The Orchestration Layer of the Future Internet platform (see section 2.2) is the most interesting one from a control perspective since it includes the cognitive features of the overall architecture.
Figure 32 Proposed Future Internet Architecture
Here the Data Analytics and QoE Evaluation/Control Subsystem (see Figure 33), implementing the QoE Management framework, is presented and discussed.
The proposed approach for coping with QoE Management in this context is to implement a modular and cognitive architecture where the QoE Management functionalities consist of a QoE Evaluator and a QoE Controller, which can be designed independently from one another.
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Figure 33 Architecture of the Data Analytics and QoE Evaluation/Control Subsystem
Such block is composed of the following subsystems:
▪ The Context Engine, which receives in real-time the Monitored Metadata from the NFV Infrastructure as well as the metadata relevant to the Service Parameters and the Users’ Feedbacks (e.g., via social networks) from the Service Management Layer. The Context Engine is, therefore, in charge of the formal description, the appropriate aggregation and the semantic enrichment of all the received metadata, eventually producing the so-called Present Context, i.e., a real-time multi-layer, multi-network and technology-independent structured record of the present state, characterizing each service session enjoyed by a user over a certain network. The Context Engine is also in charge of continuously feeding a Knowledge Database which stores all the updates of the Present Context.
▪ The Data Analytics Subsystem, which is in charge of performing the analysis of the data stored in the Knowledge Database (which can be considered as “Big Data”). This subsystem includes properly designed pattern recognition techniques (e.g., Support Vector Machine algorithms), aimed at inferring the so-called Ad-Hoc Profiles (each profile corresponding to a suitable cluster of users presenting similar session records), as well as the personalized QoE desired by a single user while enjoying a given service (Target QoE).
▪ The QoE Evaluator, which is in charge of assessing, in real-time, for each user enjoying a given service, the so-called Perceived QoE, i.e., the QoE that is currently being perceived by the user. Such computations are to be performed based on a suitable set of personalized QoE Utility Functions depending on the Present Context and on the Ad-Hoc Profiles, as explained in the next section.
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▪ The QoE Controller is aimed at the satisfaction of the personalized QoE requirements, namely at the minimization, for each running service and for each user, of the personalized QoE Error defined as the difference between the Perceived QoE (input from the QoE Evaluator) and the Target QoE (input from Data Analytics) of the considered service. The QoE Controller is in charge of the real-time computation of proper QoE Driving Parameters, namely personalized performance target values, which will then be exploited in order to ensure the desired minimization of the QoE Error associated with each service. As a result, the overall architecture will employ the dynamically deduced QoE Driving Parameters not only to drive the QoS performance of the underlying telecommunication networks, but also to ensure real-time control of the security and mobility performance of the network and/or cloud infrastructures, as well as the real-time control of the QoE Management and service delivery procedures. ▪ The Resource and Service Control Subsystem operates based on the Present Context (input from
the Context Engine) and of the QoE Driving Parameters (input from the QoE Controller). It consists of a set of cooperative and technology-independent control functionalities in charge of making appropriate coordinated and technology-neutral Control Decisions which will then affect the underlying network infrastructures, as well as of producing automatic Service Notifications associated with the detection of network/service/computing anomalies due to security problems or faults. The Service Notifications will eventually trigger suitable adaptation or reconfiguration which will be properly enforced either by the SDN-enabled NFV Layer (if the detected anomalies are related to the underlying network and/or cloud infrastructures), or by the Service Management Layer (otherwise). The Control Decisions are responsible for driving the network resources of the underlying telecommunication networks and the computing/storage resources of the underlying cloud infrastructures to reach the performance target values provided by the QoE Driving Parameters, while simultaneously ensuring efficient resource exploitation. In this respect, such Control Decisions will specify what actions will have to be enforced by the Ad-Hoc Actuation functionalities in terms of scheduling, admission control, selection of the telecommunication domains which will have to support the admission of the requested service, traffic load balancing intra-domain routing, inter-domain handovers, etc.
From the above discussions, it should be clear that the Orchestrator plays the “key role” of control: The Data Analytics and QoE Evaluation/Control Subsystems “rearrange” the feedback variables (to provide the Feedback Parameters represented by the Present Context) and generate the target reference values (namely, the Target QoE). Furthermore, the Orchestrator, based on the Feedback Parameters, produces the control variables, namely the Control Decisions impacting on the SDN-enabled NFV Layer and the Service Notifications impacting on the Service Management Layer.