International Business Machines Corporation

Roy Frank Brabson

Rohith Kottamangalam Ashok

Hugh Edward Hockett

Matt Richard Hogstrom

Ajay Arvind Apte

Scott Christopher Moonen

Donald R Woods

Wei Lung Chan

Lap Thiet Huynh

Linwood Hugh Overby, Jr

Jason Robert McGee

Orvalle T Kirby, Iii

Aaron J Quirk

Mark Richard Gambino

Matthew J Sheard

Rahul Ghosh

Constantinos Kassimis

Antonio Abbondanzio

Andrew Russell Low

Linwood H Overby, Jr

Arthur James Stagg

Wesley McMillan Devine

Michael John Burr

Barry Mosakowski

Barry P Gower

Wolfgang Segmuller

Kristin R Whetstone

John L Klonowski

Edward M Howard

David N Nguyen

Theodore O Kirby

Bret Ronald Olszewski

David Bruce Lindquist

Peter Bergersen Yocom

James Corvin Fletcher

Michael E Baskey

Ivan M Heninger

Bala Rajaraman

Sivaram Gottimukkala

Edward Glen Britton

Jose I Ortiz

Michael S Law

Carroll Eugene Fulkerson, Jr

Patrick Gerald Brown

John Majikes

Alfred Bundgaard Christensen

Samuel E Reynolds

Patrick S O'Donnell

Ralph Benjamin Case

Johnathan L Harter

John L Brady

Andrew K Tracy

Wesley McMillian Devine

Mickael Maison

Janet C Wolf

Methods, systems and computer program products for load balancing using Mobile Internet Protocol (IP) Version 6 are provided. A request for a connection is received from a client at a routing stack. A Mobile IP Version 6 Binding Update message is transmitted from the routing stack to the client responsive to the received request. The Binding Update message identifies a selected target stack so as to allow the client to communicate directly with the target stack bypassing the routing stack.

Bypassing routing stacks using mobile internet protocol

A method and technique for allocation and balancing of storage resources includes monitoring, for each of a plurality of storage controllers, an input/output (I/O) latency value based on an I/O latency associated with each storage volume controlled by a respective storage controller. An I/O latency value threshold is determined. Responsive to a change to the I/O latency value exceeding a threshold, storage volume distribution among the storage controllers is rebalanced.

Allocation and balancing of storage resources

Components are dynamically associated in a multi-tier application to different layers of a corresponding multi-tier application infrastructure. This includes defining, in a memory of a host computing system, a pattern that has an inventory of components of a multi-tier application. Each of the components are associated with a corresponding tier label for an n-tier architecture and the pattern is loaded into a pattern engine. The pattern engine deploys each component of the pattern to a layer of the n-tier architecture corresponding to a tier label associated with the component.

Dynamic association of application workload tiers to infrastructure elements in a cloud computing environment

A method and technique for allocation and balancing of storage resources includes monitoring, for each of a plurality of storage controllers, input/output (I/O) latency, network bandwidth utilization, and network latency associated with each storage volume controlled by a respective storage controller. Responsive to receiving a request to allocate a new storage volume, a type of application and an anticipated storage workload level that will utilize one or more of the storage volumes by the application is determined. The I/O latency, network bandwidth utilization, and network latency is analyzed relative to respective thresholds, and the new storage volume is allocated to a selected storage controller based on the analysis and the anticipated storage workload level.

Allocation and balancing of storage resources based on anticipated workload levels

Embodiments of the present invention provide a method, system and computer program product for the dynamic association of components in a multi-tier application to different layers of a corresponding multi-tier application infrastructure. In an embodiment of the invention, a method for dynamically associating components in a multi-tier application to different layers of a corresponding multi-tier application infrastructure includes defining in memory of a host computing system a pattern that has an inventory of components of a multi-tier application. The method also includes associating each of the components with a corresponding tier label for an n-tier architecture. The method yet further includes loading the pattern into a pattern engine. Finally, the method includes deploying by the pattern engine each component of the pattern to a layer of the n-tier architecture corresponding to a tier label associated with the component.

