Software-Defined Networking (SDN) provides programmable, flexible and fine-grained traffic control capability, which paves the way for realizing dynamic and high-performance traffic measurement and traffic engineering. In the SDN paradigm, the traffic forwarding and measurement strategies are realized through flow tables stored in the Tenantry Content Addressable Memories (TCAM) of SDN switches. However, the number of TCAM entries in SDN switches is limited. In this paper, we aim to jointly optimize the Traffic Matrix Measurement (TMM) and Traffic Engineering (TE) process under the TCAM capacity and flow aggregation constraints in software-defined networks. We first formulate the joint optimization problem as a Mixed Integer Linear Programming (MILP) model. Then to get an initial traffic matrix for the joint optimization problem, we propose a simple flow rule generation strategy named Maximum Load Rule First (MLRF) to efficiently generate feasible flow rules, which are used to provide direct measurements for the traffic matrix measurement problem. At last, to solve the joint optimization efficiently, we propose two efficient heuristic algorithms named Traffic Matrix Measurement First (TMMF) and Traffic Engineering First (TEF), respectively. TMMF and TEF can generate feasible flow rules for realizing TMM and TE strategies. Our evaluations on real network topologies and traffic traces verify that by jointly optimizing the TMM and TE strategies, both TMMF and TEF can significantly improve TMM accuracy and TE objective (i.e., load balancing) with limited TCAM resource.

Many network inference (NI) problems are modeled as under-determined linear inverse problems where the number of observations or measurements are less than the number of unknowns. In this paper, a new technique for solving NI problems in dynamic network environments is presented. This technique is called optimal-coherent NI (OCNI) and it is applied in two stages. In the first stage, called learning phase, the optimal observation matrix (OOM) of network measurements are computed. In the second stage, called measurement and inference phase (MIP), the OOM is used to compute the least-norm solution and estimate the unknowns of interest. The OOM can be adaptively modified to improve the estimation accuracy. In this paper, first, the principles of OCNI are explained, and its properties are mathematically proved and experimentally justified. In addition, a new framework for traffic matrix estimation in software defined networks (SDN) is developed where the OCNI is the main technique for estimating the size of network flows. This framework is called OCcASION. Under the hard resource constraint of the size of ternary content addressable memory (TCAM) in SDN switches, OCcASION mainly uses the readily and reliably available link-load measurements to estimate the size of network flows where link-loads are provided via simple network management protocol (SNMP). In the learning phase, OCcASION computes the OOM of SNMP link-loads. In the MIP, OCcASION adaptively identifies and measures the most informative flows; moreover, it modifies the original OOM and accurately estimate the unknown traffic matrix. For this purpose, OCcASION uses the flexibility provided by the SDN to adaptively re-program a set of TCAM/flow-table entries of OpenFlow switches. The performance of OCcASION framework is evaluated using synthetic and real traffic traces of three practical networks topologies. It is shown that, this framework can significantly improve the accuracy of the traffic matrix estimation. For example, on Geant network the estimation error is approximately reduced by 83%, compared to regular minimum-norm estimation. Furthermore, the principles of OCNI is applied to estimate network link-delays where, in the learning phase, the OOM is computed using the network topology information. In the MIP, a set of path-delay measurements are measured at each measurement interval, and the OOM is used to coherently estimate unknown link-delays.

Network inference (or tomography) problems, such as traffic matrix estimation or completion and link loss inference, have been studied rigorously in different networking applications. These problems are often posed as under-determined linear inverse (UDLI) problems and solved in a centralized manner, where all the measurements are collected at a central node, which then applies a variety of inference techniques to estimate the attributes of interest. This paper proposes a novel framework for decentralizing these large-scale under-determined network inference problems by intelligently partitioning it into smaller subproblems and solving them independently and in parallel. The resulting estimates, referred to as multiple descriptions, can then be fused together to compute the global estimate. We apply this Multiple Description and Fusion Estimation (MDFE) framework to three classical problems: traffic matrix estimation, traffic matrix completion, and loss inference. Using real topologies and traces, we demonstrate how MDFE can speed up computation while maintaining (even improving) the estimation accuracy and how it enhances robustness against noise and failures. We also show that our MDFE framework is compatible with a variety of existing inference techniques used to solve the UDLI problems.

We revisit the problem of identifying link metrics from end- to-end path measurements in practical IP networks where shortest path routing is the norm. Previous solutions rely on explicit routing techniques (e.g., source routing or MPLS) to construct independent measurement paths for efficient link metric identification. However, most IP networks still adopt shortest path routing paradigm, while the explicit routing is not supported by most of the routers. Thus, this paper studies the link metric identification problem under shortest path routing constraints. To uniquely identify the link metrics, we need to place sufficient number of monitors into the network such that there exist $m$ (the number of links) linear independent shortest paths between the monitors. In this paper, we first formulate the problem as a mixed integer linear programming problem, and then to make the problem tractable in large networks, we propose a Monitor Placement and Measurement Path Selection (MP-MPS) algorithm that adheres to shortest path routing constraints. Extensive simulations on random and real networks show that the MP- MPS gets near-optimal solutions in small networks, and MP- MPS significantly outperforms a baseline solution in large networks.

