International Business Machines Corporation

Vijay K Sukthankar

Pradipta Kumar Banerjee

Ajay Kumar Sood

Naveen G Balani

Archana Kumar

Swarna Latha Mylavarapu

Swarnalatha Mylavarapu

Albee Jhoney

Sudipta Biswas

Anmol Sandeep Agrawal

Daniel Thomas Albano

Venugopala Chanrayappa

Suman Mondal

Mohammed Asif N Kundgol

Pothuraju Srinivas

Alexander Nathenial Perira

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.

Computer server application execution scheduling latency reduction

A computer program product for a network management device, including: a computer readable storage medium to store a computer readable program, wherein the computer readable program, when executed on a computer, causes the computer to perform operations for server management in a computer network. The operations include: receiving resource usage data generated at a network communication device coupled between a server and a network management device, wherein the resource usage data describes resource usage of the server; and classifying the server into a cluster of servers based on the resource usage data from the network communication device and a cluster characterization for the cluster. The cluster includes a plurality of servers with similar resource usage data, and the cluster is one of a plurality of clusters managed by the network management device.

Server consolidation system

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.

Container structure

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.

Dynamically tuning server placement

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.

Configuring cloud resources

Systems and methods for simulation within a cloud computing environment. According to one embodiment, the system or method comprises a cloud computing environment with infrastructure management software and a cloud-based managed infrastructure. An interface is identified within infrastructure management software by first identifying a dataflow from the infrastructure management software to the infrastructure, and then, based on the dataflow, identifying a component within the infrastructure management software that is interacting with the infrastructure. A simulation class is then implemented using the identified interface, where the simulation class populates the infrastructure management software with metadata from an infrastructure database such that the infrastructure is simulated.

Model for simulation within infrastructure management software

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.

Installation of an asset from a cloud marketplace to a cloud server in a private network

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.

Real-time license metering of a provisioned application in a cloud computing environement

Real-time license metering of a provisioned application in a cloud computing environment

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. Next generation chromosomes, derived from the first generation chromosome, are executed and scored until an optimal chromosome, which utilizes the fewest computing resources, is identified and deployed.

Optimized capacity planning

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.

A computer implemented method, system and/or program product configure cloud resources. A request is received for a specific set of cloud resources. The set of cloud resources comprises at least one cloud resource that has been associated with an ontological database. The ontological database defines descriptive terms for the cloud resource and describes relationships between the descriptive terms used by different cloud resources. An optimal set of cloud resources that satisfies the request is configured and saved for future usage in responding to requests for the specific set of cloud resources.

Resource loads are determined based on normalized factors that are themselves based on resource factors. The following is repeated until each resource's load is less than or equal to the resource's willingness capacity or until all the resources are each fully loaded or unavailable. Each resource's willingness capacity is determined based on the resource's willingness parameter. The willingness parameter indicates at least whether a resource is willing to receive a number of processing requests no greater than a maximum or a minimum request limit. A throughput overflow is determined for each resource having a load greater than its willingness capacity. The load for each resource having a throughput overflow greater than zero is reset. The load for each resource that is available and not fully loaded is updated based on new normalized factors and based on a potential throughput loss that is equal to a sum of the throughput overflows.

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Bengaluru, India

Vijay K Sukthankar