The Performance of Q-Learning within SDN Controlled Static and Dynamic Mesh Networks

Randomized EEG functional brain networks in major depressive disorders with greater resilience and lower rich-club coefficient

The link prediction problem is a time-evolving model in network science that has simultaneously abetted myriad applications and experienced extensive methodological improvement. Inferring the possibility of emerging links in dynamic social networks, also known as the dynamic link prediction task, is complex and challenging. In contrast to the link prediction in cross-sectional networks, dynamic link prediction methods need to cater to the actor-level temporal changes and associated evolutionary information regarding their micro- (i.e., link formation/deletion) and mesoscale (i.e., community formation) network structure. With the advent of abundant community detection algorithms, the research community has examined community-aware link prediction strategies in static networks. However, the same task in dynamic networks where, apart from the actors and links among them, their community pattern is also dynamic, is yet to be explored. Evolutionary community-aware information, including the associated link structure and temporal neighborhood changes, can effectively be mined to build dynamic similarity metrics for dynamic link prediction. This study aims to develop and integrate such dynamic features with machine learning algorithms for link prediction tasks in dynamic social networks. It also compares the performances of these features against well-known similarity metrics (i.e., ResourceAllocation) for static networks and a time series-based link prediction strategy in dynamic networks. These proposed features achieved high-performance scores, representing them as prospective candidates for both dynamic link prediction tasks and modeling the network growth.

A very efficient and well-known computing environment is Mobile Adhoc Network (MANET). However, the node capacity and delay are affected during the data transmission due to its mobility. To tackle this problem, Multi-criteria Enhanced Optimal Capacity-Delay Tradeoff with Efficient Fuzzy-based Multi-constraint Multicast Routing (MEOCDT-EFM2R) method has been proposed to solve the optimal route selection problem where the multiple network metrics are taken into consideration. However, it requires fault tolerance-based Cluster Head (CH) selection since it does not handle any transmission or link failure in the network. Therefore in this paper, Efficient Fuzzy-based Multi-constraint Multicast Fault Tolerance Routing (MEOCDT-EFM2FTR) method is proposed to efficiently handle the transmission failure in the network. In this method, the CH for each cluster is chosen depending upon on the maximum nodal weight which is stored in the routing table. Initially, each and every node computes its weight with different network metrics like residual energy, mobility speed, computational power and fault tolerance. Then, the node with the highest weight is elected as CH of a specific cluster and each CH coordinates itself as a ring to tolerate any transmission failure by choosing a new CH. So, the new CH takes the responsibility of initial CH to complete the transmission without degrading the network performance. Finally, the simulation gives the evidence for the performance effectiveness of the MEOCDT-EFM2FTR method compared to the MEOCDT-EFM2R for throughput, routing overhead, delay, etc.

Building and analyzing the US airport network based on en-route location information

IP multicast dictates high-end routers and incurs high administrative overhead, which prevent them from being deployed in many video streaming scenarios. In this paper, we study the problem of computing the multipath multicast routes for streaming videos in Software-Defined Networks (SDNs), which adopt less expensive switches and reduce administrative overhead for lower CAPEX/OPEX. The objectives of the considered problem are robustness, load balance, SDN compatibility, and adaptiveness. We formulate this routing problem into a mathematical optimization problem, and propose two algorithms to address this problem. We implement the proposed algorithms on a popular OpenFlow controller to demonstrate its practicality, and we conduct extensive experiments to evaluate the proposed algorithms. The experiment results clearly show the merits of our algorithms over the IP multicast, e.g., we observe: (i) frame loss rate reduction between 19% and 95%, (ii) video quality improvement between 4 dB and 15 dB, (iii) sink throughput increase between 25% and 66%, and (iv) maximal link utilization reduction between 15% and 50%. We also show the tradeoff between optimality and run time of the two proposed algorithms: one of them is more suitable for smaller and more static networks, and the other one is more suitable for larger and more dynamic networks.

