Abstract: A method and system to determine a computational resource requirement is described. The method and system rank one or more computational resources for each of the plurality of tasks in an ascending order, based on a cost associated with the plurality of tasks for each of the computational resource. Based on the ranked one or more computational resources and a fairness metric, the method and system allocates the one or more capacity units associated with the computational resource to perform the plurality of tasks. The method and system determines the computational resource requirement to perform the plurality of tasks based on the allocated one or more capacity units. The fairness metric ensures that allocation of the one or more capacity units to the plurality of tasks is performed in a manner to maximize the fairness.

Abstract: Methods and systems for computational resource allocation in a distributed computing environment are disclosed. A request for computational resource allocation is received at a first computational node. The request comprises at least a threshold value of an expected reliability associated with a set of required computational resources. The availability of one or more computational resources from the set of required computational resources is determined at the first computational node. Based on the determined availability of the one or more computational resources, a first reliability score of the first computational node is determined. Further, the first reliability score is compared with the threshold value of expected reliability. Based on the comparison, the one or more computational resources are allocated to process the request.

Abstract: The disclosed embodiments illustrate methods and systems for selecting a crowd workforce for processing a task. The method includes receiving a request from a requestor to process the task. The method further includes generating a set of rules based on at least one or more attributes associated with the task. The method further includes selecting a first set of crowd workers, from one or more crowd workers, based on at least a triggering of one or more rules from the set of rules. The triggering of the one or more rules is based on at least a set of threshold values associated with the one or more crowd workers. The method further includes displaying a resource graph on a display screen of a requestor-computing device associated with a requestor, where the resource graph represents at least the first set of crowd workers.

Abstract: A method and system to determine a computational resource requirement is described. The method and system rank one or more computational resources for each of the plurality of tasks in an ascending order, based on a cost associated with the plurality of tasks for each of the computational resource. Based on the ranked one or more computational resources and a fairness metric, the method and system allocates the one or more capacity units associated with the computational resource to perform the plurality of tasks. The method and system determines the computational resource requirement to perform the plurality of tasks based on the allocated one or more capacity units. The fairness metric ensures that allocation of the one or more capacity units to the plurality of tasks is performed in a manner to maximize the fairness.

Abstract: Methods and systems for creating one or more statistical classifiers. A first set of performance parameters, corresponding to the one or more applications and the one or more computing infrastructures, is extracted from a historical data pertaining to the execution of the one or more applications on the one or more computing infrastructures. Further, a set of application-specific and a set of infrastructure-specific parameters are selected, from the first set of performance parameters, based on one or more statistical techniques. A similarity between each pair of the applications, each pair of the computing infrastructures, and each pair of possible combinations of an application and a computing infrastructure is determined. One or more statistical classifiers are created, based on the determined similarity.

Abstract: Methods and systems for sharing computational resources. A request from a first node is received for the one or more computational resources. The request comprises a service level agreement (SLA) associated with the requested one or more computational resources. The request is compared with one or more advertisements sent by at least two second nodes, other than the first node. The one or more advertisements correspond to an availability of a set of computational resources associated with each of the at least two second nodes. A portion of computational resources from the set of computational resources associated with each of the at least two second nodes is allocated to the first node, based on the comparison, such that a combination of the portion of computational resources satisfy the SLA associated with the request.

Abstract: Embodiments of a method are disclosed for computing trust index among multiple entities associated with a resource marketplace. The method includes receiving multiple inputs including interaction attributes, attribute importance factors, references to databases, and multiple entities associated with the resource marketplace. The method also includes creating a weighted-interaction graph based on the received inputs. The weighted-interaction graph includes multiple vertices representing the entities. The method further includes performing a topology-specific analysis of the weighted-interaction graph. The method furthermore includes computing Euclidean distances for each pair of vertices in the weighted-interaction graph based on the performed analysis. The method also includes determining a trust index for a first entity in the received multiple entities based on the computed Euclidean distances. The trust index includes ranking of at least one of the multiple entities with respect to the first entity.

Abstract: Methods and systems for determining incentives for sharing one or more computational resources in a network. A request from a resource requester is received for executing a workload. The request comprises a service level agreement (SLA) associated with said execution of said workload. A contribution of one or more computational resources, associated with a resource provider, in satisfying said SLA is determined based at least on a capacity associated with said one or more computational resources, a duration of a usage of said one or more computational resources for said execution, and one or more constraints included in said SLA. The incentives for said resource provider for said sharing of said one or more computational resources is determined based at least on said contribution.

