In one embodiment, a method includes training a deep neural network using a first set of network characteristics corresponding to a first time and a second set of network characteristics corresponding to a second time, generating, using the deep neural network, a predictive set of network characteristics corresponding to a future time, and assigning a task of a distributed application to a processing unit based on the predictive set of network characteristics.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method comprising: training a deep neural network using a first set of network characteristics corresponding to a first time and a second set of network characteristics corresponding to a second time; generating, using the deep neural network, a predictive set of network characteristics corresponding to a future time; and assigning a task of a distributed application to a processing unit based on the predictive set of network characteristics. 2. The method of claim 1, further comprising monitoring execution of the distributed application within a network to generate the first set of network characteristics and the second set of network characteristics. 3. The method of claim 1, wherein the first set of network characteristics includes at least one value indicative of data rate, a round-trip travel time, a traffic type or priority, or storage utilization. 4. The method of claim 1, wherein assigning the task includes selecting the processing unit based on the predictive set of network characteristics. 5. The method of claim 1, wherein assigning the task includes migrating the task to the processing unit from a second processing unit based on the predictive set of network characteristics. 6. The method of claim 1, wherein assigning the task includes generating the task from a problem to be solved by the distributed application based on the predictive set of network characteristics. 7. The method of claim 1, wherein assigning the task includes assigning a speculative task to the processing unit based on the predictive set of network characteristics. 8. The method of claim 1, wherein the deep neural network is a recurrent neural network. 9. The method of claim 8, wherein the deep neural network is a long short term memory neural network. 10. The method of claim 1, wherein the deep neural network includes a plurality of deep neural network layers. 11. The method of claim 10, wherein the plurality of deep neural network layers includes a first sparse coding layer configured to generate first features of the first set of network characteristics and second features of the second set of network characteristics, a second sparse coding layer configured to generate interrelationships between the first features and second features, and a predictive layer configured to generate at least one of the predictive set of network characteristics. 12. A system comprising: one or more processors; and a non-transitory memory comprising instructions that when executed cause the one or more processors to perform operations comprising: training a deep neural network using a first set of network characteristics corresponding to a first time and a second set of network characteristics corresponding to a second time; generating, using the deep neural network, a predictive set of network characteristics corresponding to a future time; and assigning a task of a distributed application to a processing unit based on the predictive set of network characteristics. 13. The system of claim 12, wherein the operations further comprise monitoring execution of the distributed application within a network to generate the first set of network characteristics and the second set of network characteristics. 14. The system of claim 12, wherein assigning the task includes selecting the processing unit based on the predictive set of network characteristics. 15. The system of claim 12, wherein assigning the task includes migrating the task to the processing unit from a second processing unit based on the predictive set of network characteristics. 16. The system of claim 12, wherein assigning the task includes generating the task from a problem to be solved by the distributed application based on the predictive set of network characteristics. 17. The system of claim 12, wherein assigning the task includes assigning a speculative task to the processing unit based on the predictive set of network characteristics. 18. A system comprising: a monitoring system configured to train a deep neural network using a first set of network characteristics corresponding to a first time and a second set of network characteristics corresponding to a second time and to generate, using the deep neural network, a predictive set of network characteristics corresponding to a future time; and means for assigning a task of a distributed application to a processing unit based on the predictive set of network characteristics. 19. The system of claim 18, wherein the means for assigning the task includes means for generating the task based on the predictive set of network characteristics. 20. The system of claim 18, wherein the means for assigning the task includes means for allocating the task based on the predictive set of network characteristics.