Mesh-Mon: A multi-radio mesh monitoring and management system

Major depressive disorder (MDD) is a common and intractable mood disorder. Electroconvulsive therapy (ECT) is a common means of brain electrical stimulation for the treatment of MDD, but the neurobiological mechanism of its clinical symptom relief effect is still to be explored. This study aims to explore how ECT plays a role in depression remissions by investigating the changes of static and dynamic brain network characteristics in MDD patients before and after ECT. Resting-state functional magnetic resonance imaging (fMRI) scans were obtained from nine MDD patients twice before and after a full course of ECT, all of whom responded to ECT as defined by at least a 50% reduction from baseline Hamilton Depression Scale (HAMD) scores. Both static and dynamic characteristics of the functional brain network were compared between the pre- and post-ECT scans for all participants, and the correlations between changes in clinical symptoms and altered network metrics were also investigated. The clustering coefficient and local efficiency in static brain networks were increased significantly, while the global flexibility of dynamic brain networks was decreased significantly after ECT. Several regions of interest (ROIs) that changed significantly at the local level were also identified, which involved regions of the cerebellum, hippocampus as well as frontal and temporal cortices. Although not significant, the decrease of HAMD scores were associated with trends of changed network metrics after ECT. Our results suggest that ECT may alleviate the depressive symptoms of MDD by decreasing the randomness of the brain network as reflected by changes in both static and dynamic network properties and that the temporal gyrus, frontal gyrus, hippocampus, and cerebellar regions may play key roles in such mechanisms. These findings have important implications for our understandings of ECT and depression. However, this study is limited by a relatively small sample size and the results should be confirmed in larger samples.

We are presenting a real-time traffic flow classification model for maintaining QoS in dynamic networks such as Software Defined Networks (SDN). In previous works, we managed to achieve high accuracy (90–95%) on the database of flows known for the model using Machine Learning (Supervised Learning) methods but in a dynamic SDN new network applications and flows appear more often than usual. For detection of new flows it is proposed to use the Agglomerative clustering method, which has never been used to solve the problem of network flow classification, because early approaches to traffic clustering gave insufficient results and the speed of its operation was too low. This paper offers a combination of different Machine Learning methods in such a way that Agglomerative clustering is responsible only for updating the class database, and Supervised Learning methods are responsible for quickly classifying known flows, which solves the problem of model speed. Clustering accuracy is improved by automatically controlling the cluster construction process by determining the distances between flows using the Random Forest and Extra Trees methods. In the experimental part of the study, three more most promising ways of determining distances are given for comparison: Random Trees Embedding, Euclidean and Manhattan distance. Results of clustering of TCP and UDP applications for different number of clusters and different size of the initial sample are presented. Experimental studies confirm the effectiveness of using hierarchical clustering in traffic clustering tasks under the condition of controlled cluster construction.

Source routing is the routing scheme that arranges the whole path from source to target at the origin node that may suit the requirements from the upper layer applications' perspective.The centralized control in SDN (Software-Defined Networking) networks enables the awareness of the global topology at the controller.Therefore, augmented source routing schemes can be designed to achieve various purposes.This paper proposes a source routing scheme that conducts the top-K QoS-aware paths discovery in SDN.First, the novel non-invasive QoS over LLDP scheme is designed to collect QoS information based on LLDP in a piggyback fashion.Then, variations of the KSP (K Shortest Paths) algorithm are derived to find the unconstrained/constrained top-K ranked paths with regard to individual/overall path costs, reflecting the Quality of Service.The experiment results show that the proposed scheme can efficiently collect the QoS information and find the top-K paths.Also, the performance of our scheme is applicable in QoS-sensitive application scenarios compared with previous works.

Abstract. The objective of the article is to systematize the dynamic and especially static entrepreneurial networks as they look, appear, and act in a country with so-called emerging markets. This is accomplished on the example of Russia. Special attention is paid to relevant business goals and patterns to typical members of such networks, to why, when and how one passes from outsourcing in dynamic networks to static networking.The article contains a field research based on (1) access to public databases like Spark-Interfax in Russia, with updated information on registered firms (their founders, affiliated companies, businesses) and (2) interviews with businessmen who agreed to comment on the problems under discussion.As regards the results of the research, two points seem to be especially important. First, when, according to legislation, information on registered firms and their statistical reports is public, it is not too problematic to find out and monitor the activities of practically all types of business static networks in all industries. Second, when the business environment becomes more competitive, dynamic networks tend to be transformed into static ones not only due to considerable contract risks, but also because one needs to select from wider samples of possible suppliers the most competitive and those who are ready to invest into the technological innovations.The general conclusion is that in countries with a dynamic economic growth and increasing competitiveness of the business environment, like in Russia, this advantage of static networking might prevail, especially if static networks appear as a result of selecting business partners from the firm’s experience in outsourcing.Key words: static and dynamic networks, outsourcing, information networking, affiliated companiesp>

