Maestro: Achieving scalability and coordination in centralizaed network control plane

Modem network control plane that supports versatile communication services (e.g. performance differentiation, access control, virtualization, etc.) is highly complex. Different control components such as routing protocols, security policy enforcers, resource allocation planners, quality of service modules, and more, are interacting with each other in the control plane to realize complicated control objectives. These different control components need to coordinate their actions, and sometimes they could even have conflicting goals which require careful handling. Furthermore, a lot of these existing components are distributed protocols running on large number of network devices. Because protocol state is distributed in the network, it is very difficult to tightly coordinate the actions of these distributed control components, thus inconsistent control actions could create serious problems in the network. As a result, such complexity makes it really difficult to ensure the optimality and consistency among all different components. Trying to address the complexity problem in the network control plane, researchers have proposed different approaches, and among these the centralized control plane architecture has become widely accepted as a key to solve the problem. By centralizing the control functionality into a single management station, we can minimize the state distributed in the network, thus have better control over the consistency of such state. However, the centralized architecture has fundamental limitations. First, the centralized architecture is more difficult to scale up to large network size or high requests rate. In addition, it is equally important to fairly service requests and maintain low request-handling latency, while at the same time having highly scalable throughput. Second, the centralized routing control is neither as responsive nor as robust to failures as distributed routing protocols. In order to enhance the responsiveness and robustness, one approach is to achieve the coordination between the centralized control plane and distributed routing protocols. In this thesis, we develop a centralized network control system, called Maestro, to solve the fundamental limitations of centralized network control plane. First we use Maestro as the central controller for a flow-based routing network, in which large number of requests are being sent to the controller at very high rate for processing. Such a network requires the central controller to be extremely scalable. Using Maestro, we systematically explore and study multiple design choices to optimally utilize modern multi-core processors, to fairly distribute computation resource, and to efficiently amortize unavoidable overhead. We show a Maestro design based on the abstraction that each individual thread services switches in a round-robin manner, can achieve excellent throughput scalability while maintaining far superior and near optimal max-min fairness. At the same time, low latency even at high throughput is achieved by Maestro's workload-adaptive request batching. Second, we use Maestro to achieve the coordination between centralized controls and distributed routing protocols in a network, to realize a hybrid control plane framework which is more responsive and robust than a pure centralized control plane, and more globally optimized and consistent than a pure distributed control plane. Effectively we get the advantages of both the centralized and the distributed solutions. Through experimental evaluations, we show that such coordination between the centralized controls and distributed routing protocols can improve the SLA compliance of the entire network

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial

Self-Organized Routing For Wireless Micro-Sensor Networks

In this paper we develop an energy-aware self-organized routing algorithm for the networking of simple battery-powered wireless micro-sensors (as found, for example, in security or environmental monitoring applications). In these networks, the battery life of individual sensors is typically limited by the power required to transmit their data to a receiver or sink. Thus effective network routing algorithms allow us to reduce this power and extend both the lifetime and the coverage of the sensor network as a whole. However, implementing such routing algorithms with a centralized controller is undesirable due to the physical distribution of the sensors, their limited localization ability and the dynamic nature of such networks (given that sensors may fail, move or be added at any time and the communication links between sensors are subject to noise and interference). Against this background, we present a distributed mechanism that enables individual sensors to follow locally selfish strategies, which, in turn, result in the self-organization of a routing network with desirable global properties. We show that our mechanism performs close to the optimal solution (as computed by a centralized optimizer), it deals adaptively with changing sensor numbers and topology, and it extends the useful life of the network by a factor of three over the traditional approach