Cognitive self-management for voip quality of service in wireless networks: design and performance evaluation

Στα πλαίσια της παρούσας διπλωματικής εργασίας, σχεδιάστηκε και υλοποιήθηκε ένα αλγοριθμικό πλαίσιο για την εγγύηση ποιότητας υπηρεσίας (QoS) φωνής (VoIP) στο ασύρματο περιβάλλον WiMAX. Πιο συγκεκριμένα, αναπτύχθηκε ένας μηχανισμός λήψης απόφασης για την επιλογή της καταλληλότερης ενέργειας προσαρμογής κάτω από συνθήκες φόρτου. Οι πιθανές ενέργειες προσαρμογής είναι η αλλαγή της προτεραιότητας των πακέτων φωνής στον σταθμό βάσης και η αλλαγή κωδικοποίησης της φωνητικής υπηρεσίας. Για τον υπολογισμό της έντασης κάθε ενέργειας προσαρμογής αναπτύχθηκαν μια ευριστική μέθοδος και μια μέθοδος βασισμένη στο ιστορικό παλαιοτέρων ενεργειών. Επιπρόσθετα, υλοποιήθηκε ένας μηχανισμός ανατροφοδότησης του συστήματος προκειμένου να γίνει αυτο-ρύθμιση των κατωφλίων απόφασης με χρήση μηχανικής μάθησης. Η απόδοση του υλοποιημένου πλαισίου αξιολογήθηκε στις εγκαταστάσεις του έργου Panlab το οποίο διέθεσε τον κατάλληλο εξοπλισμό για την ανάπτυξη ενός πειραματικού δικτύου WiMAX. Τα αποτελέσματα αποδεικνύουν ότι οι παράγοντες που επηρεάζουν την ποιότητα υπηρεσίας, όπως απώλεια πακέτων (packet loss), χρονοκαθυστέρηση (delay), διακύμανση της χρονοκαθυστέρησης (jitter) και ο συνδυαστικός παράγοντας R-score βελτιώνονται σημαντικά χρησιμοποιώντας το προτεινόμενο αλγοριθμικό πλαίσιο. Τέλος, θίγονται και αναλύονται ζητήματα εφαρμογής και δυναμικής λειτουργίας του συστήματος.Modern services pose strict requirements on fulfilling their quality indicators, with network operators struggling to increase the provided resources. Sophisticated performance management is needed for autonomic and efficient configuration of available network resources. The incorporation of cognitive capabilities in network management and its cooperation with the service stratum, provide the means for the development of novel performance management solutions. In this work, we propose an algorithmic framework for VoIP QoS assurance in a wireless broadband network environment. We introduce a decision making scheme for the selection of the most appropriate adaptation under congestion conditions, choosing between VoIP flows’ priority change at the wireless base station and the change of VoIP flows’ codec. A History-based method calculates the intensity of each adaptation, while a heuristic approach is used for un-classified situations. The proposed learning scheme, based on the feedback of previous actions, self-tunes the decision making tasks. We have implemented and empirically evaluated the solution in FIRE Panlab experimental facility using a WiMAX network. The results show that VoIP QoS features (packet loss, delay, jitter, R-score) are significantly improved via the proposed solution, satisfying adaptive evolution requirements. Applicability issues and the dynamic operation of the system are also analysed

Service Level Agreement-based adaptation management for Internet Service Provider (ISP) using Fuzzy Q-learning

Internet access is the vital catalyst for online users, and the number of mobile subscribers is predicted to grow from dramatically in the next few years. This huge demand is the main issue facing the Internet Service Providers (ISPs) who need to handle users’ expectations along with their current resources. An adaptive mechanism within the ISPs architecture is a promising solution to handle such situation. A Service Level Agreement (SLA)is the legal catalyst to monitor any contract violation between end users and ISPs and is embedded within a Quality of Service (QoS) framework. It strengthens and advances the quality of control over the user’s application and network resources and can be further stretched to fulfill the QoS terms through negotiation and re-negotiation. Moreover, the present literature does not focus on the combination of rule-based approaches and adaptation together to update the established learning repository. Therefore, this mainstream of this research in the context of SLAs is to fill in this gap by addressing the combination of rule-base uncertainties and iteration of the learning ability. The key to the proposed architecture is the utilization of self - * capabilities designed to have self-management over uncertainties and the provision of self-adaptive interactions. Thus, the Monitor, Analyse, Plan, Execute and Knowledge Base (MAPE-K) approach is able to deal with this problem together with the integration of Fuzzy and Q-Learning algorithms. The proposed architecture is in the context of autonomic computing. An adaptation manager is the main proposed component to update admission control on the ISP current resources and the ability to manage SLAs. A general methodology type-2 fuzzy logic is applied to ensure the uncertainties and precise decision-making are well addressed in this research. The proposed solution, demonstrating Q-Learning works adaptive with QoS parameters, e.g. Latency, Availability and Packet Loss. With the combination of fuzzy and Q-Learning, we demonstrate that the proposed adaptation manager is able to handle the uncertainties and learning abilities. Q-Learning is able to identify the initial state from various ISPs iterations and update them with appropriate actions, reflecting the reward configurations. The higher the iterations process the higher is the increase the learning ability,rewards and exploration probability. The research outcomes benefit the SLA framework by incorporating the information for SLA policies and Service Level Objectives (SLOs). Lastly, an important contribution is the ability to demonstrate that the MAPE-K approach is a contender for ISP SLA-based frameworks for QoS provision