Due to the increased usage of the internet for all sort of human activities, the demand for high-quality communication services continually grows, which makes essential the detection of events that compromise the quality of these services. The intelligent analysis of the monitoring data produced by routers can reveal relevant patterns hidden on the massive volume of data archived and, therefore, help to guide decisions of network providers. This paper presents a graphical and interactive web tool that performs intelligent analysis of visually selected geotemporal subsets of monitoring data collected by routers. The tool uses a variation of a data structure to store and selectively retrieve geo-temporal data to feed the intelligent analysis. At this time, the analysis module can detect anomalies and perform data predictions using selected Machine Learn algorithms present on Microsoft’s ML.Net framework.
In this paper, a new policy is proposed for Store and Drops cache content in the Wireless Access Networks nodes. The proposed policy select content that can be dropped and new content to be cached in a network node, on predefined time periods at each day and with pre-established time duration each one, repeated at each day. The temporal aspects and users social behavior that connect to the node for decision making are considered. An algorithm selects new content, in the same proportion, from those categories historically requested ones in these time periods on previous day. These new content selection purpose is to cache them in current day, at the corresponding time periods, to increase the content request hit ratio according to this policy. Simulation results against established FIFO, LRU, LFU and RANDOM policies shows that this proposed policy hit ratio is 2.46 times higher than the others, with an average hit ratio of 13.1% here versus an average hit ratio 5.325% of the above cited policies, in the evaluated scenarios.
Mobile networks combine different communication technologies sharing the same infrastructure. Although each technology accomplishes different networking requirements, it also hinders the network operation. Standard network monitoring tools often fail to detect service faults due to the multitude of monitored parameters of hardware from different vendors. To overcome this issue, we propose the Advanced Infrastructure for fault Diagnosis in Mobile Networks (AID-MN). AID-MN relies on applying ensemble of classifiers, a machine-learning technique, on fault postmortem data to automatically recognize the causes of service faults. We evaluate the performance of AID-MN in a case study of an operational access mobile network aiming at diagnosing intermittent 2G service failures. The results show that AID-MN assists the detection of the fault cause and supports to offer a feasible solution to the fault in a fast and effective way. Any service that have information about your status can be used with our solution.
Summary Software‐defined networks (SDN) usually rely on a centralized controller, which has limited availability and scalability by definition. Although a solution is to employ a distributed control plane, the main issue with this approach is how to maintain the consistency among multiple controllers. Consistency should be achieved with as low impact on network performance as possible and should be transparent for controllers, without requiring any change of the SDN protocols. In this work, we propose VNF‐Consensus, a virtual network function that implements Paxos to ensure strong consistency among controllers of a distributed control plane. In our solution, controllers can perform their control plane activities without having to execute the expensive tasks required to keep consistency. Experimental results are presented showing the cost and benefits of the proposed solution, in particular in terms of low controller overhead.
Intrusion Detection Systems (IDSs) are a fundamental component of defensive solutions. In particular, signaturebased IDSs aim to detect malicious activities on computer systems and networks by relying on data classification models built from a training dataset. However, classifiers performance can vary for each attack pattern. A common technique to overcome this issue is to use ensemble methods, where multiple classifiers are employed and a final decision is taken combining their outputs. Despite the potential advantages of such an approach, its usefulness is limited in scenarios where (i) multiple expert classifiers present divergent results or (ii) representative data are missing to detect a specific attack class. In this work, we introduce the concept of counselor networks to deal with conflicts from different classifiers by exploiting the collaboration between IDSs that analyze multiple and heterogeneous data sources. Our empirical results demonstrate the feasibility of the proposed architecture in improving the accuracy of the intrusion detection process.
The advent of smart grid promotes the upgrade of substation automation technology, adding processing and communication capabilities to control and protection devices. Thereby, research on substation data communication networks is pressured to find new, efficient, and reliable ways to support such scenario. In particular, the IEC 61850 standard defines stringent temporal requirements for the power systems communication comprising teleprotection schemes. Given that the power grid is a critical infrastructure, availability is also a strong requirement. In that context, Software-Defined Network (SDN) may provide powerful tools to fulfill those requirements at acceptable costs. In this paper, we survey and compare several available SDN controllers and their applicability to the teleprotection scenario.