Community structure is one of the fundamental characteristics of complex networks. Many methods have been proposed for community detection. However, most of these methods are designed for static networks and are not suitable for dynamic networks that evolve over time. Recently, the evolutionary clustering framework was proposed for clustering dynamic data, and it can also be used for community detection in dynamic networks. In this paper, a multi-similarity spectral (MSSC) method is proposed as an improvement to the former evolutionary clustering method. To detect the community structure in dynamic networks, our method considers the different similarity metrics of networks. First, multiple similarity matrices are constructed for each snapshot of dynamic networks. Then, a dynamic co-training algorithm is proposed by bootstrapping the clustering of different similarity measures. Compared with a number of baseline models, the experimental results show that the proposed MSSC method has better performance on some widely used synthetic and real-world datasets with ground-truth community structure that change over time.

Software Defined Networking (SDN) is developing as a new solution for the development and innovation of the Internet. SDN is expected to be the ideal future for the Internet, since it can provide a controllable, dynamic, and cost-effective network. The emergence of SDN provides a unique opportunity to achieve network security in a more efficient and flexible manner. However, SDN also has original structural vulnerabilities, which are the centralized controller, the control-data interface and the control-application interface. These vulnerabilities can be exploited by intruders to conduct several types of attacks. In this paper, we propose a deep learning (DL) approach for a network intrusion detection system (DeepIDS) in the SDN architecture. Our models are trained and tested with the NSL-KDD dataset and achieved an accuracy of 80.7% and 90% for a Fully Connected Deep Neural Network (DNN) and a Gated Recurrent Neural Network (GRU-RNN), respectively. Through experiments, we confirm that the DL approach has the potential for flow-based anomaly detection in the SDN environment. We also evaluate the performance of our system in terms of throughput, latency, and resource utilization. Our test results show that DeepIDS does not affect the performance of the OpenFlow controller and so is a feasible approach.

There has been rapid growth in the area of wireless communication by Wireless Mesh Networks (WMNs) where routing metric is the key metric to find the optimized route in WMNs. For obtaining optimal performance, integrating multiple performance metrics into a routing protocol is effective, as single metric will not be able to satisfy the thorough requirement of WMNs. This paper has proposed a new routing metric for Multiple Metric Cost (MMC), WMNs, incorporating three metrics: Residual energy 2) Available Bandwidth and 3) Expected Transmission Count (ETX). MMC results in a better throughput. We evaluated the performance of MMC for proactive, reactive and opportunistic routing protocols using the OMNET++ network simulator. Our evaluation shows that MMC performs well in all three classes of routing protocols.

This paper has clarified the simulation study of optical time division multiplexing/wavelength division multiplexing system based static/dynamic optical transparent networks demonstration through all-optical fiber amplifiers. Received signal power per channel is measured against network reach for both static and dynamic networks in the presence of all-optical Raman/erbium doped fiber amplifier/semiconductor optical amplifier amplifiers. Data rate transmission per channel is demonstrated against repeater spacing for all proposed optical fiber amplifiers based on various near infrared operating wavelengths for both static and dynamic optical transparent networks. In addition, the overall signal per noise ratio is clarified in relation to network throughput and various near infrared operating wavelengths for the static and dynamic transparent optical network based on all proposed amplification techniques. Moreover, the overall system bit error rate is studied and tested against network throughput and various near infrared operating wavelengths for both static and dynamic transparent optical networks based on all proposed amplification techniques.

Software Defined Networking (SDN) has emerged as a new networking paradigm that is based on the decoupling between data plane and control plane providing several benefits that include flexible, manageable, and centrally controlled networks. From a security point of view, SDNs suffer from several vulnerabilities that are associated with the nature of communication between control plane and data plane. In this context, software defined networks are vulnerable to distributed denial of service attacks. In particular, the centralization of the SDN controller makes it an attractive target for these attacks because overloading the controller with huge packet volume would result in bringing the whole network down or degrade its performance. Moreover, DDoS attacks may have the objective of flooding a network segment with huge traffic volume targeting single or multiple end systems. In this paper, we propose an entropy-based mechanism for Distributed Denial of Service (DDoS) attack detection and mitigation in SDN networks. The proposed mechanism is based on the entropy values of source and destination IP addresses of flows observed by the SDN controller which are compared to a preset entropy threshold values that change in adaptive manner based on network dynamics. The proposed mechanism has been evaluated through extensive simulation experiments.