SDN Enabled Network Efficient Data Regeneration for Distributed Storage Systems

Distributed Storage Systems (DSSs) have seen increasing levels of deployment in data centers and in cloud storage networks. DSS provides efficient and cost-effective ways to store large amount of data. To ensure reliability and resilience to failures, DSS employ mirroring and coding schemes at the block and file level. While mirroring techniques provide an efficient way to recover lost data, they do not utilize disk space efficiently, resulting in large overheads in terms of data storage. Coding techniques on the other hand provide a better way to recover data as they reduce the amount of storage space required for data recovery purposes. However, the current recovery process for coded data is not efficient due to the need to transfer large amounts of data to regenerate the data lost as a result of a failure. This results in significant delays and excessive network traffic resulting in a major performance bottleneck. In this thesis, we propose a new architecture for efficient data regeneration in distribution storage systems. A key idea of our architecture is to enable network switches to perform network coding operations, i.e., combine packets they receive over incoming links and forward the resulting packet towards the destination and do this in a principled manner. Another key element of our framework is a transport-layer reverse multicast protocol that takes advantage of network coding to minimize the rebuild time required to transmit the data by allowing more efficient utilization of network bandwidth. The new architecture is supported using the principles of Software Defined Networking (SDN) and making extensions where required in a principled manner. To enable the switches to perform network coding operations, we propose an extension of packet processing pipeline in the dataplane of a software switch. Our testbed experiments show that the proposed architecture results in modest performance gains

Towards a Principled Wireless Support in SDN

Software Defined Networking (SDN) has recently emerged as a transformational tool to design and operate communication networks and services. While the SDN approach has significant benefits for both wireline and wireless radio networks, the support for wireless networks in SDN technologies is still in its infancy as compared to wired networks. One of the key features of SDN is that networks can be managed in a programmatic manner. The challenge for building such a model for wireless radio networks is that there is a plethora of radio protocols that need to be supported, each having its own nuances. To address this, we need to build fundamental abstractions that provide enough visibility so that a programmer can implement protocols, while at the same time being rigid enough not to expose excessive details that will complicate the application development process. The purpose of this work is to introduce a principled approach towards building a cross-layer architecture for wireless networks so that they can receive the same level of programmability as wireline interfaces. Specifically we aim to integrate wireless protocols into the general SDN framework and to provide a logical and consistent view of physical layer radio resources. This is achieved by proposing a new set of abstractions and their interfaces based upon existing SDN terminology and the basic building blocks of Software Defined Radio (SDR) in wireless devices. We validate our approach by implementing our design as an extension of an existing OpenFlow data plane and deploying it in an IEEE 802.11 accesspoint as well as in a typical SDR system

Energy Efficient Designs for Collaborative Signal and Information Processing inWireless Sensor Networks

Collaborative signal and information processing (CSIP) plays an important role in the deployment of wireless sensor networks. Since each sensor has limited computing capability, constrained power usage, and limited sensing range, collaboration among sensor nodes is important in order to compensate for each other’s limitation as well as to improve the degree of fault tolerance. In order to support the execution of CSIP algorithms, distributed computing paradigm and clustering protocols, are needed, which are the major concentrations of this dissertation. In order to facilitate collaboration among sensor nodes, we present a mobile-agent computing paradigm, where instead of each sensor node sending local information to a processing center, as is typical in the client/server-based computing, the processing code is moved to the sensor nodes through mobile agents. We further conduct extensive performance evaluation versus the traditional client/server-based computing. Experimental results show that the mobile agent paradigm performs much better when the number of nodes is large while the client/server paradigm is advantageous when the number of nodes is small. Based on this result, we propose a hybrid computing paradigm that adopts different computing models within different clusters of sensor nodes. Either the client/server or the mobile agent paradigm can be employed within clusters or between clusters according to the different cluster configurations. This new computing paradigm can take full advantages of both client/server and mobile agent computing paradigms. Simulations show that the hybrid computing paradigm performs better than either the client/server or the mobile agent computing. The mobile agent itinerary has a significant impact on the overall performance of the sensor network. We thus formulate both the static mobile agent planning and the dynamic mobile agent planning as optimization problems. Based on the models, we present three itinerary planning algorithms. We have showed, through simulation, that the predictive dynamic itinerary performs the best under a wide range of conditions, thus making it particularly suitable for CSIP in wireless sensor networks. In order to facilitate the deployment of hybrid computing paradigm, we proposed a decentralized reactive clustering (DRC) protocol to cluster the sensor network in an energy-efficient way. The clustering process is only invoked by events occur in the sensor network. Nodes that do not detect the events are put into the sleep state to save energy. In addition, power control technique is used to minimize the transmission power needed. The advantages of DRC protocol are demonstrated through simulations