Abstract: Methods and systems for recommending one or more computational resources. A portion of computational resources is determined from a set of computational resources associated with a datacenter based on a user-profile associated with a user, from one or more users, and the set of computational resources. The user-profile comprises at least one of a required performance level, a required load, or a cost constraint. The one or more computational resources are recommended from the portion of computational resources, in response to requests received from the one or more users.

Abstract: There is provided systems and methods for creating a repository of templates. The templates are deterministic of a configuration a virtual machine. The method includes creating one or more templates for each of one or more applications types based on a benchmark data. Each of the one or more templates is stored in a hierarchal structure having one or more hierarchal levels. Each of the one or more hierarchal levels is indicative of a parameter of the configuration of the virtual machine. Thereafter, one or more rules are defined to traverse through the one or more hierarchal levels to access the one or more templates.

Abstract: Embodiments of a method are disclosed for computing trust index among multiple entities associated with a resource marketplace. The method includes receiving multiple inputs including interaction attributes, attribute importance factors, references to databases, and multiple entities associated with the resource marketplace. The method also includes creating a weighted-interaction graph based on the received inputs. The weighted-interaction graph includes multiple vertices representing the entities. The method further includes performing a topology-specific analysis of the weighted-interaction graph. The method furthermore includes computing Euclidean distances for each pair of vertices in the weighted-interaction graph based on the performed analysis. The method also includes determining a trust index for a first entity in the received multiple entities based on the computed Euclidean distances. The trust index includes ranking of at least one of the multiple entities with respect to the first entity.

Abstract: Methods and systems for creating one or more statistical classifiers. A first set of performance parameters, corresponding to the one or more applications and the one or more computing infrastructures, is extracted from a historical data pertaining to the execution of the one or more applications on the one or more computing infrastructures. Further, a set of application-specific and a set of infrastructure-specific parameters are selected, from the first set of performance parameters, based on one or more statistical techniques. A similarity between each pair of the applications, each pair of the computing infrastructures, and each pair of possible combinations of an application and a computing infrastructure is determined. One or more statistical classifiers are created, based on the determined similarity.

Abstract: Methods and systems for recommending one or more computational resources. A portion of computational resources is determined from a set of computational resources associated with a datacenter based on a user-profile associated with a user, from one or more users, and the set of computational resources. The user-profile comprises at least one of a required performance level, a required load, or a cost constraint. The one or more computational resources are recommended from the portion of computational resources, in response to requests received from the one or more users.

Abstract: Methods and systems for sharing computational resources. A request from a first node is received for the one or more computational resources. The request comprises a service level agreement (SLA) associated with the requested one or more computational resources. The request is compared with one or more advertisements sent by at least two second nodes, other than the first node. The one or more advertisements correspond to an availability of a set of computational resources associated with each of the at least two second nodes. A portion of computational resources from the set of computational resources associated with each of the at least two second nodes is allocated to the first node, based on the comparison, such that a combination of the portion of computational resources satisfy the SLA associated with the request.

Abstract: There is provided systems and methods for creating a repository of templates. The templates are deterministic of a configuration a virtual machine. The method includes creating one or more templates for each of one or more applications types based on a benchmark data. Each of the one or more templates is stored in a hierarchal structure having one or more hierarchal levels. Each of the one or more hierarchal levels is indicative of a parameter of the configuration of the virtual machine. Thereafter, one or more rules are defined to traverse through the one or more hierarchal levels to access the one or more templates.

Abstract: Methods and systems for determining prices of customized virtual machines required to process customer-specified workloads are disclosed. A count of instances of the customized virtual machines, required to process the customer-specified workloads is determined, based on a configuration of the customized virtual machines. The instances of the customized virtual machines are consolidated on virtual machine servers. Further, the prices of the customized virtual machines are determined based on a count of the virtual machine servers, unused resources in the virtual machine servers, and unused resources in the customized virtual machines. The determined prices are recommended to the customer. Further, at least one of the prices of the customized virtual machines or the configuration of at least one or more customized virtual machines is modified, based on a response to the recommendation received from the customer.