International Business Machines Corporation

Ashish Billore

Pradipta Kumar Banerjee

Nanjangud C Narendra

Divya K Konoor

Sudipta Biswas

Sudheesh S Kairali

Muthu Annamalai Muthiah

Divya K K

As disclosed herein a computer-implemented method includes providing a plurality of resource allocation zones corresponding to a plurality of resource preparation operations, receiving a request from a requester for a resource, determining a selected resource allocation zone for a tenant from the plurality of resource allocation zones, and determining a resource usage pattern from historical usage data corresponding to the tenant. The method further includes provisioning the requested resource from the selected resource allocation zone based on the resource usage pattern, executing a resource preparation operation corresponding to the selected resource allocation zone in conjunction with provisioning the requested resource, and providing the requested resource to the tenant. A computer program product and a computer system corresponding to the above method are also disclosed herein.

Optimized resource provisioning

As disclosed herein a computer-implemented method includes providing a plurality of resource allocation zones corresponding to a plurality of overcommit policies. The method further includes receiving a request for a resource for a tenant, and determining a selected overcommit policy corresponding to an anticipated resource need. The method further includes selecting a resource allocation zone for the tenant from the plurality of resource allocation zones, and providing the resource to the tenant. A computer program product and a computer system corresponding to the above method are also disclosed herein.

Compartmentalized overcommitting of resources

A method for modeling dependencies in a computing system including a plurality of resources, the method includes generating usage data based on data communications in a computing environment between a first resource and a second resource; determining resource relationship data for the first resource and the second resource including the usage data; and computing a measure of bonding between the first resource and the second resource based, at least in part, on recency of the data communications.

Modeling computer network topology based on dynamic usage relationships

A method and system are provided. The method includes constructing a dependency graph of cloud resources based on interdependencies between the cloud resources. The dependency graph includes a set of nodes, a set of edges, and a precedence order. Each node corresponds to one of the cloud resources. Each edge corresponds to an association between two of the cloud resources. The precedence order corresponds to a processing order of the nodes. The method further includes applying a set of cleanup rules to each node to determine a cleanup eligibility of any of the cloud resources corresponding to each node. The method also includes removing from the cloud any of the cloud resources determined to be cleanup eligible based on the applying step. The dependency graph is constructed by scanning, in a cloud resource dependency construct, different cloud resource types to be incorporated into the dependency graph.

Identifying resources for purging in a cloud based on inter-dependency graph analysis

A computer manages methods for determining workload placement in a computing environment. The computer receives a plurality of workloads with associated information, wherein the associated information for each workload contains at least: contract information, billing information, and resource availability information. The computer determines a profitability factor for each workload of the plurality of workloads, wherein the profitability factor is at least based on the billing information. The computer determines a penalty factor for each workload of the plurality of workloads, wherein the penalty factor is at least based on the contract information. The computer determines a preference factor for each workload of the plurality of workloads, wherein the preference factor is at least based on the resource availability information. The computer assigns a priority ordering for each of the workloads from the plurality of workloads.

Optimization of workload placement

A computing device receives information from one or more agents, wherein the one or more agents monitor one or more resources in a cloud network. The computing device determines that the configuration of the one or more agents monitoring the one or more resources needs to be changed based on at least the information received from the one or more agents. The computing device changes the configuration of the one or more agents monitoring the one or more resources based on at least the information received from the one or more agents.

Dynamic agent replacement within a cloud network

A method for modeling dependencies in a computing system including a plurality of resources, the method includes monitoring data communications made between the resources of the plurality of resources; modeling dependencies, at least among and between the resources, based, at least in part, upon the monitored data communications; and generating a set of topology graph(s), including at least a first topology graph, based upon the modeled dependencies.

Embodiments of the present invention provide systems, methods, and computer program products for optimizing a placement plan. In one embodiment, a method is disclosed in which a request for registration with an external advisor is received. A time to live is received from each external advisor and used to determine an overall timeout period value for a placement engine. After receiving a predictive failure alert, internal and external advisors are ranked according to criteria and advice is received from the qualified advisors. A placement plan is generated based on the advice received from the advisors.

Optimizing placement plans for host failure in cloud computing environments

A method for calculating the effect of an action on a network includes creating a mapping of a plurality of devices of a networked computing environment. In one embodiment, the mapping describes a relationship between a primary device and at least one device of the plurality of devices. In another embodiment, the method includes determining a plurality of potential actions to be performed on the primary device. In a further embodiment, the method includes calculating an effect of a potential action of the plurality of potential actions on the plurality of devices in response to simulating performing the potential action on the primary device. In yet another embodiment, the method includes performing an optimization action in response to calculating the effect of the potential action. In certain embodiments, the optimization action maximizes availability of the networked computing environment.

Calculating the effect of an action in a network

An apparatus for calculating the effect of an action on a network includes a mapping module that creates a mapping of a plurality of devices of a networked computing environment. The mapping describes a relationship between a primary device and at least one device of the plurality of devices. The apparatus includes an action module that determines a plurality of potential actions to be performed on the primary device. The apparatus includes a calculation module that calculates an effect of a potential action of the plurality of potential actions on the plurality of devices in response to simulating performing the potential action on the primary device. The apparatus includes an optimization module that performs an optimization action in response to calculating the effect of the potential action. The optimization action maximizes availability of the networked computing environment.

A mechanism is provided in a data processing system for runtime based enforcement of energy policies collaboratively. The application runtime environment executing within a virtual machine on the data processing system receives notification of a change in energy policy for the virtual machine or the physical host it is running on. Responsive to determining the virtual machine is to be run under a power cap based on the notification of a change in energy policy, the application runtime environment dynamically modifies execution of an application in the application runtime environment or requests the execution environment for delaying enforcement of energy policies. The application comprises a set of application modules.

Growing community of inventors

Bengaluru, India

Ashish Billore