encompassing the data receiver (especially a receiver application that consumes the data), a sender realm encompassing the data sender (especially a sender application that produces the data), and a communication link realm that encompasses the communication link(s) over which the data are conveyed (and possibly network-layer protocols and lower that use the communication link(s)). A monitor entity may employ a state-machine model to represent and track progress of a given data transfer between states, using information collected from the data receiver and data sender to identify state transitions. Given a time at which a transfer was delayed or halted, the monitor outputs the locus of the problem.

Abstract: Techniques for categorizing exceptions and logs are described. For example, exception data of an exception that occurred on a machine is accessed. The exception data includes a stack trace of the exception. A determination is made that the exception is unique based on the stack track of the exception. Responsive to the determination that the exception is unique, the exception is categorized, by a machine including a memory and at least one processor, into one or more categories based on the stack trace of the exception.

Abstract: Techniques for evaluating the performance of a service or machine after a change that pertains to the service or machine are described. For example, an indication of a change that pertains to a service or machine is received. In response to the receiving of the indication of the change, using at least one computer processor, a performance of the service or machine after the change is evaluated. The evaluation may be based on a particular rule for evaluating the performance of the service or machine after the change. An evaluation result is generated based on the evaluating of the performance of the service or machine after the change. The evaluation result indicates the quality of the performance of the service or machine after the change.