International Business Machines Corporation

Cloud Raxak, Inc.

Globalfoundries Inc.

Seshashayee Sankarshana Murthy

Sandeep Madhav Uttamchandani

Kavita Chavda

Sandeep Gopisetty

Aameek Singh

Pin Zhou

Mickey Iqbal

Ramani Ranjan Routray

Nagapramod S Mandagere

Brian Cho

Frederick Yung-Fung Wu

Sandip Agarwala

David P Goodman

David D Weck

Richard Ayala

Prasanna Mulgaonkar

Richard J Ayala, Jr

John Nathan Rachlin

Sandeep M Uttamachandani

Christopher J Dawson

Manish Satyapal Gupta

Eric Kevin Butler

Rajesh Radhakrishnan

Mark Davis Smith

Madhukar R Korupolu

Gabriel Alatorre

Brenda Lynn Dietrich

James T Yeh

Rama K Akkiraju

Martin Clarence Sturzenbecker

Rama Kalyani Tirumala Akkiraju

Dushmanta Mohapatra

Rainer Kerth

Robin Lougee-Heimer

Raymond T Henry

Robert Mack Fuhrer

Umeshchandra H Patel

Meenakshisundaram Venkatasubramaniam

Pinar Keskinocak

Madhuri Chawla

Systems and methods for compute resource configuration, verification, and remediation are provided herein. An example method includes verifying compliance of an operating system and compute assets provisioned configured within a middleware of a computing device using a pre-defined configuration profile, the compliance being determined by comparison of run-time hardware and software attributes of the compute assets to the pre-defined configuration profile comprising hardware and software requirements for the client of a blockchain implementation.

Trust in block-chain implementations using compute resource configuration and verification systems and methods

Systems and methods for compute resource configuration, verification, and remediation are provided herein. An example method includes verifying compliance of an operating system and compute assets provisioned configured within a middleware of a computing device using a pre-defined configuration profile, the compliance being determined by comparison of run-time hardware and software attributes of the compute assets to the pre-defined configuration profile comprising hardware and software requirements for the client.

Compute resource configuration and verification systems and methods

An approach is provided for determining availabilities of servers in multiple tiers of a workload. Based on (1) a required availability of the workload, (2) resource requirements for redundancy groups (RGs) in the workload, (3) sets of server sizes, and (4) sets of availability categories, numbers of server(s) included in respective RGs are determined, allocations to the server(s) of one or more server sizes from a selected set of server sizes are determined, and allocations to the server(s) of one or more categories of availability from a selected set of categories is determined, so that a cost of achieving the required availability of the workload is minimized.

Determining server level availability and resource allocations based on workload level availability requirements

The invention provides a method and system for continuous optimization of a data center. The method includes monitoring loads of storage modules, server modules and switch modules in the data center, detecting an overload condition upon a load exceeding a load threshold, combining server and storage virtualization to address storage overloads by planning allocation migration between the storage modules, to address server overloads by planning allocation migration between the server modules, to address switch overloads by planning allocation migration mix between server modules and storage modules for overload reduction, and orchestrating the planned allocation migration to reduce the overload condition in the data center.

Method and system for continuous optimization of data centers by combining server and storage virtualization

A method and system for optimizing power consumption of a data center by dynamic workload adjustment. Workload of the data center is dynamically adjusted from a current workload distribution to an optimal workload solution. The optimal workload solution is a candidate workload solution of at least one candidate workload solution having a lowest sum of a respective power cost and a respective migration cost. Each candidate workload solution represents a respective application map that specifies a respective workload distribution among application programs of the data center. Dynamically adjusting the workload of the data center includes: estimating a respective overall cost of each candidate workload solution, selecting the optimal workload solution that has a lowest overall cost as determined from the estimating, and transferring the optimal workload solution to devices of a computer system for deployment.

Optimizing power consumption by dynamic workload adjustment

A method and associated system for continuously optimizing data archive management scheduling. A flow network is modeled, which creates vertexes organized in multiple levels and creating multiple edges sequentially connecting the vertexes of the multiple levels. The multiple levels consist of N+1 levels denoted as LEVEL, LEVEL, . . . , LEVEL, wherein N is at least 2, wherein LEVELincludes a source vertex, wherein LEVELincludes at least one task vertex associated with at least one archive management task, wherein EDGEis directed from the vertex of LEVELto the vertex of LEVELfor K=1, . . . , N, wherein EDGEindicates that a first task associated with a first task vertex of LEVELis scheduled by a job scheduler, and wherein EDGEhas a first weight equal to a time unit to perform the first task.

