Ca, Inc.

Other

Hyperformix, Inc.

Hyperformit, Inc.

Douglas Neuse

Kenneth Zink

Allan Drew Clarke

Laurence E Clay

Paul David Peterson

Douglas K Matchett

Christopher Brian Walton

Neal Tibrewala

Chris Walton

Serguei Mankovskii

Ramanjaneyulu Malisetti

Rajasekhar Gogula

Subhasis Khatua

Steven M Oberlin

Balaji Varadaraju

Theodore Lehr

Methods of analyzing and capacity planning for multi-core, multi-chip, multi-threaded computer system environments by analyzing the scalability of a fourth layer complexity, the processor boards, and incorporating this factor into the calculation of the expected throughput of a system constructed with multiple processor boards. In particular, the method may comprise identifying a system for which system performance prediction is desired, specifying a simulation model, and determining configuration parameters for the system, the system with at least one processor board, at least one chip per board, at least one core per chip, and at least one thread per core. The method may further comprise obtaining scalability factors based on the configuration data for the system, executing a simulation process for the simulation model for a deterministic simulation time, calculating a throughput of the system as a prediction of the performance of the system, and storing the results in a storage device.

Capacity planning for systems with multiprocessor boards

Data processing workloads are selectively assigned within a data center and/or among data centers based on non-data processing overhead within the data center and/or among the data centers. Power consumption of a rack including servers is predicted based on data processing demands that are placed on the servers for a given data processing workload, and power consumed by the rack is measured when the servers are performing the given data processing workload. A metric of power consumed by the rack for non-data processing overhead is derived based on a difference between results of the predicting and the measuring. A future data processing workload is selectively assigned to the rack based on the metric of power of power consumed by the rack for the non-data processing overhead. Assignment may also take place at an aisle and/or data center level based on these metrics.

Assigning data processing workloads in data centers based on non-data processing overhead

In accordance with the teachings of the present disclosure, a method of assessment of hosting suitability of multiple applications in a cloud is disclosed. The method may include defining, in a first synthetic application definition, a first plurality of resource consumptions, wherein the first plurality of resource consumptions are equivalent to consumptions by a first candidate application, and defining, in a second synthetic application definition, a second plurality of resource consumptions, wherein the second plurality of resource consumptions are equivalent to consumptions by a second candidate application. The method may also include consuming quantities of resources of pluralities of nodes of the computing system, recording performances of the synthetic, and evaluating the computing system based upon the recorded performances. The present disclosure additionally includes associated systems and apparatuses.

Assessment of hosting suitability of multiple applications in a cloud

In accordance with the teachings of the present disclosure, a method of assessment of cloud hosting suitability for multiple applications is disclosed. The method may include creating a second synthetic application definition based on a first synthetic application definition, wherein creating the second application definition comprises defining, in the second synthetic application definition, a second plurality of resource consumptions, wherein the second plurality of resource consumptions are equivalent to consumptions by the candidate application at a second level of user demand. The method may also include consuming, based on the second synthetic application definition, a plurality of quantities of resources of the computing system and evaluating the computing system. The present disclosure additionally includes associated systems and apparatuses.

Assessment of cloud hosting suitability for multiple applications

In accordance with the teachings of the present disclosure, a method of assessment of service level agreement compliance is disclosed. The method may include defining, in a synthetic application definition, a plurality of resource consumptions, wherein the plurality of resource consumptions are equivalent to consumptions by a candidate application, and distributing the synthetic application definition to a synthetic application in a node of a computing system. The method may further include consuming, with the synthetic application and based on the synthetic application definition, a plurality of quantities of resources of a plurality of nodes of the computing system. The method may additionally include recording a performance of the synthetic application, and comparing the recorded performance to a requirement of a service level agreement (SLA). The present disclosure additionally includes associated systems and apparatuses.

Assessment of service level agreement compliance

In accordance with the teachings of the present disclosure, a method of application-specific assessment of cloud hosting suitability is disclosed. The method includes distributing synthetic applications to nodes of the computer system. The method further includes consuming quantities of resources of nodes based upon a synthetic application definition. The method additionally includes measuring a performance characteristic of the synthetic applications, and evaluating performance of the synthetic applications. The present disclosure also includes associated systems and apparatuses.

Application-specific assessment of cloud hosting suitability

In accordance with the teachings of the present disclosure, a method of loading a definition of a synthetic application is disclosed. The method includes identifying a synthetic application definition for use by synthetic applications in nodes in a computing system. The method further includes identifying sets of node properties for the synthetic applications. The sets of node properties specify consumption of quantities of resources in the nodes. The sets of node properties are to cause the synthetic applications to consume resources in the computing system equivalent to consumption by a candidate application.

