Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A set of algorithms for detecting a renewal power-tall 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 tall portion of a power-tall distribution. The tests can be performed in any order, and in other embodiments, fewer than three can be performed.

Abstract: 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.

Abstract: 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.