The Implementation of Cluster-Based Traffic Simulation System

Realistic network environments are commonly emulated by Internet application developers to test both the performance and the correctness of their applications. In this paper, we argue that emulating just realistic network environments may be inadequate for detecting hard-to-detect faults that have very low occurrence probabilities but potentially high impact if exposed in the real world. Specifically, we conduct extensive experiments to study the correctness testing capabilities of realistic and unlikely network environments. Our results show that small investments in emulating unlikely network environments may help in quickly detecting otherwise hard-to-detect faults.

Rucas over privacy of domain name registration

Enhanced network speed and the appearance of various applications have recently resulted in the rapid increase of Internet users and the explosive growth of network traffic. Under this circumstance, Internet users are eager to receive reliable and Quality of Service (QoS)-guaranteed services. To provide reliable network services, network managers need to perform control measures involving dropping or blocking each traffic type. To manage a traffic type, it is necessary to rapidly measure and correctly analyze Internet traffic as well as classify network traffic according to applications. Such traffic classification result provides basic information for ensuring service-specific QoS. Several traffic classification methodologies have been introduced; however, there has been no favorable method in achieving optimal performance in terms of accuracy, completeness, and applicability in a real network environment. In this paper, we propose a method to classify Internet traffic as the first step to provide stable network services. We integrate the existing methodologies to compensate their weaknesses and to improve the overall accuracy and completeness of the classification. We prioritize the existing methodologies, which complement each other, in our integrated classification system.

Quality of service is becoming highly important in wireless sensor networks (WSNs) due to many emerging applications; hence the need to know the worst case bounds for traffic delay and backlog in such networks is valuable. Network calculus provides a methodology for determining the worst case bounds in packet scheduling using the concept of arrival and service curves. Using Network Calculus, this paper presents a mathematical model of a TDMA-based medium access control protocol, where a cluster based system is modeled and arrival/service curve is proposed. In addition, the model is also extended to allow finding the maximum delay and backlog bounds for applications with certain QoS requirements.

In this paper, we propose the China-US international data placement laboratory (iDPL) based on an inter-continental testbed for data placement research. iDPL is able to support various data placement research due to its scalability and flexibility in deploying the experiments in the real network environment. The core design of iDPL leverages reliable workflow management and lightweight I/O protocol to allow complex experiment setup and on-the-fly experiment deployment. It is also extensible to plugin different network profiling tools such as iperf. We expect the powerful measurement capability of iDPL promotes research study on the intelligent data placement policies which adapt to the uncertainty of the wide-area network and guarantee the quality of service (QoS) of the big data applications. As a case study, we setup a set of data placement experiments to measure the end-to-end network performance constantly among several sites between China and US using different data placement tools. The experiments have been running for more than one year, and its measurement data is public available (http://mickey.buaa.edu.cn:8080/). We believe the measurement data is valuable for both network and big data researchers to understand the performance disparity between the raw network and the actual data placement, which provides useful insights to design big data applications with performance awareness. We encourage more researchers to deploy their own data placement experiments on iDPL, expediting the research direction of intelligent data placement with real network environment.

The question of quality of service in networks of the latest generation is studied. The impact of heterogeneous traffic on the main quality of service indicators reflecting the probability of packet loss and delivery delay over time is evaluated. A model of quality of service in information communication networks has been developed to ensure low traffic delay. Taking into account the concept of quality in information communication networks, the issue of quality of service in networks of the latest generation becomes important from the standpoint of ensuring high reliability and reliability of information transmission, which significantly affects the quality of services provided to the user. This, of course, determines the need to control various parameters of equipment and communication lines, which are determined both by the features of the transmission system and by the parameters of leased channels and tracts. The basis for determining the quality of service, as mentioned above, is the provision of the international concept of quality of service (QoS). According to the requirements of the compliance control model proposed in the concept, the approach to quality indicators is structurally related to the OSI reference model. The main advantages of modern technologies of information communication networks are undoubtedly 5G technologies, which already support such resource-intensive services as massive machine-to-machine communication, ultra-reliable communication and low-latency communication. All of them require high data transfer rates, low millisecond latency, and connectivity of millions of devices. At the same time, it is necessary to ensure a high level of quality of service (QoS), which plays an important role in 5G technologies. The proposed model makes it possible to evaluate not only the quality of service of the combined traffic flow of Internet of Things, tactile Internet, and augmented reality services, but also the delivery delay and the probability of packet loss. The simulation results showed that the parameters of the quality of service of the traffic of the Internet of Things and other modern applications differ significantly during the service of the aggregate flow. When studying the service of aggregated traffic, it was established that the probability of packet loss is higher for it than for Internet of Things traffic. In addition, this difference becomes more significant when the intensity of the incoming load increases.

