International Business Machines Corporation

Madhukar R Korupolu

Aameek Singh

Sandeep Gopisetty

Kaladhar Voruganti

Stephen Dill

Stefan Jaquet

Bhuvan Bamba

Michael D Cardosa

Andrew Stephen Tomkins

Binny Sher Gill

Karan Gupta

Prasenjit Sarkar

Shiva Chaitanya

Sandeep Madhav Uttamchandani

Seshashayee Sankarshana Murthy

Prashant Pandey

Soumyadeb Mitra

Konstantinos Magoutis

Scott Joseph Colbeck

Li Yin

Dushmanta Mohapatra

A virtual machine is assigned to a target physical server based on virtualization parameters for maximizing utility of a multiple virtual machines and physical servers. Resource allocation is performed for and deployment of the virtual machine to the target physical server based on capabilities of the target physical server and multiple virtual machine resource requirements. The virtualization parameters include a minimum parameter, a maximum parameter and a shares parameter. Processing resources are allocated based on utility priority of applications operating on the virtual machine using the shares parameter of contending virtual machines to determine a processing cycle ratio for distributing processing cycles between different utility priority applications operating on the contending virtual machines.

Server consolidation using virtual machine resource tradeoffs

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 integrated server-storage deployment planning for virtual appliances is provided. One implementation involves determining a performance cost in deploying the virtual appliance to different pairings of candidate host and storage subsystems. A host and storage subsystem pair is preferentially selected among the candidate pairings, a pairing of a host and storage subsystem with certain performance cost to satisfy performance requirements of the virtual appliance. Deployment planning may further involve deploying a virtual appliance on one or more appropriate spatially proximate hosts and storage subsystems that exhibit certain preferable connectivity and path capacity to satisfy the performance requirements of the virtual appliance.

Method and system for integrated deployment planning for virtual appliances

A method, system, and computer program product for visualization-centric performance-based volume allocation in a data storage system using a processor in communication with a memory device is provided. A unified resource graph representative of a global hierarchy of storage components in the data storage system, including each of a plurality of storage controllers, is generated. The unified resource graph includes a common root node and a plurality of subtree nodes corresponding to each of a plurality of nodes internal to the plurality of storage controllers. The common root node and the plurality of subtree nodes are ordered in a top-down orientation. Scalable volume provisioning of an existing or new workload amount by graphical manipulation of at least one of the storage components represented by the unified resource graph is performed based on an input.

Visualization-centric performance-based volume allocation in a data storage system

Server consolidation using virtual machine resource tradeoffs, is provided. One implementation involves assigning a virtual machine to a target physical server based on a plurality of virtualization parameters for maximizing utility of a plurality of virtual machines and physical servers. The assigning performs resource allocation for the virtual machine based on capabilities of the target physical server and a plurality of virtual machine resource requirements. Virtualization parameters include a reservation parameter (min) representing a minimum resources required for a VM, a limit parameter (max) representing a maximum resources allowable for the VM, and a weight parameter (shares) representing a share of spare resources for the VM.

A scalable, performance-based, volume allocation technique that can be applied in large storage controller collections is disclosed. A global resource tree of multiple nodes representing interconnected components of a storage system is analyzed to yield gap values for each node for a specific time period. The gap value for each node is an estimate of the amount of the additional or increased workload that can be allocated in the subtree of that node without exceeding the performance and space bounds at any of the nodes in that subtree for the specific time period. The gap values of the global resource tree are further analyzed to generate an ordered allocation list of the volumes of the storage system.

Scalable performance-based volume allocation for time-based storage access services

A method and apparatus are provided for enhancing the impact of data de-duplication by preferential selection of the master copy to be retained based on current loads and performance metrics of the storage media devices. The computer system is configured to take as input the identified duplicate copies of data and evaluating their locations in storage devices to determine the cumulative affects of retaining one of the identified duplicate copies as a master copy and optionally allocating a new location if needed. Once a master copy has been designated, the remaining identified duplicate copies are removed from storage.

Method of enhancing de-duplication impact by preferential selection of master copy to be retained

A scalable, performance-based, volume allocation technique that can be applied in large storage controller collections is disclosed. A global resource tree of multiple nodes representing interconnected components of a storage system in a plurality of component layers is analyzed to yield gap values for each node (e.g., a bottom-up estimation). The gap value for each node is an estimate of the amount in GB of the new workload that can be allocated in the subtree of that node without exceeding the performance and space bounds at any of the nodes in that subtree. The gap values of the global resource tree are further analyzed to generate an ordered allocation list of the volumes of the storage system (e.g., a top-down selection). The volumes may be applied to a storage workload in the order of the allocation list and the gap values and list are updated.

Scalable performance-based volume allocation in large storage controller collections

A storage placement planning system receives a resource graph describing SAN's resources and virtual machine applications requiring a particular amount of a processing resource element and a storage resource element. The system then determines a coupled placement of the processing element and storage element for each of the applications on a coupled pair of the resource nodes based on a specified throughput and a distance factor between coupled pairs of resource nodes. The coupled placement is determined using an algorithm that implements a cost function that determines affinities between processing nodes and storage nodes for each of said applications of a particular workload. The coupled placement for each of said applications identifies the particular amount of processing resource element placed on a first node for providing a processing resource and the particular amount of storage resource element placed on a second node for providing a storage resource for that application.

Integrated placement planning for heterogenous storage area network data centers

Disclosed is a storage management framework that integrates corrective action plans output from multiple different types of planning tools, sorts the different corrective action plans based on utility and risk values and outputs a time-based schedule for implementing one or more of the corrective action plans to resolve identified current and anticipated workload service level objective (SLO) violations.

System and method for selecting and scheduling corrective actions for automated storage management

A by-line extraction system detects a set of potential headlines from a title meta-tag of a crawled document, selects a candidate headline from the set of potential headlines, and extracts the by-line information from the document using the location of the selected candidate headline. The system constructs the set of potential headlines based on the title meta-tag. The system selects a candidate headline by evaluating the set of potential headlines in order of the lengths of the potential headlines. The system extracts the by-line information from the document by using the location of the selected candidate headline to extract a string representing a date, a name, or a source located within a minimum distance from the location of the potential headline.

Automatically extracting by-line information

A method and system selecting a target physical machine for a virtual machine (VM) migration is provided. Selecting a target physical machine for a VM migration involves determining storage volume connectivity and spare path capacity of one or more candidate physical machines, and preferentially selecting among the candidate physical machines a migration target with storage volume connectivity and spare path capacity to satisfy storage volume access requirements of the VM.

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Sunnyvale, CA, United States of America

Madhukar R Korupolu