Abstract: Servers are allocated for use in one of a plurality of compute-resources or for stand-by storage in a free-pool. Server load metrics are selected (e.g., ping-reply time or CP utilization) for measuring load in the servers. Metrics are measured for the servers allocated to the compute-resources. Several metrics can be measured simultaneously. The metrics for each compute-resource are normalized and averaged. Then, the metrics for each compute-resource are combined using weighting coefficients, producing a global load value, G, for each compute-resource. The G value is recalculated at timed intervals. Upper and lower thresholds are set for each compute-resource, and the G values are compared to the thresholds. If the G value exceeds the upper threshold, then a server in the free-pool is reallocated to the compute-resource; if the G value is less than the lower threshold, then a server is moved from the compute-resource to the free-pool.

Abstract: A method (and system) for managing and controlling allocation and de-allocation of resources based on a guaranteed amount of resource and additional resources based on a best effort for a plurality of customers, includes dynamically allocating server resources for a plurality of customers, such that the resources received by a customer are dynamically controlled and the customer receives a guaranteed minimum amount of resources as specified under a service level agreement (SLA). The best effort is defined in the SLA as a range of service to be provided to the customer if server resources are currently available.

Abstract: A method (and system) for managing and controlling allocation and de-allocation of resources based on a guaranteed amount of resource and additional resources based on a best effort for a plurality of customers, includes dynamically allocating server resources for a plurality of customers, such that the resources received by a customer are dynamically controlled and the customer receives a guaranteed minimum amount of resources as specified under a service level agreement (SLA). The best effort is defined in the SLA as a range of service to be provided to the customer if server resources are currently available.

Abstract: Servers are allocated for use in one of a plurality of compute-resources or for stand-by storage in a free-pool. Server load metrics are selected (e.g., ping-reply time or CP utilization) for measuring load in the servers. Metrics are measured for the servers allocated to the compute-resources. Several metrics can be measured simultaneously. The metrics for each compute-resource are normalized and averaged. Then, the metrics for each compute-resource are combined using weighting coefficients, producing a global load value, G, for each compute-resource. The G value is recalculated at timed intervals. Upper and lower thresholds are set for each compute-resource, and the G values are compared to the thresholds. If the G value exceeds the upper threshold, then a server in the free-pool is reallocated to the compute-resource; if the G value is less than the lower threshold, then a server is moved from the compute-resource to the free-pool.

Abstract: A method (and system) for managing and controlling allocation and de-allocation of resources based on a guaranteed amount of resource and additional resources based on a best effort for a plurality of customers, includes dynamically allocating server resources for a plurality of customers, such that the resources received by a customer are dynamically controlled and the customer receives a guaranteed minimum amount of resources as specified under a service level agreement (SLA).

Abstract: Servers are allocated for use in one of a plurality of compute-resources or for stand-by storage in a free-pool. Server load metrics are selected (e.g., ping-reply time or CP utilization) for measuring load in the servers. Metrics are measured for the servers allocated to the compute-resources. Several metrics can be measured simultaneously. The metrics for each compute-resource are normalized and averaged. Then, the metrics for each compute-resource are combined using weighting coefficients, producing a global load value, G, for each compute-resource. The G value is recalculated at timed intervals. Upper and lower thresholds are set for each compute-resource, and the G values are compared to the thresholds. If the G value exceeds the upper threshold, then a server in the free-pool is reallocated to the compute-resource; if the G value is less than the lower threshold, then a server is moved from the compute-resource to the free-pool.

Abstract: A method (and system) for managing and controlling allocation and de-allocation of resources based on a guaranteed amount of resource and additional resources based on a best effort for a plurality of customers, includes dynamically allocating server resources for a plurality of customers, such that the resources received by a customer are dynamically controlled and the customer receives a guaranteed minimum amount of resources as specified under a service level agreement (SLA).

Abstract: A method (and system) for managing and controlling allocation and de-allocation of resources based on a guaranteed amount of resource and additional resources based on a best effort for a plurality of customers, includes dynamically allocating server resources for a plurality of customers, such that the resources received by a customer are dynamically controlled and the customer receives a guaranteed minimum amount of resources as specified under a service level agreement (SLA).

Abstract: Exemplary embodiments include a method for implementing business process modules for performing business process modeling. The method includes identifying tasks required in order to achieve a capability and designing a process module for enabling the capability. The designing includes interconnecting logic flow among the tasks resulting in an optimized, repeatable pattern of logically transformed inputs to outputs required for achieving the capability. The method also includes selecting and associating attributes to the tasks. The attributes are selected from categories including: information technology component services, data, operational business rules, roles, and measurements. The method further includes defining and associating metadata with the process module. The metadata describes functional capabilities provided by the process module and business and technical contexts into which the process module is used.

Abstract: Exemplary embodiments include a method for performing business process modeling. The method includes identifying capabilities, activities, and requirements associated with a business problem or an opportunity. The activities are operable for implementing the capabilities and the capabilities are operable for resolving the business problem or exploiting the opportunity. For each of the activities, the method includes selecting at least one corresponding process module that includes listings of attributes applicable to the activities, selecting at least one of the attributes from the listings, and defining transition artifacts for integrating selected process modules. The method also includes compiling the transition artifacts, selected attributes, and selected activities with corresponding process modules, and generating a business process model as a result of the compilation.

Abstract: A highly scalable system and method for supporting (mim,max) based Service Level Agreements (SLA) on outbound bandwidth usage for a plurality of customers whose applications (e.g.,Web sites) are hosted by a server farm that consists of a very large number of servers. The system employs a feedback system that enforces the outbound link bandwidth SLAs by regulating the inbound traffic to a server or server farm. Inbound traffic is admitted to servers using a rate denoted as Rt(i,j), which is the amount of the ith customer's jth type of traffic that can be admitted within a service cycle time to servers which support the ith customer. A centralized device computes Rt(i,j) based on the history of admitted inbound traffic to servers, the history of generated outbound traffic from servers, and the SLAs of various customers. The Rt(i,j) value is then relayed to one or more inbound traffic limiters that regulate the inbound traffic using the rates Rt(i,j) in a given service cycle time.