A key requirement for network management is the accurate and reliable monitoring of relevant network characteristics. In today's large-scale networks, this is a challenging task due to the hard constraints of network measurement resources. This paper proposes a new framework, SNIPER, which leverages the flexibility provided by Software-Defined Networking (SDN) to design the optimal observation or measurement matrix that can leads to the best achievable estimation accuracy using Matrix Completion (MC) techniques. To cope with the complexity of designing large-scale optimal observation matrices, we use the Evolutionary Optimization Algorithms (EOA) which directly target the ultimate estimation accuracy as the optimization objective function. We evaluate the performance of SNIPER using both synthetic and real network measurement traces from different network topologies and by considering two main applications including per-flow size and delay estimations. Our results show that SNIPER can be applied to a variety of network performance measurements under hard resource constraints. For example, by measuring 8.8\% of per-flow path delays in Harvard network, congested paths can be detected with probability 0.94. To demonstrate the feasibility of our framework, we also have implemented a prototype of SNIPER in Mininet.

Fine-grained traffic flow measurement, which provides useful information for network management tasks and security analysis, can be challenging to obtain due to monitoring resource constraints. The alternate approach of inferring flow statistics from partial measurement data has to be robust against dynamic temporal/spatial fluctuations of network traffic. In this paper, we propose an intelligent Traffic (de)Aggregation and Measurement Paradigm (iSTAMP), which partitions TCAM entries of switches/routers into two parts to: 1) optimally aggregate part of incoming flows for aggregate measurements, and 2) de-aggregate and directly measure the most informative flows for per-flow measurements. iSTAMP then processes these aggregate and per-flow measurements to effectively estimate network flows using a variety of optimization techniques. With the advent of Software-Defined-Networking (SDN), such real-time rule (re)configuration can be achieved via OpenFlow or other similar SDN APIs. We first show how to design the optimal aggregation matrix for minimizing the flow-size estimation error. Moreover, we propose a method for designing an efficient-compressive flow aggregation matrix under hard resource constraints of limited TCAM sizes. In addition, we propose an intelligent Multi-Armed Bandit based algorithm to adaptively sample the most “rewarding” flows, whose accurate measurements have the highest impact on the overall flow measurement and estimation performance. We evaluate the performance of iSTAMP using real traffic traces from a variety of network environments and by considering two applications: traffic matrix estimation and heavy hitter detection. Also, we have implemented a prototype of iSTAMP and demonstrated its feasibility and effectiveness in Mininet environment.

We have previously introduced Multiple Description Fusion Estimation (MDFE) framework that partitions a large-scale Under-Determined Linear Inverse (UDLI) problem into smaller sub-problems that can be solved independently and in parallel. The resulting estimates, referred to as multiple descriptions, can then be fused together to compute the global estimate [1]. In this paper, we extend MDFE framework to make it compatible with Compressive Sensing (CS) network inference, where the attributes of interests (i.e. unknowns) are fluctuating rapidly over time and/or space. For this purpose, we propose a new clustering based technique to intelligently divide a large-scale compressive sensing problem into smaller sub-problems where observations between sub-spaces contain redundancy. We apply this new framework, referred to as Compressive Sensing MDFE (CS-MDFE), to three classical inference problems in networking: traffic matrix estimation, traffic matrix completion, and loss inference. Using real topologies and traces, we demonstrate how CS-MDFE can improve the estimation accuracy and speed up computation time, and how it enhances robustness against noise and failures. We also show that this framework is compatible with different CS inference techniques.

Two forms of network inference (or tomography) problems have been studied rigorously: (a) traffic matrix estimation or completion based on link-level traffic measurements, and (b) link-level loss or delay inference based on end-to-end measurements. These problems are often posed as underdetermined linear inverse (UDLI) problems and solved in a centralized manner, where all the measurements are collected at a central node, which then applies a variety of inference techniques to estimate the attributes of interest. This paper proposes a novel framework for decentralizing these large-scale UDLI network inference problems by intelligently partitioning it into smaller sub-problems and solving them independently and in parallel. The resulting estimates, referred to as multiple descriptions, can then be fused together to compute the global estimate. We apply this Multiple Description and Fusion Estimation (MDFE) framework to three classical problems: traffic matrix estimation, traffic matrix completion, and loss inference. Using real topologies and traces, we demonstrate how MDFE can speed up computation time while maintaining (even improving) the estimation accuracy and how it enhances robustness against noise and failures. We also show that our MDFE framework is compatible with a variety of existing inference techniques used to solve the UDLI problems.

High speed communication systems play an important role in the development of advanced networks. The optimum design of these communication links under bandwidth and power constraints needs the use of efficient approaches. In this paper, a novel power efficient modulation scheme is introduced. For this purpose, the modulator at the transmitter uses simple and effective pulse shaping functions to change the amplitude and width of the transmitter pulse. Using this simple method, a high power efficient transceiver structure, with the same performance as typical NRZ signaling, can be designed. Our results show that not only we can reduce the power consumption by more than 10\% without loosing the performance but also the probability of error is improved in the presence of jitter and interference.

An unequal importance communication approach, for cheap, reliable and real-time image communication over heterogeneous networks and its applications in mobile multimedia communication systems is presented. Here, an image is partitioned into different layers of importance: base layer and enhancement layers. The low-rate base layer description, which can be strongly protected, is transmitted through a low-bandwidth, but robust and more expensive channel, supporting delay-sensitive applications (e.g. GSM network), while the high-rate enhancement layer description(s) are transmitted through a high-bandwidth, but high/variable-delay and cheap channel which does not support delay-sensitive applications (e.g. WLAN). In the receiver, when the base layer description is quickly received, an acceptable quality image is reconstructed, and when the enhancement layer description(s) are received a high quality image can be reconstructed. Our practical results indicated that this effective, flexible, and highly compatible method not only facilitates high quality image distribution among heterogeneous networks and increases the coverage, quality of service, and bandwidth and power efficiency but also decreases the delay and cost of image distribution in mobile communication systems.