Continuous optimization of archive management scheduling by use of integrated content-resource analytic model

The present invention proactively identifies hotspots in a cloud computing environment through cloud resource usage models that use workload parameters as inputs. In some embodiments the cloud resource usage models are based upon performance data from cloud resources and time series based workload trend models. Hotspots may occur and can be detected at any layer of the cloud computing environment, including the server, storage, and network level. In a typical embodiment, parameters for a workload are identified in the cloud computing environment and inputted into a cloud resource usage model. The model is run with the inputted workload parameters to identify potential hotspots, and resources are then provisioned for the workload so as to avoid these hotspots.

Proactive identification of hotspots in a cloud computing environment

Embodiments of the present invention provide an approach for adapting an information extraction middleware for a clustered computing environment (e.g., a cloud environment) by creating and managing a set of statistical models generated from performance statistics of operating devices within the clustered computing environment. This approach takes into account the required accuracy in modeling, including computation cost of modeling, to pick the best modeling solution at a given point in time. When higher accuracy is desired (e.g., nearing workload saturation), the approach adapts to use an appropriate modeling algorithm. Adapting statistical models to the data characteristics ensures optimal accuracy with minimal computation time and resources for modeling. This approach provides intelligent selective refinement of models using accuracy-based and operating probability-based triggers to optimize the clustered computing environment, i.e., maximize accuracy and minimize computation time.

Optimizing storage cloud environments through adaptive statistical modeling

A method is implemented in a computer infrastructure having computer executable code tangibly embodied on a computer readable storage medium having programming instructions. The programming instructions are operable to determine one or more optimal mappings between a server layer and a storage layer through a network layer based on performance metrics of one or more ports of at least one of the server layer, the storage layer and the network layer.

System and method for a storage area network virtualization optimization

A method and system for optimizing power consumption of a data center by dynamic workload adjustment. At least one candidate workload solution for the data center is generated. Each candidate workload solution represents a respective application map that specifies a respective workload distribution among application programs of the data center. Workload of the data center is dynamically adjusted from a current workload distribution to an optimal workload solution. The optimal workload solution is a candidate workload solution of the at least one candidate workload solution having a lowest sum of a respective power cost and a respective migration cost. Dynamically adjusting the workload of the data center includes: estimating a respective overall cost of each candidate workload solution, selecting the optimal workload solution that has a lowest overall cost as determined from the estimating, and transferring the optimal workload solution to devices of a computer system for deployment.

Embodiments of the present invention provide performance isolation for storage clouds. Under one embodiment, workloads across a storage cloud architecture are grouped into clusters based on administrator or system input. A performance isolation domain is then created for each of the clusters, with each of the performance isolation domains comprising a set of data stores associated with a set of storage subsystems and a set of data paths that connect the set of data stores to a set of clients. Thereafter, performance isolation is provided among a set of layers of the performance isolation domains.

Performance isolation for storage clouds

In general, embodiments of present invention provide an approach for calibrating a cloud computing environment. Specifically, embodiments of the present invention provide an empirical approach for obtaining end-to-end performance characteristics for workloads in the cloud computing environment (hereinafter the 'environment'). In a typical embodiment, different combinations of cloud server(s) and cloud storage unit(s) are determined. Then, a virtual machine is deployed to one or more of the servers within the cloud computing environment. The virtual machine is used to generate a desired workload on a set of servers within the environment. Thereafter, performance measurements for each of the different combinations under the desired workload will be taken. Among other things, the performance measurements indicate a connection quality between the set of servers and the set of storage units, and are used in calibrating the cloud computing environment to determine future workload placement. Along these lines, the performance measurements can be populated into a table or the like, and a dynamic map of a data center having the set of storage units can be generated.

Calibrating cloud computing environments

A method, system and program product for transmitting configuration item data by a network object within a network. The steps include the network object detecting a change; the network object detecting an amount of traffic within the network; the network object transmitting configuration item data to the network at a level based on the amount of traffic present within the network. If all of the configuration item data was not sent and the network object detects that the amount of network traffic within the network is below a determined amount, the network object transmits remaining configuration item data to the network; and if all configuration item data was sent, returns to the step of the network object detecting a change.

Transmitting of configuration items within a network

A method and associated system for continuously optimizing data archive management scheduling. A flow network is modeled. The flow network represents data content, software programs, physical devices, and communication capacity of the archive management system in various levels of vertices such that an optimal path in the flow network from a task of at least one archive management task to a worker program of the archive management system represents an optimal initial schedule for the worker program to perform the task.

Growing community of inventors

Somers, NY, United States of America

Seshashayee Sankarshana Murthy