involves taking actions, but also a series of preparatory works, including learn- ing, analyzing, and decision making. It is almost similar to make commercial plans in industries. Before taking commercial actions, a company should make a deci- sion, while before making a mature decision, a company should do surveys, learn from current or past successes and failures, and then scheme a suitable one for our own. Among numerous branches and minor application areas, all successes and Service Delivery Platforms failures can be deemed as experiences and then the whole process can be considered as a process of machine learning and decision making. Machine learning is programming computers to optimize a performance crite- rion using example data or past experience. It also uses the theory of statistics in building mathematical models [56]. Therefore, machine learning can help to learn from context information and past experiences according to certain performance criterions and then further help improve performances. Decision making can be regarded as an outcome of mental processes (cognitive process) leading to the selection of a course of action among several alternatives. Every decision-making process produces a final choice. The output can be an action or an opinion of choice [57]. Correspondingly, decision making can contribute to further actions, such as adjustment or improvement to implement adaptation to performance requirements. The combination of machine learning and decision making will enable the whole system to perform intelligently to be adaptive to dynamic changes and finally help guarantee QoE and QoS, and also improve real-time performance. These are just the very requirements of Cognitive NGSDP taking into account performance and intelligence. 6.3.4.3 Feasible Solutions Based on the requirements of cognitive functions and theories and technologies of perception and action as well as machine learning and decision making, our research group also proposed feasible solutions to implement the conceptual Cognit