Abstract: Disclosed are various embodiments for the planning of resources used in computing. Usage statistics regarding one or more virtual machine instances executing in a networked plurality of computing devices are obtained. The usage statistics are grouped, for example, based on one or more customer usage classifications, thereby producing one or more usage groups. A corresponding demand forecast is generated for each of the usage groups. A projected demand for one or more physical components of the networked computing devices is calculated according to the demand forecasts.

Abstract: A storage system includes a plurality of data vats, and a processor including an optimizing unit that optimizes a value of data stored in the storage system. The optimizing unit optimizes the value by computing and implementing an optimal decision for allocating new data to a first data vat of the plurality of data vats, moving existing data from at least a second data vat of the plurality of data vats to the first data vat, and deleting existing data from the first data vat, based on an amount of data in each of the plurality of data vats.

Abstract: Disclosed are various embodiments for the planning of resources used in computing. Usage statistics regarding one or more machine instances executing in a plurality of networked computing devices are obtained. The usage statistics are grouped based at least in part on one or more customer usage classifications, thereby producing one or more usage groups. A corresponding demand forecast is generated for each of the usage groups. A projected demand for one or more physical components of the networked computing devices is calculated according to the demand forecasts.

Abstract: A storage system includes a plurality of data vats, and a processor including an optimizing unit that optimizes a value of data stored in the storage system. The optimizing unit optimizes the value by computing and implementing an optimal decision for allocating new data to a first data vat of the plurality of data vats, moving existing data from at least a second data vat of the plurality of data vats to the first data vat, and deleting existing data from the first data vat, based on an amount of data in each of the plurality of data vats.

Abstract: Techniques for heuristic customer order fulfillment planning. A method may include iteratively partitioning ordered items into shipment groups according to a set partition algorithm. Of a working set including the unassigned items, the algorithm may determine the subset that optimizes fulfillment cost per member of the subset, and may assign this subset to a shipment group. To determine the subset, the algorithm may iterate over the set of combinations of fulfillment decision variables. For each combination, a list of members of the working set that may be shipped according to the combination is generated. The subset of this list that optimizes, per member of the subset, the costs of shipping according to the combination is then identified.

Abstract: A method (and system) of storing data in a value-based storage system, includes optimizing a value of data stored in the value-based storage system.

Abstract: A method (and system) of storing data in a value-based storage system, includes optimizing a value of data stored in the value-based storage system.