Abstract:
The present disclosure describes a method for cloud resource placement optimization. A resources monitor monitors state information associated with cloud resources and physical hosts in the federated cloud having a plurality of clouds managed by a plurality of cloud providers. A rebalance trigger triggers a rebalancing request to initiate cloud resource placement optimization based on one or more conditions. A cloud resource placement optimizer determines an optimized placement of cloud resources on physical hosts across the plurality of clouds in the federated cloud based on (1) costs including migration costs, (2) the state information, and (3) constraints, wherein each physical host is identified in the constraints-driven optimization solver by an identifier of a respective cloud provider and an identifier of the physical host. A migrations enforcer determines an ordered migration plan and transmits requests to place or migrate cloud resources according to the ordered migration plan.
Abstract:
The present disclosure describes a method for cloud resource placement optimization. A resources monitor monitors state information associated with cloud resources and physical hosts in the federated cloud having a plurality of clouds managed by a plurality of cloud providers. A rebalance trigger triggers a rebalancing request to initiate cloud resource placement optimization based on one or more conditions. A cloud resource placement optimizer determines an optimized placement of cloud resources on physical hosts across the plurality of clouds in the federated cloud based on (1) costs including migration costs, (2) the state information, and (3) constraints, wherein each physical host is identified in the constraints-driven optimization solver by an identifier of a respective cloud provider and an identifier of the physical host. A migrations enforcer determines an ordered migration plan and transmits requests to place or migrate cloud resources according to the ordered migration plan.
Abstract:
The present disclosure describes a method for cloud resource placement optimization. A resources monitor monitors state information associated with cloud resources and physical hosts in the federated cloud having a plurality of clouds managed by a plurality of cloud providers. A rebalance trigger triggers a rebalancing request to initiate cloud resource placement optimization based on one or more conditions. A cloud resource placement optimizer determines an optimized placement of cloud resources on physical hosts across the plurality of clouds in the federated cloud based on (1) costs including migration costs, (2) the state information, and (3) constraints, wherein each physical host is identified in the constraints-driven optimization solver by an identifier of a respective cloud provider and an identifier of the physical host. A migrations enforcer determines an ordered migration plan and transmits requests to place or migrate cloud resources according to the ordered migration plan.
Abstract:
The present disclosure describes a method for cloud resource placement optimization. A resources monitor monitors state information associated with cloud resources and physical hosts in the federated cloud having a plurality of clouds managed by a plurality of cloud providers. A rebalance trigger triggers a rebalancing request to initiate cloud resource placement optimization based on one or more conditions. A cloud resource placement optimizer determines an optimized placement of cloud resources on physical hosts across the plurality of clouds in the federated cloud based on (1) costs including migration costs, (2) the state information, and (3) constraints, wherein each physical host is identified in the constraints-driven optimization solver by an identifier of a respective cloud provider and an identifier of the physical host. A migrations enforcer determines an ordered migration plan and transmits requests to place or migrate cloud resources according to the ordered migration plan.
Abstract:
The present disclosure describes a method for virtual machine placement optimization based on generalized organizational scenarios. The method involves defining a variable matrix (wherein each entry of the variable matrix indicate whether a particular virtual machine is to be placed on a particular host server), a first set of variables (wherein each variable of the first set of variables indicate whether a particular host server has at least one virtual machine to be placed thereon), a second set of variables (wherein the second set of variables indicates for all possible pairs of host servers whether two particular host servers both have at least one virtual machine to be placed thereon). The method further involves determining a set of virtual machine to host server allocations by solving a constraints optimization problem over the first set of variables and the second set of variables based on a generalized organizational scenario.
Abstract:
The present disclosure describes a method for virtual machine placement optimization based on generalized organizational scenarios. The method involves defining a variable matrix (wherein each entry of the variable matrix indicate whether a particular virtual machine is to be placed on a particular host server), a first set of variables (wherein each variable of the first set of variables indicate whether a particular host server has at least one virtual machine to be placed thereon), a second set of variables (wherein the second set of variables indicates for all possible pairs of host servers whether two particular host servers both have at least one virtual machine to be placed thereon). The method further involves determining a set of virtual machine to host server allocations by solving a constraints optimization problem over the first set of variables and the second set of variables based on a generalized organizational scenario.