Abstract: An approach for forecasting demand. The approach includes a method that includes receiving one or more variables associated with an event. The method further includes generating, by at least one computing device, a model to forecast future demand based on the one or more variables. The method further includes determining, by the at least one computing device, a load to provision one or more servers to meet the future demand. The load is based on the model.

Abstract: An approach for forecasting demand. The approach includes a method that includes receiving one or more variables associated with an event. The method further includes generating, by at least one computing device, a model to forecast future demand based on the one or more variables. The method further includes determining, by the at least one computing device, a load to provision one or more servers to meet the future demand. The load is based on the model.

Abstract: An approach for forecasting demand. The approach includes a method that includes receiving one or more variables associated with an event. The method further includes generating, by at least one computing device, a model to forecast future demand based on the one or more variables. The method further includes determining, by the at least one computing device, a load to provision one or more servers to meet the future demand. The load is based on the model.

Abstract: Embodiments of the present invention provide an approach for allocating computing resources based on social networking/media trends in a networked computing environment (e.g., a cloud computing environment). In a typical embodiment, a baseline computing resource allocation will be determined for the networked computing environment based upon historical computing resource data (e.g., stored in at least one computer storage device). Social networking trend data corresponding to usage of a set of social networking websites may be received and analyzed to determine a forecasted computing resource allocation (e.g., based on social networking trends). The baseline computing resource allocation may be compared to the forecasted computing resource allocation to identify any difference therebetween. A computing resource allocation protocol/plan may then be determined based on the comparison (e.g., to address the difference).

Abstract: An approach is provided for allowing a network computing (e.g., cloud computing) infrastructure to modify its resource allocation plan (e.g., an instance count) by using a Kth derivative vector plot, which may be generated using historical logs. Among other things, this approach enables an infrastructure to project an allocation forecast for a specified duration and adapt to changes in network traffic.

Abstract: An approach is provided for allowing a network computing (e.g., cloud computing) infrastructure to modify its resource allocation plan (e.g., an instance count) by using a Kth derivative vector plot, which may be generated using historical logs. Among other things, this approach enables an infrastructure to project an allocation forecast for a specified duration and adapt to changes in network traffic.

Abstract: An approach for forecasting demand. The approach includes a method that includes receiving one or more variables associated with an event. The method further includes generating, by at least one computing device, a model to forecast future demand based on the one or more variables. The method further includes determining, by the at least one computing device, a load to provision one or more servers to meet the future demand. The load is based on the model.

Abstract: An approach is provided for allowing a network computing (e.g., cloud computing) infrastructure to modify its resource allocation plan (e.g., an instance count) by using a Kth derivative vector plot, which may be generated using historical logs. Among other things, this approach enables an infrastructure to project an allocation forecast for a specified duration and adapt to changes in network traffic.

Abstract: An approach is provided for allowing a network computing (e.g., cloud computing) infrastructure to modify its resource allocation plan (e.g., an instance count) by using a Kth derivative vector plot, which may be generated using historical logs. Among other things, this approach enables an infrastructure to project an allocation forecast for a specified duration and adapt to changes in network traffic.

Abstract: Embodiments of the present invention provide an approach for allocating computing resources based on social networking/media trends in a networked computing environment (e.g., a cloud computing environment). In a typical embodiment, a baseline computing resource allocation will be determined for the networked computing environment based upon historical computing resource data (e.g., stored in at least one computer storage device). Social networking trend data corresponding to usage of a set of social networking websites may be received and analyzed to determine a forecasted computing resource allocation (e.g., based on social networking trends). The baseline computing resource allocation may be compared to the forecasted computing resource allocation to identify any difference therebetween. A computing resource allocation protocol/plan may then be determined based on the comparison (e.g., to address the difference).