TOWARDS AUTOMATING POLICY- BASED MANAGEMENT SYSTEMS

The goal of distributed systems management is to provide reliable, secure and efficient utilization of the network, processors and devices that comprise those systems. The management system makes use of management agents to collect events and data from managed objects while policies provide information on how to modify the behaviour of a managed system. Systems as well as policies governing the behaviour of the system and its constituents can change dynamically. The aim of this work is to provide the services and algorithms needed to automatically identify and deploy management entities and be able to respond automatically to both changes to the system itself as well as to changes in the way the system is to be managed, i.e., changes to the set of management policies or sets of management agents. One significant challenge in the use of policy-based management systems is finding efficient mechanisms to address and simplify the gap between expressing and specifying policies and an actual configuration of a management system that realizes and makes use of policies. Little work has been done to define how the monitoring operations are to be configured and updated according to the policies. This Thesis proposes a general architecture for a policy-based management system for distributed systems which allows for expressing and automating the deployment of a wide range of management policies. The proposed solution is based on the matching between the management operations that are carried out by the management agents and the policies. The matching process relies on the attributes that the agents can monitor and the extracted attributes from the components of the policies. One major contribution of this Thesis is to build the policy model and services on existing management services found in commercial management systems. The work of this Thesis also focuses in finding87 strategies for selecting and configuring agents to be used to keep the time of a policy deployment low. The Thesis introduces the Policy-Management Agent Integrated Console (PMagic) prototype. The PMagic prototype has been implemented to provide a practical validation of the policy based management system model proposed. The approach, architecture and prototype have demonstrated that it is possible to create a more autonomic management system, particularly one that can instantiate agents to react to changes in sets of policies

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