On the Problem of Optimal Path Encoding for Software-Defined Networks

Packet networks need to maintain state in the form of forwarding tables at each switch. The cost of this state increases as networks support ever more sophisticated per-flow routing, traffic engineering, and service chaining. Per-flow or per-path state at the switches can be eliminated by encoding each packet's desired path in its header. A key component of such a method is an efficient encoding of paths through the network. We introduce a mathematical formulation of this optimal path-encoding problem. We prove that the problem is APX-hard, by showing that approximating it to within a factor less than 8/7 is NP-hard. Thus, at best we can hope for a constant-factor approximation algorithm. We then present such an algorithm, approximating the optimal path-encoding problem to within a factor 2. Finally, we provide empirical results illustrating the effectiveness of the proposed algorithm.Comment: To appear in IEEE/ACM Transactions on Networkin

Design of Virtualized Network Coding Functionality for Reliability Control of Communication Services over Satellite

Network coding (NC) is a novel coding technology that can be seen as a generalization of classic point-to-point coding. As with classic coding, both information theoretical and algebraic views bring different and complementary insights of NC benefits and corresponding tradeoffs. However, the multi-user nature of NC and its inherent applicability across all layers of the protocol stack, call for novel design approaches towards efficient practical implementation of this technology. In this paper, we present a possible way forward to the design of NC as a virtual network functionality offered to the communication service designer. Specifically, we propose the integration of NC and Network Function Virtualization (NFV) architectural designs. The integration is realized as a toolbox of NC design domains that the service designer can use for flow engineering. Our proposed design framework combines network protocol-driven design and system modular-driven design approaches. In particular, the adaptive choice of the network codes and its use for a specific service can then be tailored and optimized depending on the ultimate service intent and underlying (virtualized) system or network. We work out a complete use case where we design geo-network coding, an application of NC for which coding rate is optimized using databases of geo-location information towards an energy-efficient use of resources. Our numerical results highlight the benefits of both the proposed NC design framework and the specific application

Protection Over Asymmetric Channels, S-MATE: Secure Multipath Adaptive Traffic Engineering

Several approaches have been proposed to the problem of provisioning traffic engineering between core network nodes in Internet Service Provider (ISP) networks. Such approaches aim to minimize network delay, increase capacity, and enhance security services between two core (relay) network nodes, an ingress node and an egress node. MATE (Multipath Adaptive Traffic Engineering) has been proposed for multipath adaptive traffic engineering between an ingress node (source) and an egress node (destination) to distribute the network flow among multiple disjoint paths. Its novel idea is to avoid network congestion and attacks that might exist in edge and node disjoint paths between two core network nodes. This paper proposes protection schemes over asymmetric channels. Precisely, the paper aims to develop an adaptive, robust, and reliable traffic engineering scheme to improve performance and reliability of communication networks. This scheme will also provision Quality of Server (QoS) and protection of traffic engineering to maximize network efficiency. Specifically, S-MATE (secure MATE) is proposed to protect the network traffic between two core nodes (routers, switches, etc.) in a cloud network. S-MATE secures against a single link attack/failure by adding redundancy in one of the operational redundant paths between the sender and receiver nodes. It is also extended to secure against multiple attacked links. The proposed scheme can be applied to secure core networks such as optical and IP networks.Comment: 4 figures, 9 pages, journal paper of S-MAT

A Decentralized Approach to Software-Defined Networks (SDNs)

Redistribution of the intelligence and management in the software defined networks (SDNs) is a potential approach to address the bottlenecks of scalability and integrity of these networks. We propose to revisit the routing concept based on the notion of regions. Using basic and consistent definition of regions, a region-based packet routing called SmartRegion Routing is presented. The flexibility of regions in terms of naming and addressing is then leveraged in the form of a region stack among other features placed in the associated packet header. In this way, most of complexity and dynamicity of a network is absorbed, and therefore highly fast and simplified routing at the inter-region level along with semi-autonomous intra-region routing will be feasible. In addition, multipath planning can be naturally realized at both inter and intra levels. A basic form of SmartRegion routing mechanism is provided. Simplicity, scalability, and manageability of the proposed approach would also bring future potentials to reduce energy consumption and environmental footprint associated to the SDNs. Finally, various applications, such as enabling seamless broadband access, providing beyond IP addressing mechanisms, and also address-equivalent naming mechanisms, are considered and discussed.Comment: 26 pages, 5 tables, and 8 figure

Handling Mobility in Dense Networks

Network densification is one of key technologies in future networks to significantly increase network capacity. The gain obtained by network densification for fixed terminals have been studied and proved. However for mobility users, there are a number of issues, such as more frequent handover, packet loss due to high mobility, interference management and so on. The conventional solutions are to handover high speed mobiles to macro base stations or multicast traffic to multiple base stations. These solutions fail to exploit the capacity of dense networks and overuse the backhaul capacity. In this paper we propose a set of solutions to systematically solve the technical challenges of mobile dense networks. We introduce network architecture together with data transmission protocols to support mobile users. A software-defined protocol (SDP) concept is presented so that combinations of transport protocols and physical layer functions can be optimized and triggered on demand. Our solutions can significantly boost performance of dense networks and simplify the packet handling process. Importantly, the gain brought by network densification to fixed users can also be achieved for mobile users

