Abstract: A method for allocating resources for a plurality of time intervals, including: receiving a forecasted workload and at least one required service metric value; applying a search algorithm to identify an initial allocation assignment; inputting the assignment to a machine learning algorithm, the machine learning algorithm trained on historic data of past intervals; predicting an expected service metric value provided by the initial allocation assignment; adjusting the initial allocation assignment based on a difference between the expected service metric value and the corresponding required service metric value; iteratively repeating the applying, inputting, predicting, and adjusting operations until one of: the expected service metric value predicted for an adjusted allocation assignment is within a predetermined distance of the corresponding at least one required service metric value for the interval; or a predetermined time has elapsed.

Abstract: A method for allocating resources for a plurality of time intervals, including: receiving a forecasted workload and at least one required service metric value; applying a search algorithm to identify an initial allocation assignment; inputting the assignment to a machine learning algorithm, the machine learning algorithm trained on historic data of past intervals; predicting an expected service metric value provided by the initial allocation assignment; adjusting the initial allocation assignment based on a difference between the expected service metric value and the corresponding required service metric value; iteratively repeating the applying, inputting, predicting, and adjusting operations until one of: the expected service metric value predicted for an adjusted allocation assignment is within a predetermined distance of the corresponding at least one required service metric value for the interval; or a predetermined time has elapsed.