Abstract: A genetic algorithm is described to determine a near-optimal schedule for assigning heterogeneous computations to resources in a heterogeneous computational environment. The genetic algorithm evolves a computation-to-compute resource mapping optimized with respect to a set of inter-dependent, and possibly conflicting objectives including cost of computation, data transfer cost, time to complete computation, profitability, etc. A set of scheduling plans are randomly created and then evaluated and assigned a fitness value based on the ability to meet a set of weighted objectives. Scheduling plans having desirable fitness values are selected as parents to procreate one or more new scheduling plans, each new plan inheriting resource mappings from at least two parents. This evolutionary process is repeated until the fitness values across scheduling plans converge or a time threshold is exceeded. At the end of evolution, a scheduling plan with the best assigned value is chosen for scheduling.

Abstract: A genetic algorithm is described to determine a near-optimal schedule for assigning heterogeneous computations to resources in a heterogeneous computational environment. The genetic algorithm evolves a computation-to-compute resource mapping optimized with respect to a set of inter-dependent, and possibly conflicting objectives including cost of computation, data transfer cost, time to complete computation, profitability, etc. A set of scheduling plans are randomly created and then evaluated and assigned a fitness value based on the ability to meet a set of weighted objectives. Scheduling plans having desirable fitness values are selected as parents to procreate one or more new scheduling plans, each new plan inheriting resource mappings from at least two parents. This evolutionary process is repeated until the fitness values across scheduling plans converge or a time threshold is exceeded. At the end of evolution, a scheduling plan with the best assigned value is chosen for scheduling.

Abstract: A genetic algorithm is described to determine a near-optimal schedule for assigning heterogeneous computations to resources in a heterogeneous computational environment. The genetic algorithm evolves a computation-to-compute resource mapping optimized with respect to a set of inter-dependent, and possibly conflicting objectives including cost of computation, data transfer cost, time to complete computation, profitability, etc. A set of scheduling plans are randomly created and then evaluated and assigned a fitness value based on the ability to meet a set of weighted objectives. Scheduling plans having desirable fitness values are selected as parents to procreate one or more new scheduling plans, each new plan inheriting resource mappings from at least two parents. This evolutionary process is repeated until the fitness values across scheduling plans converge or a time threshold is exceeded. At the end of evolution, a scheduling plan with the best assigned value is chosen for scheduling.

Abstract: An apparatus for communicating between an access endpoint and a mobile device endpoint comprising a network service center attached to the access endpoint. A memory stores a prototype corpus representing historical communication information. The network service center includes a processor to train the prototype corpus and create a plurality of records including a protocol trigger, training data and a user dictionary. The access endpoint sends the training data to endpoints to produce training data specific to classes of endpoints. The access endpoint and the mobile device endpoint communicate with one another, and the network service center collects communication data related to the communications interaction and stores the communication data in the memory. The processor generates feedback data based at least in part of the communication data. Advantages of the invention include the ability for network operators to optimize communication techniques based on network traffic.