Current Issue : October - December Volume : 2020 Issue Number : 4 Articles : 5 Articles
Optical Burst Switching (OBS) paradigm coupled with Dense Wavelength Division Multiplexing (DWDM) has become a practical\ncandidate solution for the next-generation optical backbone networks. In its practical deployment only the edge nodes are\nprovisioned with buffering capabilities, whereas all interior (core) nodes remain buffer-less. In that way the implementation\nbecomes quite simple as well as cost effective as there will be no need for optical buffers in the interior. However, the buffer-less\nnature of the interior nodes makes such networks prone to data burst contention occurrences that lead to a degradation in overall\nnetwork performance as a result of sporadic heavy burst losses. Such drawbacks can be partly countered by appropriately\ndimensioning available network resources and reactively by way of deflecting excess as well as contending data bursts to available\nleast-cost alternate paths. However, the deflected data bursts (traffic) must not cause network performance degradations in the\ndeflection routes. Because minimizing contention occurrences is key to provisioning a consistent Quality of Service (QoS), we\ntherefore in this paper propose and analyze a framework (scheme) that seeks to intelligently deflect traffic in the core network such\nthat QoS degradations caused by contention occurrences are minimized. This is by way of regulated deflection routing (rDr) in\nwhich neural network agents are utilized in reinforcing the deflection route choices at core nodes. The framework primarily relies\non both reactive and proactive regulated deflection routing approaches in order to prevent or resolve data burst contentions.\nSimulation results show that the scheme does effectively improve overall network performance when compared with existing\ncontention resolution approaches. Notably, the scheme minimizes burst losses, end-to-end delays, frequency of contention\noccurrences, and burst deflections....
Constantly faster, mobile terminals are developing, as well as wireless networks, to satisfy the growth of â??Always Best Connectedâ?\ndemand. Users nowadays want to access the best available wireless network, either from 3GPP or IEEE group technologies,\nwherever they are, without losing their sessions. Consequently, mobile terminals must seamlessly transfer the communications to\nanother access technology (vertical handover) if needed, as they often move into heterogeneous wireless environments. This work\naims to optimize the network selection step in the vertical handover process. Multiattribute Decision-Making methods naturally\nfit this context. Nevertheless, they make wrong handover decisions sometimes, due to imprecise data collected from the metrics.\nThis manuscript presents the use of a hybrid method, combining the fuzzy technique for order preference by similarity to the ideal\nsituation and fuzzy analytic network process, in the network selection, to improve the quality of service and avoid, as much as\npossible, unnecessary handovers. The results demonstrate that this combination is the best, compared to the other methods of the\nsame type in the network selection context....
In recent years, the convolutional neural network (CNN) has made remarkable achievements in semantic segmentation. The\nmethod of semantic segmentation has a desirable application prospect. Nowadays, the methods mostly use an encoder-decoder\narchitecture as a way of generating pixel by pixel segmentation prediction. The encoder is for extracting feature maps and\ndecoder for recovering feature map resolution. An improved semantic segmentation method on the basis of the encoderdecoder\narchitecture is proposed. We can get better segmentation accuracy on several hard classes and reduce the computational\ncomplexity significantly. This is possible by modifying the backbone and some refining techniques. Finally, after some\nprocessing, the framework has achieved good performance in many datasets. In comparison with the traditional architecture,\nour architecture does not need additional decoding layer and further reuses the encoder weight, thus reducing the complete\nquantity of parameters needed for processing. In this paper, a modified focal loss function is also put forward, as a replacement\nfor the cross-entropy function to achieve a better treatment of the imbalance problem of the training data. In addition, more\ncontext information is added to the decode module as a way of improving the segmentation results. Experiments prove that the\npresented method can get better segmentation results. As an integral part of a smart city, multimedia information plays an\nimportant role. Semantic segmentation is an important basic technology for building a smart city...
Despite the existence of several metrics to perform measurements on out-of-order packets, few works have used these metrics for\ncomparative purposes. A potential reason for this is that the use of these simple singleton metrics makes it difficult to analyze all\nthe effects of packet reordering. On the other hand, more complete metrics are represented in a vectorial manner, making\ncomparative analysis challenging. In this paper, we present a scenario for testing and describe a methodology for conducting\nexperiments to compare network paths with respect to unordered packets. The results of several simulations explore simple packet\nreordering metrics derived from vector metric that may allow future work to be benchmarked against. We demonstrated the\nbehaviour of some TCP congestion control algorithms by adjusting different levels of reordering. We highlight good results with\nthe Entropy reorder metric....
We have investigated dynamics of the Internet performance through the assessment of scaling features of a network ICMP echo\nmechanism or pinging. Time series of round-trip times (RTT) from the host computer to 5 destination hosts and back, recorded\nduring three consecutive days and nights, have been used. To assess correlation and scaling features of network echo mechanism,\nwe used method of detrended fluctuation analysis (DFA) for RTTdata sets. It was shown that for different, 10 minute long periods\nof day and night observations, RTTdata sets mostly fluctuate within a narrow range, though sometimes we observe strong sharp\nspikes. RTT variations mostly reveal persistent behavior. DFA fluctuation curves often are characterized by crossovers indication\nstronger or lesser changes in the dynamics of network performance. Distribution function of DFA scaling exponents of considered\nRTT time series mostly was asymmetric with long tail on the right hand side. Dynamical changes occurring in the scaling features\nof Internet network as assessed by RTT fluctuations do not depend on the location of the host and destination nodes. Larger delays\nin round-trip time responses make the scaling behavior of the RTT series complicated and strongly influence their long range\ncorrelation features....
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