Adaptive management for networked systems

Abstract: As networked systems grow in size and dynamicity, management systems must become adaptive to changing networking conditions. The focus of the work presented in this thesis is on developing engineering principles for adaptive management systems. We investigate three problems in the context of adaptive management for networked systems.First, we address the control of the performance of an SMS system. We present a design for policy-based performance management of such systems. The design takes as input the operator's performance goals, which are expressed as policies that can be adjusted at run-time. The system attempts to achieve the given goals by periodically solving an optimization problem that takes as input the policies and traffic statistics and computes a new configuration. We have evaluated the design through extensive simulations in various scenarios and compared it with an ideal system. A prototype has been developed on a commercial SMS platform, which proves the validity of our design.Second, we address the problem of decentralized continuous monitoring of network state variables with configurable accuracy. Network state variables are computed from device counters using aggregation functions, such as SUM, AVERAGE and MAX. We present A-GAP, a protocol that aims at minimizing the management overhead for a configurable average error of the estimation of the global aggregate. The protocol follows the push approach to monitoring and uses the concept of incremental aggregation on a self-stabilizing spanning tree. A-GAP is decentralized and asynchronous to achieve robustness and scalability. We evaluate the protocol through simulation in several scenarios. The results show that we can effectively control the fundamental trade-off in monitoring between accuracy and overhead.Third, we aim at improving the performance of the policy distribution task: the mechanism that provides the right policies at the right locations in the network when they are needed. Policy distribution is a key aspect for developing policy-based systems that scale, which is a must for dynamic scenarios. We present a scalable framework for policy distribution. The framework is based on aggregating the addresses of the policies and applying multipoint communication techniques. We show the validity of the framework in a case study.