SummaryEthereum is a new blockchain‐based platform that is also capable of running smart contracts. Despite its increasing popularity, there is a lack of studies on characterizing this system, in special the fees paid by users and the respective delay to confirm the transactions, that is, the pending time. In this sense, we study the main features of Ethereum transactions and evaluate the common belief—for blockchain systems that rely on proof of work—that users who pay higher fees will have their transactions confirmed faster. Specifically, we collect information about 7.2 million of transactions in Ethereum and correlate their pending time to several fee‐related features. Moreover, we conduct our study evaluating different ranges of values for the features, such as default and unusual values adopted by users as well as clusters of users with similar behaviors. Our empirical analysis shows strong evidence that there is no clear correlation between fees‐related features and the pending time. Overall, we conclude from our investigation that transaction's features, including gas and gas price defined by users, cannot determine the pending time of transactions.
Recently, with popularisation of video streaming service, new video distribution technologies have been created. Currently, one of the most promising ones is the Moving Picture Expert Group Dynamic Adaptive Streaming over HTTP or MPEG-DASH. Even so, with the limitation of the TCP/IP network structure, the end user Quality of Experience (QoE) may be affected. One issue that can affect user QoE is the workload of content distribution servers. Thus, the unbalancing of server's workload comprising user's attendance can lead to a content server provider non optimised choice. This work presents two load-balancing solutions between MPEG-DASH video servers based on Software-Defined Networks, using as a balancing workload metric the throughput of the content server as well as the CPU load.
SummarySince the term Internet of Things (IoT) was coined by Kevin Ashton in 1999, a number of middleware platforms have been developed to cope with important challenges such as the integration of different technologies. In this context of heterogeneous technologies, IoT message brokers become critical elements for the proper function of smart systems and wireless sensor networks (WSN) infrastructures. There are several evaluations made on IoT messaging middleware performance. Nevertheless, most of them ignore crucial aspects of the IoT context that also need to be included, such as reliability and other qualitative aspects. Thus, in this article, we propose a methodology for classification and evaluation of IoT brokers to help the scientific community and technology industry on evaluating them according to their interests, without leaving out important aspects for the context of smart environments. Our methodology bases its qualitative evaluations on the ISO/IEC 25000 (SQuaRE) set of standards and its quantitative evaluations on Jain's process for performance evaluation. We developed a case study to illustrate our proposal with 12 different open‐source brokers, validating the feasibility of our methodological approach.
Mobile devices are increasingly occupying sectors of society and one of its most important features is mobility. However, the use of mobile devices is subject to the lifetime of the batteries. Thus, the use of energy batteries has become an important issue in the study of wireless network technologies. In this context, new solutions that enable aggregate energy efficiency not only through energy saving, and principally they are evaluated from a more realistic model of energy discharge, if easy adaptation to existing protocols. This paper presents a study on the energy needed and the lifetime for Wireless Sensor Networks (WSN) using a heterogeneous network and applying the LEACH protocol.
The Academic Network of Uruguay (in spanish Red Académica Uruguaya - RAU) comprises several universities, research centres and government institutions. RAU is planning a major upgrade, and Software Defined Networking (SDN) is being evaluated as a technology which may promote the deployment of improved network services, while allowing researchers to keep on investigating over the operational network. To this end, we are building an evolved “RAU2” network prototype, in order to test the feasibility of the proposed architecture.
Wireless Sensor Networks (WSN) are already trend in many applications, environmental modeling in this network is of fundamental importance to maintain communication between modules always in acceptable power transmission and reception levels. Faced with this challenge the aim of this work is to conduct a study on the propagation of radio frequency signals in WSN applied in an environment used as water reservoir, this analysis will be done by comparing the real values Received Signal Strength Indicator (RSSI) with the estimates proposed by some of the most used power prediction models for WSN, thus achieving the best modeling of the proposed environment and thus determine which power prediction model more suitable for the application.
Electric power industries have expanded the automation of their networks in recent years to meet the growing demands for improvements in services. The introduction of the concept of Smart Grid, the increased availability of smart devices and improvements in telecommunications are key factors for that. Besides the benefits in control and management that this concept brings, there are some technical restrictions for the adoption due to legacy or multiple vendor equipments, each one with its own standard, as well as interoperability with supervisory and management softwares. This paper, then, proposes the use of a gateway to enable interoperability between devices using different communication protocols by translating them, centering data and control in a database defined by the Common Information Model(CIM) standard.
