Open AccessAdaptation of MAPE-K and Fuzzy Q-Learning in SLA management
Author(s) -
Ahmad Kamal Ramli,
Mohd Helmy Abd Wahab,
Syed Zulkarnain Syed Idrus
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1529/2/022100
Subject(s) - service level agreement , computer science , fuzzy logic , adaptation (eye) , quality of service , latency (audio) , context (archaeology) , q learning , service level , reinforcement learning , network packet , process (computing) , distributed computing , artificial intelligence , machine learning , computer network , operating system , telecommunications , paleontology , statistics , physics , mathematics , optics , biology
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. 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 can deal with this problem together with the integration of Fuzzy and Q-Learning algorithms. The proposed experiment 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.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.