Definition of a multi-node synthetic application for assessment of cloud-hosting suitability

In accordance with the teachings of the present disclosure, a method of performing application-specific assessment of hosting suitability of multiple clouds is disclosed. The method may include defining, in a synthetic application definition, a plurality of resource consumptions, wherein the plurality of resource consumptions are equivalent to consumptions by a candidate application. The method may further include consuming a first plurality of quantities of resources of a plurality of nodes of a first computing system and consuming a second plurality of quantities of resources of a plurality of nodes of a second computing system. The method may also include recording a performance of the first and second synthetic application, and comparing the first computing system and the second computing system based upon the first performance and the second performance. The present disclosure additionally includes associated systems and apparatuses.

Application-specific assessment of hosting suitability of multiple clouds

A method includes receiving a request for access to an instance of a computing resource, the request having a performance standard associated therewith, requesting the instance of the computing resource from a computing resource provider, evaluating a performance of the instance of the computing resource provided by the computing resource provider to determine if the performance of the instance of the computing resource complies with the performance standard associated with the request, and providing access to the instance of the computing resource provided by the computing resource provider responsive to a determination that the performance of the instance of the computing resource complies with the performance standard associated with the request. Related systems and computer program products are also disclosed.

Methods, systems, and computer program products for user side optimization of acquisition of virtualized resources

A method includes receiving, at a load balancer for a plurality of computing components, a workload request. The method also includes accessing a database including a respective non-linear scalability model for each of the plurality of computing components. The method further includes determining a particular computing component from the plurality of computing components based on each respective non-linear scalability model; and scheduling the workload request for execution by the particular computing component.

Load balancing using improved component capacity estimation

A method for expressing a hierarchy of scalabilities in complex systems, including a discrete event simulation and an analytic model, for analysis and prediction of the performance of multi-chip, multi-core, multi-threaded computer processors is provided. Further provided is a capacity planning tool for migrating data center systems from a source configuration which may include source systems with multithreaded, multicore, multichip central processing units to a destination configuration which may include destination systems with multithreaded, multicore and multichip central processing units, wherein the destination systems may be different than the source systems. Apparatus and methods are taught for the assembling of and utilization of linear and exponential scalability factors in the capacity planning tool when a plurality of active processor threads populate processors with multiple chips, multiple cores per chip and multiple threads per core.

System and method for capacity planning for systems with multithreaded multicore multiprocessor resources

A system and method for automatically reconfiguring a computing environment comprises a consumption analysis server, a placement server, a deployment server in communication with a set of virtual machine monitors and a data warehouse in communication with a set of data collection agents, and a database. The consumption analysis server operates on measured resource utilization data in the data warehouse to yield a set of resource consumptions, available capacities and host and virtual machine configurations from the computing environment. The deployment server continuously monitors an event triggering condition and when the triggering condition is met, the placement server assigns a set of target virtual machines to a target set of hosts in a new placement and the deployment server implements the new placement through communication with the set of virtual machine monitors. The placement server right-sizes the virtual machines and the target set of hosts.

System and method for continuous optimization of computing systems with automated assignment of virtual machines and physical machines to hosts

A power utilization index is determined for a server that defines an amount of energy that is consumed by the server for a unit of workload performed by the server. Future power usage by the server may then be predicted based on the power utilization index and a projected workload demand on the server. Moreover, workload for the server may be selectively assigned in response to the predicting. The power utilization index may be determined by a obtaining measurements of power consumed by the server in response to various workloads. Measurements of workload demands placed on the server are also obtained for the workloads. The measurements of workload demands may separately account for demands placed upon a processor subsystem of the server, a memory subsystem of the server, a network communication subsystem of the server and a storage subsystem of the server, for the various workloads.

Determining and using power utilization indexes for servers

A server migration tool used to construct data center migration scenarios allowing for a user to rapidly manipulate a large number of input parameters required to describe a transformation from one data center configuration to a new data center configuration. The tool then performs the transformation and allows the user to interact with new data center configuration to understand its performance. A novel parameterization, speed independent service demand (SISD), greatly facilitates scaling performance metrics between different hardware platforms.

Apparatus and method for capacity planning for data center server consolidation and workload reassignment

A method includes determining historical resource metrics for a host, and determining a workload forecast for the host based on the historical resource metrics. The method also includes determining a first series of virtual resource configurations based on the workload forecast. Each virtual resource configuration corresponds to a respective virtual machine of a plurality of virtual machines from the host. Each virtual resource configuration includes a time interval of the workload forecast, a processing specification of the corresponding virtual machine, and a host assignment indicative of a corresponding target host of the plurality of hosts on which to run the corresponding virtual machine. The method further includes configuring each respective virtual machine according to each corresponding virtual resource configuration in the first series of virtual resource configurations.

Growing community of inventors

Austin, TX, United States of America

Douglas Neuse