Abstract: This disclosure relates generally to selection of cloud service providers in a multi-cloud. The selection of cloud service providers (CSPs) in a multi-cloud environment is challenging as it depends on multiple criteria such as security, usability, latency, and service cost. In an embodiment, for each CSP and multi-CSP combination, two decision problems are solved; namely, a VM selection problem and a cloud region selection problem, taking latency requirement and DR laws constraints into consideration. By solving the VM selection and CC selection problems, the service cost and the user to cloud region mapping are estimated. Using said estimates and other qualitative criteria, a multi-cloud multi-criteria decision problem is solved using MCDM techniques to rank the CSP combinations for each application. Based on the ranks and a constraint on a service cost of the entity one of a CSP and a CSP combination is selected for each application.

Abstract: Data is an asset to any organization and any breach to the data during task allocation to agents may lead to serious damage to organizations including loss of consumer confidence, trust, reputation, financial penalties and the like. Conventional methods mainly focus on the allocating task to agents based on user satisfaction, overall throughput and maximize revenue and less focus is given to data privacy. The present subject matter overcomes the limitations of the conventional methods for task allocation by utilizing a dynamic data exposure analysis method, which enables seamless upgrading of the data access policy and or control. Here, a data exposure is monitored based on a data exposure score, dynamic identification of conflicting tasks and a dynamic privacy budget. The data exposure score is calculated in two execution points. Finally all the values are updated in the system for utilization in the further privacy enabled task allocation.

Abstract: This disclosure relates generally to selection of cloud service providers in a multi-cloud. The selection of cloud service providers (CSPs) in a multi-cloud environment is challenging as it depends on multiple criteria such as security, usability, latency, and service cost. In an embodiment, for each CSP and multi-CSP combination, two decision problems are solved; namely, a VM selection problem and a cloud region selection problem, taking latency requirement and DR laws constraints into consideration. By solving the VM selection and CC selection problems, the service cost and the user to cloud region mapping are estimated. Using said estimates and other qualitative criteria, a multi-cloud multi-criteria decision problem is solved using MCDM techniques to rank the CSP combinations for each application. Based on the ranks and a constraint on a service cost of the entity one of a CSP and a CSP combination is selected for each application.

Abstract: Data is an asset to any organization and any breach to the data during task allocation to agents may lead to serious damage to organizations including loss of consumer confidence, trust, reputation, financial penalties and the like. Conventional methods mainly focus on the allocating task to agents based on user satisfaction, overall throughput and maximize revenue and less focus is given to data privacy. The present subject matter overcomes the limitations of the conventional methods for task allocation by utilizing a dynamic data exposure analysis method, which enables seamless upgrading of the data access policy and or control. Here, a data exposure is monitored based on a data exposure score, dynamic identification of conflicting tasks and a dynamic privacy budget. The data exposure score is calculated in two execution points. Finally all the values are updated in the system for utilization in the further privacy enabled task allocation.