Wireless Sensor Networks (WSNs) comprise of groups of tiny sensor nodes that are deployed for collaborative missions such as environmental monitoring, target tracking and surveillance. Due to the miniature size of the nodes, they are typically deployed in large numbers and communicate via multiple hops through a wireless shared communication channel. The successful implementation of such networks is dependent on the enabling technologies (such as digital electronics and wireless communications), as well as the provisioning of Quality of Service (QoS) in the network. While there have been many efforts in QoS provisioning in traditional networks such as the Internet and Mobile Ad Hoc Networks (MANETs), these networks have very different characteristics from that of WSNs. Consequently, the QoS models and protocols that have been designed for the Internet and MANETs cannot be directly applied to WSNs. In this paper, we look at some of the existing QoS mechanisms in the networking literature, and the inherent characteristics of WSNs which make it challenging to provision for QoS in the network. We then identify some key performance metrics for WSN QoS and outline some mechanisms to achieve QoS in the sensor network. Finally, we propose WISER - a framework to enhance QoS in WIreless SEnsoR networks.

Recently different capacity boosting technologies, <span>including 3D beamforming and expanding operating bandwidth have been investigated and included in the enhanced fourth generation (4G) and fifth-generation standards. For mobile operators to enhance their network performance, applying these advanced technologies is vital. To achieve that, planning and optimization work need to consider the spatiotemporal distribution of users. Although the capacity impact of advanced antenna technologies is investigated well, deployment options to exploit their benefits in a cost-effective manner considering a realistic network environment are seldom reported. This work presents a data driven and multi-objective based optimal vertical beams placement for an enhanced 4G mobile network with the newly introduced C-band. The method is utilized for an area located in Addis Ababa, Ethiopia, taking into account the existing 4G mobile network as a reference configuration. Users are distributed based on the data collected by ethio-telecom network management system. Findings indicate that optimal vertical beams placement achieved for gradual deployment with consideration cell edge and aggregate throughput performances while reducing network cost. While being cost-effective, up to 69.2% and 73.8% cell edge performance gain is achieved at 20 and 30 pareto points relative to an existing macro mobile network.</span>

A graph-based proactive fault identification approach in computer networks

To accommodate the increasing data rate demand, the fifth-generation (5G) cellular network came up with new technological advancements including massive multiple-input multiple-output (massive MIMO) and hyperdensification which can significantly boost network capacity. On the other hand, the introduction of these technologies along with their heterogeneity brings a challenge in terms of network operators’ need to identify a cost-effective optimal deployment approach which is hardly entertained by the legacy planning and optimization method. Hence, to leverage the core benefits of those technologies in a cost-effective manner, we need a holistic planning framework that takes into account their coverage, capacity and cost impact, and realistic spatiotemporal distribution of users. In this work, we present a data-driven multiobjective optimization planning framework that can be used not only for small cells but also for massive MIMO. The planning framework is illustrated using a 5G planning case study for a service area in Addis Ababa, Ethiopia, considering its realistic network data that is collected from the network management system and different potential identified deployment options. Ray tracing is employed to compute propagation, and users and demands are distributed based on the realistic network data. A two-stage optimization and a joint optimization are applied to identify points that provide optimal network performance. Simulation results reveal that the planning method provides Pareto points for different deployment options that can significantly improve the performance of the existing network while reducing the total network cost.

Recently the studies have found that real network traffic has the fractal characteristic,and it has great impact on the performance of traffic.With the convergence traffic of OBS network edge node,the author proposed a new model based on the wavelet transform under the multi-fractal condition.Different from the common fractal model,the new model considered not only the optimal of the scale factor in wavelet transform but also real network environment(such as buffer size,utilization and so on).Experimental results prove that the performance of traffic does not just relate with correlation,but also with the network environment.Finally,scale depiction and performance evaluation of traffic verify the effectiveness of the model.

