Abstract: Technical solutions are described for assigning containers for executing functions in a software application using a server cluster that uses functions as a service architecture. An example method includes creating, by a global scheduler of the server cluster, a first container at a first host node in the server cluster for executing a first function from the software application. The method further includes intercepting, by an in-node scheduler of the first host node, a command from the first function for creating a second container for executing a second function. The method further includes assigning, by the in-node scheduler, an in-node container as the second container, the in-node container being a container executing on the first host node. The method further includes updating, by the in-node scheduler, a resource database that indicates that the second function is being executed in the in-node container on the first host node.

Abstract: Aspects generating a container structure wherein processors are configured to compare attributes of a new container to attributes of each of plurality of existing containers within a container model as a function of a weighted Jaccard co-efficient distance matrix. The aspects identify a neighbor subset of the model containers that each have attributes that are nearest to the new container attributes, relative to remaining others of the model containers; select, as a nearest container, a container of the neighbor subset that has closest matching statistical properties to statistical properties calculated for the new container attributes; and generate a sizing recommendation for the new container to meet future utilization needs predicted as a function of usage pattern data of the nearest container.

Abstract: Aspects generating a container structure wherein processors are configured to compare attributes of a new container to attributes of each of plurality of existing containers within a container model as a function of a weighted Jaccard co-efficient distance matrix. The aspects identify a neighbor subset of the model containers that each have attributes that are nearest to the new container attributes, relative to remaining others of the model containers; select, as a nearest container, a container of the neighbor subset that has closest matching statistical properties to statistical properties calculated for the new container attributes; and generate a sizing recommendation for the new container to meet future utilization needs predicted as a function of usage pattern data of the nearest container.

Abstract: Technical solutions are described for assigning containers for executing functions in a software application using a server cluster that uses functions as a service architecture. An example method includes creating, by a global scheduler of the server cluster, a first container at a first host node in the server cluster for executing a first function from the software application. The method further includes intercepting, by an in-node scheduler of the first host node, a command from the first function for creating a second container for executing a second function. The method further includes assigning, by the in-node scheduler, an in-node container as the second container, the in-node container being a container executing on the first host node. The method further includes updating, by the in-node scheduler, a resource database that indicates that the second function is being executed in the in-node container on the first host node.

Abstract: An input is received relative to a user objective. An application placement consideration is established based upon, at least in part, the user objective. A best-fit application placement is dynamically determined for a plurality of applications relative to a plurality of servers based upon, at least in part, the application placement consideration.

Abstract: An input is received relative to a user objective. An application placement consideration is established based upon, at least in part, the user objective. A best-fit application placement is dynamically determined for a plurality of applications relative to a plurality of servers based upon, at least in part, the application placement consideration.

Abstract: A method, system, and program product for identifying cloud resources are provided, and further a method, system, and program product for configuring cloud resources are provided. The method for identifying cloud resources may include receiving a request with respect to at least one resource in a cloud and determining a set of resources among the at least one resource in the cloud in accordance with the received request. Determining the set of resources may include consulting an ontology including metadata associated with the at least one resource in the cloud and computing the set of resources based on the metadata and the received request. Additionally, the method may include computing a cost factor with respect to the determined set of resources. Furthermore, the method may include rendering, in response to the received request, the determined set of resources and the cost factor with respect to the determined set of resources.

Abstract: A mechanism is provided in a data processing system for deploying of an asset from a marketplace to a computing behind an enterprise firewall. A grabber service in a cloud management computing device in a private network behind the enterprise firewall monitors a placeholder in a file system of a marketplace server. Responsive to the grabber service finding a cloud asset deployment job stored in the placeholder, the grabber service retrieves the cloud asset deployment job. The cloud asset deployment job comprises logic for deploying a cloud asset. The mechanism downloads the cloud asset from a cloud asset repository to a cache behind the enterprise firewall according to the cloud asset deployment job. The mechanism deploys the cloud asset according to the cloud asset deployment job.

Abstract: A method, computer program product, and system are disclosed for modifying a computing environment hosting one or more workload patterns. A computer system obtains a list of deployed workload patterns on a on a computing environment, wherein each workload pattern is configured to support a predefined workload. The computer system identifies an extension to be deployed on a selected pattern from the list of deployed patterns. The computer system deploys the extension on the selected pattern. The computer system informs a user of the computing environment of new billing information based on deployment of the extension.

Abstract: A method, computer program product, and system are disclosed for modifying a computing environment hosting one or more workload patterns. A computer system obtains a list of deployed workload patterns on a on a computing environment, wherein each workload pattern is configured to support a predefined workload. The computer system identifies an extension to be deployed on a selected pattern from the list of deployed patterns. The computer system deploys the extension on the selected pattern. The computer system informs a user of the computing environment of new billing information based on deployment of the extension.

