1. A computer-implemented machine learning method for scheduling a computational task by a scheduling entity in a datacenter environment, wherein the datacenter includes a plurality of computing entities and the scheduling entity includes a first and second parameter processing entities, the method comprising: receiving feedback information from the datacenter by the scheduling entity, wherein the feedback information includes: historical selections of the plurality of computing entities for processing historical computing tasks, and historical processing results of the historical computing tasks as processed by the historical computing entities; receiving and processing at the first parameter processing entity incoming data corresponding to a new computational task to be processed by the datacenter environment, wherein the incoming data includes: a set of computational task parameters specifying characteristics of the new computational task, and a set of computational tasks requirements specifying criteria which have to be fulfilled by a computer entity for processing the new computational task; receiving and processing at the second parameter processing entity a first and second set of computing entity parameters; wherein the first set of computing entity parameters specifies characteristics of the computing entities of the plurality of computing entities, and the second set of computing entity parameters specifies load parameters of the plurality of computing entities; providing processing result from the first and second parameter processing entities to machine logic of the scheduling entity; identifying, by a self-learning mechanism of the scheduling entity based on the processing result and the feedback information, features from one or more computing entities among the plurality of computing entities that have similar characteristics to the received set of computing entity parameters in the processing result; creating a contextual model that corresponds to the identified features of the one or more of the computing entities; responsive to detecting changes in the information deriving from the received feedback information, the processing result from the first and second parameter processing entities, and the created contextual model, modifying the set of identified features by increasing or decreasing the effect of one or more parameters; selecting, by machine logic of the scheduling entity, one or more computing entities of the plurality of computing entities for processing the new computational task based, at least in part, upon: the processing result from the first parameter processing entity, the processing result from the second parameter entity, the received feedback information, and the created contextual model; and processing the computational task on the selected one or more computing entities.
1. A computer-implemented machine learning method for scheduling a computational task by a scheduling entity in a datacenter environment, wherein the datacenter includes a plurality of computing entities and the scheduling entity includes a first and second parameter processing entities, the method comprising: receiving feedback information from the datacenter by the scheduling entity, wherein the feedback information includes: historical selections of the plurality of computing entities for processing historical computing tasks, and historical processing results of the historical computing tasks as processed by the historical computing entities; receiving and processing at the first parameter processing entity incoming data corresponding to a new computational task to be processed by the datacenter environment, wherein the incoming data includes: a set of computational task parameters specifying characteristics of the new computational task, and a set of computational tasks requirements specifying criteria which have to be fulfilled by a computer entity for processing the new computational task; receiving and processing at the second parameter processing entity a first and second set of computing entity parameters; wherein the first set of computing entity parameters specifies characteristics of the computing entities of the plurality of computing entities, and the second set of computing entity parameters specifies load parameters of the plurality of computing entities; providing processing result from the first and second parameter processing entities to machine logic of the scheduling entity; identifying, by a self-learning mechanism of the scheduling entity based on the processing result and the feedback information, features from one or more computing entities among the plurality of computing entities that have similar characteristics to the received set of computing entity parameters in the processing result; creating a contextual model that corresponds to the identified features of the one or more of the computing entities; responsive to detecting changes in the information deriving from the received feedback information, the processing result from the first and second parameter processing entities, and the created contextual model, modifying the set of identified features by increasing or decreasing the effect of one or more parameters; selecting, by machine logic of the scheduling entity, one or more computing entities of the plurality of computing entities for processing the new computational task based, at least in part, upon: the processing result from the first parameter processing entity, the processing result from the second parameter entity, the received feedback information, and the created contextual model; and processing the computational task on the selected one or more computing entities. 3. The method according to claim 1 , wherein the self-learning mechanism includes a self-learning scheduling mechanism capability that takes into account the feedback information, in the selection of the selected one or more computing entities.