Abstract: An application server may be instrumented to provide a resource measurement framework to collect resource usage data regarding request processing by the application server and applications executing on the application server. The resource measurement framework of an application server may collect hardware and software resource usage data regarding request processing at interception points located at interfaces between application components and services or other components of the application server by instrumenting those interfaces. The resource measurement framework may collect resource usage by instrumenting standard interfaces and/or methods of various specifications, such as implemented by containers or other components of the application server. Thus, the resource measurement framework may collect resource usage for applications or application components that do not include any resource measuring capabilities.

Abstract: One embodiment of the present invention provides a system that supports vector operations on a multi-threaded microprocessor. During operation, the system detects a vector instruction in a program. The system maps this vector instruction onto the thread contexts of the multi-threaded microprocessor. As part of the mapping process, the system splits the vector instruction across a set of threads that execute in parallel and generates a set of instructions for the set of threads. This mapping process allows the vector instruction to be executed efficiently across multiple threads.

Abstract: One embodiment of the present invention provides a system that characterizes computer system workloads. During operation, the system collects metrics for a number of workloads of interest as the workloads of interest execute on a computer system. Next, the system uses the collected metrics to build a statistical regression model, wherein the statistical regression model uses a performance indicator as a response, and uses the metrics as predictors. The system then defines a distance metric between workloads, wherein the distance between two workloads is a function of the differences between metric values for the two workloads. Furthermore, these differences are weighted by corresponding coefficients for the metric values in the statistical regression model.

Abstract: One embodiment of the present invention provides a system that computes a distance metric between computer system workloads. During operation, the system receives a dataset containing metrics that have been collected for a number of workloads of interest. Next, the system uses splines to define bases for a regression model which uses a performance indicator y as a response and uses the metrics (represented by a vector x) as predictors. The system then fits the regression model to the dataset using a penalized least squares (PLS) criterion to obtain functions f1, . . . , fP, which are smooth univariate functions of individual metrics that add up to the regression function f, such that y=f(x)+?= ? i = 1 P ? f i ? ( x i ) + ? , wherein ? represents noise. Finally, the system uses the fitted regression function to define the distance metric.

Abstract: One embodiment of the present invention provides a system that manages an LRU list such that the rank, or position, of data records in the sequence can be determined efficiently. The system initializes an index field in each record to the record's initial rank. When a record is accessed, the system moves it to the beginning of the LRU list and appends the value of the record's index field to a “change list.” The system then sets the record's index field to zero. The change list effectively tracks the records accessed since initialization, and combined with the records' index fields can be used to efficiently compute the rank of any record in the list. This ability to efficiently compute the rank of the data record in the LRU list reduces the frequency with which the computationally-expensive initialization operation must be executed on the LRU list.