Abstract: A mechanism is provided in a data processing system for deploying of an asset from a marketplace to a computing behind an enterprise firewall. A grabber service in a cloud management computing device in a private network behind the enterprise firewall monitors a placeholder in a file system of a marketplace server. Responsive to the grabber service finding a cloud asset deployment job stored in the placeholder, the grabber service retrieves the cloud asset deployment job. The cloud asset deployment job comprises logic for deploying a cloud asset. The mechanism downloads the cloud asset from a cloud asset repository to a cache behind the enterprise firewall according to the cloud asset deployment job. The mechanism deploys the cloud asset according to the cloud asset deployment job.

Abstract: A method, system, and program product for identifying cloud resources are provided, and further a method, system, and program product for configuring cloud resources are provided. The method for identifying cloud resources may include receiving a request with respect to at least one resource in a cloud and determining a set of resources among the at least one resource in the cloud in accordance with the received request. Determining the set of resources may include consulting an ontology including metadata associated with the at least one resource in the cloud and computing the set of resources based on the metadata and the received request. Additionally, the method may include computing a cost factor with respect to the determined set of resources. Furthermore, the method may include rendering, in response to the received request, the determined set of resources and the cost factor with respect to the determined set of resources.

Abstract: Provided are techniques for the costing of a license based on real-time usage of software on a hardware resource instead of upfront PVU based purchase of licenses. Service provider need not pay for the license charge till the end of life of that application and need not pay for an entire set of N licenses, considering that the service provider expects that the maximum number of application instances could be N.

Abstract: A method, system, and program product for identifying cloud resources are provided, and further a method, system, and program product for configuring cloud resources are provided. The method for identifying cloud resources may include receiving a request with respect to at least one resource in a cloud and determining a set of resources among the at least one resource in the cloud in accordance with the received request. Determining the set of resources may include consulting an ontology including metadata associated with the at least one resource in the cloud and computing the set of resources based on the metadata and the received request. Additionally, the method may include computing a cost factor with respect to the determined set of resources. Furthermore, the method may include rendering, in response to the received request, the determined set of resources and the cost factor with respect to the determined set of resources.

Abstract: A technique for fairly distributing power savings benefits to virtual machines (VMs) provisioned to customers in a computing cloud. One or more VMs are provisioned on a target cloud host in response to resource requests from one or more customer devices. Host power savings on the target host are monitored. The host power savings are used as a variable component in determining per-customer cloud usage for accounting purposes. The host power savings may be reflected as power related cost savings in a generated cloud usage calculation result that may be distributed proportionately to the VMs based on VM size and utilization. VMs of relatively larger size and lower utilization may receive a higher percentage of the cost savings than VMs of relatively smaller size and higher utilization.

Abstract: A technique for fairly distributing power savings benefits to virtual machines (VMs) provisioned to customers in a computing cloud. One or more VMs are provisioned on a target cloud host in response to resource requests from one or more customer devices. Host power savings on the target host are monitored. The host power savings are used as a variable component in determining per-customer cloud usage for accounting purposes. The host power savings may be reflected as power related cost savings in a generated cloud usage calculation result that may be distributed proportionately to the VMs based on VM size and utilization. VMs of relatively larger size and lower utilization may receive a higher percentage of the cost savings than VMs of relatively smaller size and higher utilization.

Abstract: Provided are techniques for the costing of a license based on real-time usage of software on a hardware resource instead of upfront PVU based purchase of licenses. Service provider need not pay for the license charge till the end of life of that application and need not pay for an entire set of N licenses, considering that the service provider expects that the maximum number of application instances could be N.

Abstract: Provided are techniques for the costing of a license based on real-time usage of software on a hardware resource instead of upfront PVU based purchase of licenses. Service provider need not pay for the license charge till the end of life of that application and need not pay for an entire set of N licenses, considering that the service provider expects that the maximum number of application instances could be N.

Abstract: A computer implemented method, system and/or program product determine capacity planning of resources by utilizing a stochastic analysis of application workloads. Average workloads, which describe past resource utilization, of each of multiple applications are computed. A random sequence of the multiple applications is encoded into a first generation chromosome. The first generation chromosome is executed using a set of computing resources in a resource cloud. In response to execution of the first generation chromosome on the set of computing resources meeting a predetermined service level agreement (SLA) for the first generation chromosome, the first generation chromosome is scored in accordance with how many computing resources within the set of computing resources are utilized in order to meet the predetermined SLA.

Abstract: A computer implemented method, system and/or program product determine capacity planning of resources allocation for an application scheduled to execute on a virtual machine from a set of multiple applications by computing a mean associated with a pool of pre-defined resources utilization over a time interval; computing a variance associated with the pool of pre-defined resources utilization over the same time interval; identifying a set of resource to execute the scheduled application from the pool of pre-defined resources, wherein the pool of pre-defined resources is created from a pre-defined Service Level Agreement (SLA); and allocating a set of fixed resources from the pool of pre-defined resources to execute the application based on the mean resource utilization.