Efficient packet delivery in modern communication networks

Modern communication networks are often designed for diverse applications, such as voice, data and video. Packet-switching is often adapted in today’s networks to transmit multiple types of traffic. In packet-switching networks, network performance is directly affected by how the networks handle their packets. This work addresses the packet-handling issues from the following two aspects: Quality of Service (QoS) and network coding. QoS has been a well-addressed issue in the study of IP-based networks. Generally, nodes in a network need to be informed of the state of each communication link in order to make intelligent decisions to route packets according to their QoS demands. The link state can, however, change rapidly in a network; therefore, nodes would have to receive frequent link state updates in order to maintain the latest link state information at all times. Frequent link state updating is resource-consuming and hence impractical in network design. Therefore, there is a trade-off between the link state updating frequency and the QoS routing performance. It is necessary to design a link state update algorithm that utilizes less frequent link state updates to achieve a high degree of satisfaction in QoS performance. The first part of this work addresses this link state update problem and provides two solutions: ROSE and Smart Packet Marking. ROSE is a class-based link state update algorithm, in which the class boundaries are designed based on the statistical data of users’ QoS requests. By doing so, link state update is triggered only when certain necessary conditions are met. For example, if the available bandwidth of a link is fluctuating within a range that is higher than the highest possible bandwidth request, there is no need to update the state of this link. Smart Packet Marking utilizes a similar concept like ROSE, except that the link state information is carried in the probing packet sent in conjunction with each connection request instead of through link state updates. The second part of this work addresses the packet-handling issue by means of network coding. Instead of the traditional store-and-forward approach, network coding allows intermediate nodes in a multi-hop path to code multiple packets into one in order to reduce bandwidth consumption. The coded packet can later be decoded by its recipients to retrieve the original plain packet. Network coding is found to be beneficial in many network applications. This dissertation makes contributions in network coding in two areas: peer-to-peer file sharing and wireless ad-hoc networks. The benefit of network coding in peer-to-peer file sharing networks is analyzed, and a network coding algorithm – Downloader-Initiated Random Linear Network Coding (DRLNC) – is proposed. DLRNC shifts the coding decision from the seeders to the leechers; by doing so it solves the “collision” problem without increasing the field size. In wireless network coding, this work addresses the implementation difficulty pertaining to MAC layer scheduling. To achieve the ideal network coding gain in wireless networks, it requires perfect MAC layer scheduling. This dissertation first provides an algorithm to solve the ideal-case MAC layer scheduling problem. Since the ideal MAC layer schedule is often difficult to realize, a practical approach is then proposed to increase the network coding performance by modifying the ACK packets in the 802.11 MAC

Mobility modeling and management for next generation wireless networks

Mobility modeling and management in wireless networks are the set of tasks performed in order to model motion patterns, predict trajectories, get information on mobiles\u27 whereabouts and to make use of this information in handoff, routing, location management, resource allocation and other functions. In the literature, the speed of mobile is often and misleadingly referred to as the level of mobility, such as high or low mobility. This dissertation presents an information theoretic approach to mobility modeling and management, in which mobility is considered as a measure of uncertainty in mobile\u27s trajectory, that is, the mobility is low if the trajectory of a mobile is highly predictable even if the mobile is moving with high speed. On the other hand, the mobility is high if the trajectory of the mobile is highly erratic. Based on this mobility modeling concept, we classify mobiles into predictable and non-predictable mobility classes and optimize network operations for each mobility class. The dynamic mobility classification technique is applied to various mobility related issues of the next generation wireless networks such as location management, location-based services, and energy efficient routing in multihop cellular networks

Scalability of Information Centric Networking Using Mediated Topology Management

Information centric networking is a new concept that places emphasis on the information items themselves rather than on where the information items are stored. Consequently, routing decisions can be made based on the information items rather than on simply destination addresses. There are a number of models proposed for information centric networking and it is important that these models are investigated for their scalability if we are to move from early prototypes towards proposing that these models are used for networks operating at the scale of the current Internet. This paper investigates the scalability of an ICN system that uses mediation between information providers and information consumers using a publish/subscribe delivery mechanism. The scalability is investigated by extrapolating current IP traffic models for a typical national-scale network provider in the UK to estimate mediation workload. The investigation demonstrates that the mediation workload for route determination is on a scale that is comparable to, or less than, that of current IP routing while using a forwarding mechanism with considerably smaller tables than current IP routing tables. Additionally, the work shows that this can be achieved using a security mechanism that mitigates against maliciously injected packets thus stopping attacks such as denial of service that is common with the current IP infrastructure