The relationship between the user perceived Quality of Experience (QoE) with Internet applications and the Quality of Service (QoS) of the underlying network and applications is complex. Unveiling statistical relations between QoE and QoS can boost the prediction and diagnosis of QoE. In this paper, we shed light on the relationship between QoE and QoS for a popular application: YouTube video streaming. We conducted a controlled study where we asked users to rate their perceived quality of YouTube videos under different network conditions. During this experiments, we also captured network QoS and application QoS. We then analyze the resulting dataset with SES, a feature selection algorithm that identifies multiple, minimal-size, statistically-equivalent signatures with maximal predictive power for a target variable (e.g., QoE). We found that we can build optimal QoE predictors using a minimal signature of only three features from application or network QoS metrics compared to four when we consider features from both layers.

In an era of rapid urbanization, the quality of network services in urban environments is becoming increasingly important. This article explores efforts to improve network service quality in several sampang cities through an evaluation of Quality of Service (QoS) and user perceptions of Quality of Experience (QoE). The research is based on a survey conducted at various locations in sampang city to collect data on the reliability, speed, and availability of network services. The survey involved a diverse sample of users, covering a wide range of ages, professions, and levels of technology usage. In addition, we also analyzed users' perceptions of service quality based on their experiences of using the network in an urban environment. The results highlighted several key challenges in improving QoS, such as user density, signal interference, and network infrastructure limitations. However, we also found that strategies such as the use of advanced network technologies, such as 5G and beamforming, as well as traffic prioritization based on application type, can help to significantly improve QoS. In addition, we found a strong positive correlation between improved QoS and improved QoE, suggesting that improved quality of service has a direct impact on improving user experience. These findings provide valuable insights for network operators and policy makers in their efforts to improve network services in dense urban environments. The research also recommends further investment in network technologies and the development of policies that support more effective management of network traffic to ensure optimal quality of service.

Known for excellent convenience and abundant facilities, smart cities offer CCTV, delivery robots, security robots, and so on to its residents. Along with the collaboration of IoT (Internet Of Things), the innovation of smart city has gained immense attraction at present. Besides, the risks and challenging in the field of telecommunication still persists as the implemented wireless networks results in traffic and anomaly behaviour. Such issues become critical in case of large-scale infrastructure networks like WSN’s. As such circumstances, to perform efficient health and environment monitoring, the need for a next generation networked system raises. As the traditional anomaly detection schemes doesn’t work out for delay-sensitive environments due to increased latency, we propose a scalable, hybrid spatiotemporal anomaly detection approach that can effectively detect potential anomalies in the network. With the use of real-time stream processing, and other methodologies like Software-Defined Networking (SDN), a Fog Computing-based 5G low-power Wide Area Network (LPWAN) solution is developed and tested on a Antwerp’s City of Things testbed. The proposed approach is found to be beneficial when deployed in a real network environment with nearly 1800 sensor nodes.

Telecommunications carriers have begun to upgrade their networks with state-of-the-art optical equipment, referred to as optical-bypass technology. The ramifications of this technology are manifold, affecting the architecture, operation, and economics of the network, all of which are covered in this book. The book is oriented towards practical implementation in metro and backbone networks, taking advantage of the authors extensive experience with actual commercial equipment and carrier networks. The book starts with an overview of optical networking, including an introduction to state-of-the-art optical networks. The second chapter covers legacy optical equipment and the new optical-bypass technology, with an emphasis on the architectural impact of the equipment. For example, the discussion covers how the various types of equipment affect the economics and flexibility of the network. One of the challenges of optical-bypass technology is that it requires sophisticated algorithms in order to operate the network efficiently. Chapters three, four, and five describe such algorithms, where the focus is on techniques that have been proven to produce efficient results in realistic carrier networks. The design and planning strategies described in these chapters are readily implementable. All of the algorithms presented scale well with network size so that they are suitable for real-time design. Chapters six and seven focus on two important aspects of optical networks, namely efficient bundling of the traffic and protection of the traffic. Rather than cover every aspect of these two subjects, the book focuses on how best to perform bundling and protection in the presence of optical-bypass technology. Again, the emphasis is on techniques that have proven effective in real network environments. The final chapter explores the economics of optical networking. Several studies are presented that offer guidelines as to when and how optical-bypass technology should be deployed. The code for some of the routing algorithms is provided in the appendix, which adds to the utility of the book.