We present a control plane architecture to accelerate multicast and incast traffic delivery for data-intensive applications in cluster-computing interconnection networks. The architecture is experimentally examined by enabling physical layer optical multicasting on-demand for the application layer to achieve non-blocking performance.

Core and aggregation optical networks are remarkably static, despite the emerging dynamic capabilities of the individual optical devices. This stems from the inability to address optical impairments in real-time. As a result, tasks such as adding and removing wavelengths take a substantial amount of time, and therefore, optical networks are over-provisioned and inefficient in terms of capacity and energy. Optical Performance Monitors (OPMs) that assess the Quality of Transmission (QoT) in real-time can be used to overcome these inefficiencies. However, prior work mostly focused on the single link level. In this paper, we present a network-wide optimization algorithm that leverages OPM measurements to dynamically control the wavelengths' power levels. Hence, it allows adding and dropping wavelengths quickly while mitigating the impacts of impairments caused by these actions, thereby facilitating efficient operation of higher layer protocols. We evaluate the algorithm's performance using a network-scale optical simulator under real-world scenarios and show that the ability to add and drop wavelengths dynamically can lead to significant power savings. Moreover, we experimentally evaluate the algorithm in an optical testbed and discuss the practical implementation issues. To the best of our knowledge, this paper is the first attempt at providing a global power control algorithm that uses live OPM measurements to enable dynamic optical networking.

Core and aggregation optical networks are remarkably static, despite the emerging dynamic capabilities of the individual optical devices. This stems from the inability to address optical impairments in real-time. As a result, tasks such as adding and removing wavelengths take a substantial amount of time, and therefore, optical networks are over-provisioned and inefficient in terms of capacity and energy. Optical Performance Monitors (OPMs) that assess the Quality of Transmission (QoT) in real-time can be used to overcome these inefficiencies. However, prior work mostly focused on the single link level. In this paper, we present a network-wide optimization algorithm that leverages OPM measurements to dynamically control the wavelengths' power levels. Hence, it allows adding and dropping wavelengths quickly while mitigating the impacts of impairments caused by these actions, thereby facilitating efficient operation of higher layer protocols. We evaluate the algorithm's performance using a network-scale optical simulator under real-world scenarios and show that the ability to add and drop wavelengths dynamically can lead to significant power savings. Moreover, we experimentally evaluate the algorithm in an optical testbed and discuss the practical implementation issues. To the best of our knowledge, this paper is the first attempt at providing a global power control algorithm that uses live OPM measurements to enable dynamic optical networking.

Efficient operation of wireless networks and switches requires using simple (and in some cases distributed) scheduling algorithms. In general, simple greedy algorithms (known as Greedy Maximal Scheduling, or GMS) are guaranteed to achieve only a fraction of the maximum possible throughput (e.g., 50% throughput in switches). However, it was recently shown that in networks in which the Local Pooling conditions are satisfied, GMS achieves 100% throughput. Moreover, in networks in which the σ-Local Pooling conditions hold, GMS achieves σ% throughput. In this paper, we focus on identifying the specific network topologies that satisfy these conditions. In particular, we provide the first characterization of all the network graphs in which Local Pooling holds under primary interference constraints (in these networks, GMS achieves 100% throughput). This leads to a linear-time algorithm for identifying Local-Pooling-satisfying graphs. Moreover, by using similar graph-theoretical methods, we show that in all bipartite graphs (i.e., input-queued switches) of size up to 7 ×n, GMS is guaranteed to achieve 66% throughput, thereby improving upon the previously known 50% lower bound. Finally, we study the performance of GMS in interference graphs and show that in certain specific topologies, its performance could be very bad. Overall, the paper demonstrates that using graph-theoretical techniques can significantly contribute to our understanding of greedy scheduling algorithms.

The performance of many algorithms in dynamic networks depends on the properties of the underlying graph representing the network. Since such a graph is inherently time-varying, quantifying the change in its structure is important for understanding the behavior of higher-layer network algorithms. In this paper, we study change in the dynamic graph structure of mobile wireless networks that evolve over time due to node mobility. We define several graph evolution metrics and evaluate them through extensive numerical simulations under Levy Walk mobility, which has been shown previously to have similarities to human mobility patterns. Based on the mean and distribution of these metrics, we obtain important insights into the properties of the evolving graph generated by Levy Walk mobility, and then compare the results to the Random Waypoint mobility model. Finally, we discuss the effects of the rate of graph change on the performance of network applications such as data routing and flooding. Our results suggest that the proposed metrics are viable for quantitatively measuring the magnitude of change in a sequence of evolving graphs.

Efficient operation of wireless networks and switches requires using simple (and in some cases distributed) scheduling algorithms. In general, simple greedy algorithms (known as Greedy Maximal Scheduling - GMS) are guaranteed to achieve only a fraction of the maximum possible throughput (e.g., 50% throughput in switches). However, it was recently shown that in networks in which the Local Pooling conditions are satisfied, GMS achieves 100% throughput. Moreover, in networks in which the ¿-Local Pooling conditions hold, GMS achieves ¿% throughput. In this paper, we focus on identifying the specific network topologies that satisfy these conditions. In particular, we provide the first characterization of all the network graphs in which Local Pooling holds under primary interference constraints (in these networks GMS achieves 100% throughput). This leads to a linear time algorithm for identifying Local Pooling-satisfying graphs. Moreover, by using similar graph theoretical methods, we show that in all bipartite graphs (i.e., input-queued switches) of size up to 7 × n, GMS is guaranteed to achieve 66% throughput, thereby improving upon the previously known 50% lower bound. Finally, we study the performance of GMS in interference graphs and show that in certain specific topologies its performance could be very bad. Overall, the paper demonstrates that using graph theoretical techniques can significantly contribute to our understanding of greedy scheduling algorithms.

A ring oscillator-based true-random number generator design (Rings design) was introduced in Sunar et al. [2007]. The design was rigorously analyzed under a simple mathematical model and its performance characteristics were established. In this article we focus on the practical aspects of the Rings design on a reconfigurable logic platform and determine their implications on the earlier analysis framework. We make recommendations for avoiding pitfalls in real-life implementations by considering ring interaction, transistor-level effects, narrow signal rejection, transmission line attenuation, and sampler bias. Furthermore, we present experimental results showing that changing operating conditions such as the power supply voltage or the operating temperature may affect the output quality when the signal is subsampled. Hence, an attacker may shift the operating point via a simple noninvasive influence and easily bias the TRNG output. Finally, we propose modifications to the design which significantly improve its robustness against attacks, alleviate implementation-related problems, and simultaneously improve its area, throughput, and power performance.