Improved learning automata applied to routing in multi-service networks

Multi-service communications networks are generally designed, provisioned and configured, based on source-destination user demands expected to occur over a recurring time period. However due to network users' actions being non-deterministic, actual user demands will vary from those expected, potentially causing some network resources to be under- provisioned, with others possibly over-provisioned. As actual user demands vary over the recurring time period from those expected, so the status of the various shared network resources may also vary. This high degree of uncertainty necessitates using adaptive resource allocation mechanisms to share the finite network resources more efficiently so that more of actual user demands may be accommodated onto the network. The overhead for these adaptive resource allocation mechanisms must be low in order to scale for use in large networks carrying many source-destination user demands. This thesis examines the use of stochastic learning automata for the adaptive routing problem (these being adaptive, distributed and simple in implementation and operation) and seeks to improve their weakness of slow convergence whilst maintaining their strength of subsequent near optimal performance. Firstly, current reinforcement algorithms (the part causing the automaton to learn) are examined for applicability, and contrary to the literature the discretised schemes are found in general to be unsuitable. Two algorithms are chosen (one with fast convergence, the other with good subsequent performance) and are improved through automatically adapting the learning rates and automatically switching between the two algorithms. Both novel methods use local entropy of action probabilities for determining convergence state. However when the convergence speed and blocking probability is compared to a bandwidth-based dynamic link-state shortest-path algorithm, the latter is found to be superior. A novel re-application of learning automata to the routing problem is therefore proposed: using link utilisation levels instead of call acceptance or packet delay. Learning automata now return a lower blocking probability than the dynamic shortest-path based scheme under realistic loading levels, but still suffer from a significant number of convergence iterations. Therefore the final improvement is to combine both learning automata and shortest-path concepts to form a hybrid algorithm. The resulting blocking probability of this novel routing algorithm is superior to either algorithm, even when using trend user demands

VINEA: a policy-based virtual network embedding architecture

Network virtualization has enabled new business models by allowing infrastructure providers to lease or share their physical network. To concurrently run multiple customized virtual network services, such infrastructure providers need to run a virtual network embedding protocol. The virtual network embedding is the (NP-hard) problem of matching constrained virtual networks onto the physical network. We present the design and implementation of a policy-based architecture for the virtual network embedding problem. By policy, we mean a variant aspect of any of the (invariant) embedding mechanisms: resource discovery, virtual network mapping, and allocation on the physical infrastructure. Our architecture adapts to different scenarios by instantiating appropriate policies, and has bounds on embedding efficiency and on convergence embedding time, over a single provider, or across multiple federated providers. The performance of representative novel policy configurations are compared over a prototype implementation. We also present an object model as a foundation for a protocol specification, and we release a testbed to enable users to test their own embedding policies, and to run applications within their virtual networks. The testbed uses a Linux system architecture to reserve virtual node and link capacities.National Science Foundation (CNS-0963974

Towards Automated Network Configuration Management

Modern networks are designed to satisfy a wide variety of competing goals related to network operation requirements such as reachability, security, performance, reliability and availability. These high level goals are realized through a complex chain of low level configuration commands performed on network devices. As networks become larger, more complex and more heterogeneous, human errors become the most significant threat to network operation and the main cause of network outage. In addition, the gap between high-level requirements and low-level configuration data is continuously increasing and difficult to close. Although many solutions have been introduced to reduce the complexity of configuration management, network changes, in most cases, are still manually performed via low--level command line interfaces (CLIs). The Internet Engineering Task Force (IETF) has introduced NETwork CONFiguration (NETCONF) protocol along with its associated data--modeling language, YANG, that significantly reduce network configuration complexity. However, NETCONF is limited to the interaction between managers and agents, and it has weak support for compliance to high-level management functionalities. We design and develop a network configuration management system called AutoConf that addresses the aforementioned problems. AutoConf is a distributed system that manages, validates, and automates the configuration of IP networks. We propose a new framework to augment NETCONF/YANG framework. This framework includes a Configuration Semantic Model (CSM), which provides a formal representation of domain knowledge needed to deploy a successful management system. Along with CSM, we develop a domain--specific language called Structured Configuration language to specify configuration tasks as well as high--level requirements. CSM/SCL together with NETCONF/YANG makes a powerful management system that supports network--wide configuration. AutoConf supports two levels of verifications: consistency verification and behavioral verification. We apply a set of logical formalizations to verifying the consistency and dependency of configuration parameters. In behavioral verification, we present a set of formal models and algorithms based on Binary Decision Diagram (BDD) to capture the behaviors of forwarding control lists that are deployed in firewalls, routers, and NAT devices. We also adopt an enhanced version of Dyna-Q algorithm to support dynamic adaptation of network configuration in response to changes occurred during network operation. This adaptation approach maintains a coherent relationship between high level requirements and low level device configuration. We evaluate AutoConf by running several configuration scenarios such as interface configuration, RIP configuration, OSPF configuration and MPLS configuration. We also evaluate AutoConf by running several simulation models to demonstrate the effectiveness and the scalability of handling large-scale networks

Engineering technology for networks

Space Network (SN) modeling and evaluation are presented. The following tasks are included: Network Modeling (developing measures and metrics for SN, modeling of the Network Control Center (NCC), using knowledge acquired from the NCC to model the SNC, and modeling the SN); and Space Network Resource scheduling