A method and technique for allocation and balancing of storage resources includes monitoring, for each of a plurality of storage controllers, an input/output (I/O) latency value based on an I/O latency associated with each storage volume controlled by a respective storage controller. A network bandwidth utilization value and a network latency value corresponding to each storage controller is also monitored. An I/O latency value threshold, a network bandwidth utilization threshold, and a network latency value threshold is determined. Responsive to at least one of the I/O latency value, the network bandwidth utilization value and the network latency value for at least one storage controller exceeding a respective threshold, storage volume distribution among the storage controllers is rebalanced.

A method, system and computer program product for selecting hypervisor platforms that are best suited to process application workloads. Attribute requirements for an application workload, such as high CPU capacity, high power and low cost, are received. A ranking algorithm is then applied to a list of pools of compute nodes to identify an ordered list of pools of compute nodes that are best suited for satisfying the attribute requirements of the application workload by comparing hypervisor characteristics of the pools of compute nodes with the attribute requirements of the application workload. Each pool of compute nodes runs on a particular hypervisor platform which has a unique combination of characteristics that correspond to a combination of a set of attribute requirements (e.g., medium CPU/memory/disk capacity; high CPU and memory performance). In this manner, the hypervisor platforms that are best suited for satisfying the application workload requirements are identified.

Selecting optimal hypervisor platforms that satisfy application workload requirements

A method, system and computer program product for deploying a pattern. A placement engine of a hierarchical tier selects the region(s) of a next lower tier based on placement policies and constraints at the next lower tier and availability of the artifacts required by the virtual machine instance(s) in the next lower tier utilizing the summarization of resource usage and availability at the next lower tier. The placement engine at the lowest tier selects the compute node(s) in the selected region(s) of the lowest tier to place the virtual machine instances of the pattern based on placement policies and constraints at the compute node level and availability of the artifacts required by the virtual machine instance(s) of the pattern in the compute node level. In this manner, the virtual machine instances of the pattern can be effectively deployed when the resources of the cloud environment are large and geographically dispersed.

Deploying virtual machine instances of a pattern to regions of a hierarchical tier using placement policies and constraints

A method, system and computer program product for allocating compute nodes among cloud groups. Policies are created for cloud groups for specifying conditions when a compute node will need to be borrowed by a cloud group as well as when the borrowed compute node is to be returned. Furthermore, priorities are assigned to the conditions in the policies for borrowing a compute node as well as to the cloud groups concerning borrowing compute nodes. A compute node is allocated temporarily from a first cloud group to a second cloud group based on the priority assigned to the second cloud group and the priority assigned to the monitored condition invoking the borrowing of the compute node as well as based on the monitored condition satisfying a condition in the policy for the second cloud group as to when the compute node will need to be borrowed by the second cloud group.

Dynamically allocating compute nodes among cloud groups based on priority and policies

A method, system and computer program product for determining a relative priority for a job. A 'policy' is selected based on the job itself and the reason that the job is being executed, where the policy includes a priority range for the job and for an application. A priority for the job that is within the priority range of the job as established by the selected policy is determined based on environmental and context considerations. This job priority is then adjusted based on the priority of the application (within the priority range as established by the policy) becoming the job's final priority. By formulating a priority that more accurately reflects the true priority or importance of the job by taking into consideration the environmental and context considerations, job managers will now be able to process these jobs in a more efficient manner.

Determining a relative priority for a job using context and environmental considerations

A method and technique for allocation and balancing of storage resources includes: determining, for each of a plurality of storage controllers, an input/output (I/O) latency value based on an I/O latency associated with each storage volume controlled by a respective storage controller; determining network bandwidth utilization and network latency values corresponding to each storage controller; responsive to receiving a request to allocate a new storage volume, selecting a storage controller having a desired I/O latency value; determining whether the network bandwidth utilization and network latency values for the selected storage controller are below respective network bandwidth utilization and network latency value thresholds; and responsive to determining that the network bandwidth utilization and network latency values for the selected storage controller are below the respective thresholds, allocating the new storage volume to the selected storage controller.

Growing community of inventors

Raleigh, NC, United States of America

Roy Frank Brabson