Joint Data Scheduling and FEC Coding for Multihomed Wireless Video Delivery

This paper studies the problem of mobile video delivery in heterogenous wireless networks from a server to multihomed device. Most existing works only consider delivering video streaming on single path which bandwidth is limited causing ultimate video transmission rate. To solve this live video streaming transmission bottleneck problem, we propose a novel solution named Joint Data Allocation and Fountain Coding (JDAFC) method that contain below characters: (1) path selection, (2) dynamic data allocation, and (3) fountain coding. We evaluate the performance of JDAFC by simulation experiments using Exata and JVSM and compare it with some reference solutions. Experimental results represent that JDAFC outperforms the competing solutions in improving the video peak signal-to-noise ratio as well as reducing the end-to-end delay.Comment: 6 page

Progressive Decoding for Data Availability and Reliability in Distributed Networked Storage

To harness the ever growing capacity and decreasing cost of storage, providing an abstraction of dependable storage in the presence of crash-stop and Byzantine failures is compulsory. We propose a decentralized Reed Solomon coding mechanism with minimum communication overhead. Using a progressive data retrieval scheme, a data collector contacts only the necessary number of storage nodes needed to guarantee data integrity. The scheme gracefully adapts the cost of successful data retrieval to the number of storage node failures. Moreover, by leveraging the Welch-Berlekamp algorithm, it avoids unnecessary computations. Compared to the state-of-the-art decoding scheme, the implementation and evaluation results show that our progressive data retrieval scheme has up to 35 times better computation performance for low Byzantine node rates. Additionally, the communication cost in data retrieval is derived analytically and corroborated by Monte-Carlo simulation results. Our implementation is flexible in that the level of redundancy it provides is independent of the number of data generating nodes, a requirement for distributed storage systemsComment: 13 page

Designing Networks: A Mixed-Integer Linear Optimization Approach

Designing networks with specified collective properties is useful in a variety of application areas, enabling the study of how given properties affect the behavior of network models, the downscaling of empirical networks to workable sizes, and the analysis of network evolution. Despite the importance of the task, there currently exists a gap in our ability to systematically generate networks that adhere to theoretical guarantees for the given property specifications. In this paper, we propose the use of Mixed-Integer Linear Optimization modeling and solution methodologies to address this Network Generation Problem. We present a number of useful modeling techniques and apply them to mathematically express and constrain network properties in the context of an optimization formulation. We then develop complete formulations for the generation of networks that attain specified levels of connectivity, spread, assortativity and robustness, and we illustrate these via a number of computational case studies

Applied Erasure Coding in Networks and Distributed Storage

The amount of digital data is rapidly growing. There is an increasing use of a wide range of computer systems, from mobile devices to large-scale data centers, and important for reliable operation of all computer systems is mitigating the occurrence and the impact of errors in digital data. The demand for new ultra-fast and highly reliable coding techniques for data at rest and for data in transit is a major research challenge. Reliability is one of the most important design requirements. The simplest way of providing a degree of reliability is by using data replication techniques. However, replication is highly inefficient in terms of capacity utilization. Erasure coding has therefore become a viable alternative to replication since it provides the same level of reliability as replication with significantly less storage overhead. The present thesis investigates efficient constructions of erasure codes for different applications. Methods from both coding and information theory have been applied to network coding, Optical Packet Switching (OPS) networks and distributed storage systems. The following four issues are addressed: - Construction of binary and non-binary erasure codes; - Reduction of the header overhead due to the encoding coefficients in network coding; - Construction and implementation of new erasure codes for large-scale distributed storage systems that provide savings in the storage and network resources compared to state-of-the-art codes; and - Provision of a unified view on Quality of Service (QoS) in OPS networks when erasure codes are used, with the focus on Packet Loss Rate (PLR), survivability and secrecy

Affine Multiplexing Networks: System Analysis, Learning, and Computation

We introduce a novel architecture and computational framework for formal, automated analysis of systems with a broad set of nonlinearities in the feedback loop, such as neural networks, vision controllers, switched systems, and even simple programs. We call this computational structure an affine multiplexing network (AMN). The architecture is based on interconnections of two basic conceptual building blocks: multiplexers ($\mu$), and affine transformations ($\alpha$). When attached together appropriately, these building blocks translate to conjunctions and disjunctions of affine statements, resulting in an encoding of the network into satisfiability modulo theory (SMT), mixed integer programming, and sequential convex optimization solvers. We show how to formulate and verify system properties like stability and robustness, how to compute margins, and how to verify performance through a sequence of SMT queries. As illustration, we use the framework to verify closed loop, possibly nonlinear dynamical systems that contain neural networks in the loop, and hint at a number of extensions that can make AMNs a potent playground for interfacing between machine learning, control, convex and nonconvex optimization, and formal methods.Comment: 30 pages, 12 figure