No abstract

Active Queue Management (AQM) applies a suitable control policy upon detecting congestion in networks. In this paper, an adaptive Proportional-Integral (PI) controller based on Artificial Neural Networks (ANN) is applied to AQM for the objective of congestion avoidance and control in middle nodes. The proposed controller is simple and can be easily implemented in high-speed routers. Neural Network PI (NNPI) dynamically adapts its parameters with respect to changes in the system. It is anticipated that this results in better response compared to linear controllers due to the nonlinear nature of NNPI. We simulated our method in ns2 and compared its performance with the conventional PI controller. The simulation results show NNPI yields better performance.

No abstract

Coupling processors with acceleration hardware is an effective manner to improve energy efficiency of embedded systems. Many-core is nowadays a dominating design paradigm for SoCs, which opens new challenges and opportunities for designing HW blocks. Exploring acceleration solutions that naturally fit into well-established parallel programming models and that can be incrementally added on top of existing parallel applications is thus extremely important. In this paper we focus on tightly-coupled multi-core cluster architectures, representative of the basic building block of the most recent many-cores, and we enhance it with dedicated HW processing units (HWPU). We propose an architecture where the HWPUs share the same L1 data memory through which processors also communicate, implementing a zero-copy communication model. High-level synthesis (HLS) tools are used to generate HW blocks, then a custom wrapper interfaces the latter to the tightly coupled cluster. We validate our proposal on RTL models, running both synthetic workload and real applications. Experimental results demonstrate that on average our solution provides nearly identical performance to traditional private-memory coarse-grained accelerators, but it achieves up to 32 percent better performance/area/watt and it requires only minimal modifications to legacy parallel codes.

The entities of real-world networks are connected via different types of connections (i.e., layers). The task of link prediction in multiplex networks is about finding missing connections based on both intra-layer and inter-layer correlations. Our observations confirm that in a wide range of real-world multiplex networks, from social to biological and technological, a positive correlation exists between connection probability in one layer and similarity in other layers. Accordingly, a similarity-based automatic general-purpose multiplex link prediction method—SimBins—is devised that quantifies the amount of connection uncertainty based on observed inter-layer correlations in a multiplex network. Moreover, SimBins enhances the prediction quality in the target layer by incorporating the effect of link overlap across layers. Applying SimBins to various datasets from diverse domains, our findings indicate that SimBins outperforms the compared methods (both baseline and state-of-the-art methods) in most instances when predicting links. Furthermore, it is discussed that SimBins imposes minor computational overhead to the base similarity measures making it a potentially fast method, suitable for large-scale multiplex networks.

Networks are invaluable tools to study real biological, social and technological complex systems in which connected elements form a purposeful phenomenon. A higher resolution image of these systems shows that the connection types do not confine to one but to a variety of types. Multiplex networks encode this complexity with a set of nodes which are connected in different layers via different types of links. A large body of research on link prediction problem is devoted to finding missing links in single-layer (simplex) networks. In recent years, the problem of link prediction in multiplex networks has gained the attention of researchers from different scientific communities. Although most of these studies suggest that prediction performance can be enhanced by using the information contained in different layers of the network, the exact source of this enhancement remains obscure. Here, it is shown that similarity w.r.t. structural features (eigenvectors) is a major source of enhancements for link prediction task in multiplex networks using the proposed layer reconstruction method and experiments on real-world multiplex networks from different disciplines. Moreover, we characterize how low values of similarity w.r.t. structural features result in cases where improving prediction performance is substantially hard.

No abstract

An ad hoc network is a dynamically reconfigurable wireless network with no fixed wired infrastructure. The primary concerns in ad hoc networks are bandwidth limitation and unpredictable dynamic topology. The OnDemand Multicast Routing Protocol (ODMRP) was designed for multicast routing in ad hoc networks. In this paper, we propose a cluster-based on demand multicast routing protocol(SC-ODMRP) to the lack of extension of flat multicast routing protocols in large scale ad hoc networks. Simulation results show that using clustering base ODMRP improves network performance in terms of end-to-end delay and control packets. Also we propose a link stability approach to design a stable multicast algorithm. By this approach data delivery will be increase, and overhead will be decrease.