Bmc Software, Inc.

Riverbed Technology, Inc.

Opnet Technologies, Inc.

Yiping Ding

Fred Johannessen

Assaf Marron

Kenneth W Newman

David Carter

Shankar Ananthanarayanan

Pierre Fiorini

Alain J Cohen

The method may include collecting performance data relating to processing nodes of a computer system which provide services via one or more applications, analyzing the performance data to generate an operational profile characterizing resource usage of the processing nodes, receiving a set of attributes characterizing expected performance goals in which the services are expected to be provided, and generating at least one provisioning policy based on an analysis of the operational profile in conjunction with the set of attributes. The at least one provisioning policy may specify a condition for re-allocating resources associated with at least one processing node in a manner that satisfies the performance goals of the set of attributes. The method may further include re-allocating, during runtime, the resources associated with the at least one processing node when the condition of the at least one provisioning policy is determined as satisfied.

Automated capacity provisioning method using historical performance data

An interactive virtualization management system provides an assessment of proposed or existing virtualization schemes. A Virtual Technology Overhead Profile (VTOP) is created for each of a variety of configurations of host computer systems and virtualization technologies by measuring the overhead experienced under a variety of conditions. The multi-variate overhead profile corresponding to each target configuration being evaluated is used by the virtualization management system to determine the overhead that is to be expected on the target system, based on the particular set of conditions at the target system. Based on these overhead estimates, and the parameters of the jobs assigned to each virtual machine on each target system, the resultant overall performance of the target system for meeting the performance criteria of each of the jobs in each virtual machine is determined, and over-committed virtual machines and computer systems are identified.

Virtualization planning system

The present invention relates to a system and method for assessing application performance and user satisfaction. In one embodiment, the system and method calculates an Operational Index (OPdex) representing user satisfaction with an application. The OPdex may be a number quantifying user satisfaction with an application, such as a web application, and system performance. The OPdex may be based on one or more measurable metrics having a range of values that may affect user satisfaction or performance of an application. The OPdex may comprise calculating the index based on a soft threshold, a hard threshold, and measurements indicating a perceived application response time. The OPdex calculation may also account for sensitivity of user satisfaction to response time. Based on the OPdex, the system and methods also provide information indicating the relationship among application response time thresholds set by the users, the user satisfaction level, and the mean response time.

Assessing application performance with an operational index

The embodiments related to systems and methods for virtualization planning. A set of target machines may employ one or more virtualization technologies to divide resources of the given target computer system into multiple execution environments for virtual machines. Overhead profiles are determined based on a configuration of a given target computer system, the virtualization technology, and work performed by the virtual machines. The overhead consumed by the virtualization technologies is estimated for the proposed allocation of virtual machines. Performance of the proposed allocation of virtual machines is then modeled and various performance measures are provided.

Virtualization planning system that models performance of virtual machines allocated on computer systems

An automated system obtains performance data of a computer system having partitioned servers. The performance data includes a performance rating and a current measured utilization of each server, actual workload (e.g. transaction arrival rate), and actual service levels (e.g. response time or transaction processing rate). From the data, automated system normalizes a utilization value for each server over time and generates a weighted average for each and expected service levels for various times and workloads. Automated system receives a service level objective (SLO) for each server and future time and automatically determines a policy based on the weighted average normalized utilization values, past performance information, and received SLOs. The policy can include rules for provisioning required servers to meet the SLOs, a throughput for each server, and a potential service level for each server. Based on the generated policy, the system automatically provisions operation of the servers across partitions.

Modeling overhead for a plurality of virtualization technologies in a computer system

A method using statistical parameters (e.g. mean, standard deviation, exceptional values) of performance monitoring metrics to substantially reduce the quantity of performance monitoring data collected and reported, make system performance monitoring scalable and enhance the readability of the system performance display. The number of metrics monitored may be reduced by monitoring only one of any two metrics that are closely correlated.

System and method for statistical performance monitoring

A system and method for automatic workload characterization are provided. Transactions performed in a computer system may be logged. The log of transactions comprises a timestamp for each transaction. Resource usage in the computer system may be logged. The log of resource usage comprises one or more periods of time during which each of a plurality of resources is used, and the log of resource usage comprises a plurality of system performance metrics which reflect resource consumption by one or more processes that performed the transactions. The timestamps in the log of transactions may be compared to the periods of time in the log of resource usage. It may be determined which transactions used which resources as a result of the comparing the timestamps in the log of transactions to the periods of time in the log of resource usage. One or more workloads may be determined using the determining which transactions used which resources. Heuristics may be used to group processes into workloads.

System and method for automatic workload characterization

A system and method for estimating statistics concerning system metrics to provide for the accurate and efficient monitoring of one or more computer systems. The system preferably comprises a distributed computing environment, i.e., an enterprise, which comprises a plurality of interconnected computer systems. At least one of the computer systems is an agent computer system which includes agent software and/or system software for the collection of data relating to one or more metrics, i.e., measurements of system resources. Metric data is continually collected over the course of a measurement interval, regularly placed into a registry of metrics, and then periodically sampled from the registry indirectly. Sampling-related uncertainty and inaccuracy arise from two primary sources: the unsampled residual segments of seen (i.e., sampled and therefore known) events, and unseen (i.e., unsampled and therefore unknown) events. The total unsampled utilization and the total unseen utilization are accurately estimated according to the properties of one or more process service time distributions. The total unseen utilization is also estimated with an iterative method using gradations of the sample interval. The length distribution of the unseen processes is determined with the same iterative method.

Enterprise management system and method which includes statistical recreation of system resource usage for more accurate monitoring, prediction, and performance workload characterization

Analytic tests are used to detect chaotic (power-tail) behavior in one or more computer system resources in a distributed computing environment. The analytic tests are used to determine if data (indicative of one or more parameters related to computer system resources) exhibit large deviations from a mean, a high variance and other properties consistent with large values in the tail portion of a power-tail distribution. The tests can be performed in any order, and fewer than three can be performed. If all three tests indicate the existence of power-tail behavior, chaotic behavior of the data is likely. If all three tests indicate the lack of power-tail behavior, chaotic behavior of the data is unlikely. If the results are mixed, then more data or analysis may be needed. The results may be used for modeling and/or altering the configuration of the distributed computing environment.

Enterprise management system and method which indicates chaotic behavior in system resource usage for more accurate modeling and prediction

A set of algorithms for detecting a renewal power-tail behavior which often relates to chaotic system activities in one or more computer system resources of a distributed computing environment, i.e., an enterprise. Analysis and/or prediction software receives a set of metric data points from agent software on one or more computer systems. The analysis and/or prediction software performs three analytic tests relating to distinctive properties of power-tail distributions: a first test to determine whether the set of data points exhibits large deviations from the mean, a second test to determine whether the set of data points exhibits a high variance, and a third test to determine whether the set of the largest data points exhibits properties consistent with large values in a tail portion of a power-tail distribution. The tests can be performed in any order, and in other embodiments, fewer than three can be performed. Each test has two possible results: successful if the test indicates a likelihood of power-tail behavior, or unsuccessful if it indicates that power-tail behavior is unlikely. The results of the three tests are combined to determine the overall likelihood of a power-tail distribution. If all three tests are successful, then power-tail behavior is likely. If all three tests are unsuccessful, then power-tail behavior is unlikely. If the results are mixed, then typically more data or analysis is needed. The results are used for modeling and/or altering the configuration of the enterprise.

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Dover, MA, United States of America

Yiping Ding