International Business Machines Corporation

Emc Corporation

Emc IP Holding Company LLC

Thirumale N Niranjan

Joseph M Swingler

Vitaly Stanislavovitch Kozlovsky

Sairam Veeraswamy

Mark Albert Reid

Karthikeyan Srinivasan

Michael Wayne Young

Gregory Tad Kishi

Jonathan Wayne Peake

Dmitry Nikolayevich Tylik

Thomas W Bish

Steven M Berman

Ullas Balan Nambiar

Animesh Singh

Ivan Valentinovich Gumenyuk

Karthik Nagesh

Prateek Sharma

Techniques are disclosed for managing workloads across a distributed virtual infrastructure. In one example, a method comprises the following steps. Information is collected relating to services offered by service providers across a multi cloud environment implemented in accordance with a distributed virtual infrastructure. A service capability model is maintained based on the collected information. Service level claim conformance is validated for the services offered by the service providers. One or more recommendations are generated based on the collecting, maintaining and validating steps for presentation to a subscriber to assist in management of one or more workloads across the multi cloud environment.

Workload management in multi cloud environment

A first data set is obtained specifying configuration information associated with elements of a computing system. A second data set is also obtained specifying dependency information associated with the elements of the computing system. A third data set is also obtained specifying deployment constraint information associated with the elements of the computing system. A plan for migrating one or more of the elements of the computing system is automatically generated based on at least a portion of the first data set, at least a portion of the second data set, and at least a portion of the third data set. The automatic generation of the migration plan checks for one or more conflicts between configuration information, dependency information and deployment constraint information and generates the migration plan to at least substantially eliminate the one or more conflicts.

Management of computing system element migration

An improved technique involves providing application workload simulation capability via a centrally located synthetic data generation service to an external party. Along these lines, a client inputs a description of an application scenario into a web form which sends a request to a central server to generate a synthetic data simulation program. The central server extracts parameters values from the request corresponding to the application scenario and finds a model in a model database based on the parameter values. From the model, the central server derives a Java program that generates synthetic data for simulating the operation load of the application scenario described by the client, and sends the Java program to the client. The client is then able to generate synthetic data using the Java program on various configurations to determine which will successfully handle the synthetic data load continuously over a given period of time.

Public service for emulation of application load based on synthetic data generation derived from preexisting models

Techniques are disclosed for measuring and attesting to service levels offered by a service provider in a computing system implemented with a distributed virtual infrastructure. In one example, a method comprises the following steps. Information is maintained relating to one or more services offered by one or more service providers in accordance with a distributed virtual infrastructure. One or more service level claims associated with the one or more services are validated. A subscriber is sent validation results for at least a portion of the one or more services.

Cloud service level attestation

An improved technique involves generating a predictive model for data storage system configuration management. A customer support center generates such a predictive model from detailed customer configuration and transaction history. For example, a population of customers submits transaction logs to the customer support center; such transaction logs provide details as to how the customers responded to various events. The population of customers may also submit data including various statistics such as load intensity, workload characteristics, data access patterns, data change patterns, and data fingerprints to the customer support center. The customer support center then performs an analysis on the data and, from the analysis, computes values of model parameters that define a predictive model. This predictive model is configured to take in a particular state of any data storage system and produce a configuration that optimizes performance of that data storage system.

Predictive models for configuration management of data storage systems

An improved technique involves consulting a predictive model to optimize a configuration of a data storage system. A customer at a data storage system requests a configuration optimization from a customer support center by sending values of input parameters to the customer support center. Such input parameters represent a current state of the data storage system, e.g., initial configuration. The customer support center then receives configuration parameters in response to inputting the input parameters into a predictive model running on a host computer. The customer uses the configuration parameters to define the configuration state in which the data storage system runs.

Embedded advisory framework for storage configuration management

For selecting an input/output tape volume cache (TVC), a history module maintains access history instances for a plurality of clusters, each cluster comprising a TVC. A request module receives an access request for a logical volume wherein an instance of the logical volume is stored on each of the plurality of clusters and each instance of the logical volume is synchronized with each other instance of the logical volume. An adjustment module weights the access history instances in favor of recent access history instances. A calculation module calculates an affinity of the logical volume instance stored on each cluster of the plurality of clusters. A selection module selects a cluster TVC with a highest logical volume affinity as the TVC for the logical volume.

Selecting an input/output tape volume cache

An apparatus, system, and method are disclosed for selecting an input/output tape volume cache (TVC). A history module maintains access history instances for a plurality of clusters. A request module receives an access request for a logical volume. An adjustment module weights the access history instances in favor of recent access history instances. A calculation module calculates an affinity of the logical volume for each cluster of the plurality of clusters. The calculation module may calculate the affinity of the logical volume for each cluster of the plurality of clusters using read/write/scratch granularity and an algorithm. Further, the calculation module may filter out clusters. A selection module selects a cluster TVC with a highest affinity as the TVC for the logical volume.

Apparatus, system, and method for selecting an input/output tape volume cache

Provided are techniques for workload balancing. A message is received on a channel. A thread in a thread pool is selected to process the message. In response to determining that the message has been processed and a response has been sent on the channel by the thread, it is determined whether a total number of threads in the thread pool is greater than a low water mark plus one and whether the channel has more than a maximum number of threads blocked on a receive, wherein the low water mark represents a minimum number of threads in the thread pool. In response to determining that a number of threads in the thread pool is greater than the low water mark plus one and that the channel has more than the maximum number of threads blocked on a receive, the thread is terminated. In response to determining at least one of the number of threads in the thread pool is less than or equal to the low water mark plus one and the channel has less than or equal to the maximum number of threads blocked on a receive, the thread is retained.

Determining whether to retain or terminate a thread based on a minimum number of threads in a thread pool and a maximum number of threads allowed waiting on the channel

Provided are a method, system, and article of manufacture that maintain a plurality of nodes coupled over a network, wherein a first node of the plurality of nodes initially owns a plurality of storage objects accessible via the network. A serialization lock is maintained in association with the plurality of storage objects, wherein the first node becomes unavailable. A second node of the plurality of nodes determines that the second node is to preemptively assume ownership of a selected storage object of the plurality of storage objects initially owned by the first node that has become unavailable. The second node acquires the serialization lock. The second node secures ownership of the selected storage object, subsequent to acquiring the serialization lock.

Securing transfer of ownership of a storage object from an unavailable owner node to another node

An apparatus, system, and method are disclosed for selecting a source cluster in a distributed storage configuration. A measurement module measures system factors for a plurality of clusters over a plurality of instances. The clusters are in communication over a network and each cluster comprises at least one tape volume cache. A smoothing module applies a smoothing function to the system factors, wherein recent instances have higher weights. A lifespan module calculates a mount-to-dismount lifespan for each cluster from the smoothed system factors. A selection module selects a source cluster for accessing an instance of a specified volume in response to the mount-to-dismount lifespans and a user policy.

Selecting a source cluster by measuring system factors, calculating a mount-to-dismount lifespan, and selecting the source cluster in response to the lifespan and a user policy

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Bengaluru, India

Thirumale N Niranjan