Journal of Communications and Information Networks
Print ISSN 2096-1081 CN 10-1375/TN
Editor-in-Chief: Wei Zhang
In SDN, the control logic of packet processing devices is moved onto the SDN centralized controller. This decoupled networking architecture creates some critical concerns when compared to traditional distributed architectures. One primary concern in this architecture is the placement of the controller, which is commonly known as the CPP. From our extensive literature review, we identify that there is no strict placement rule that best fits every network in SDN. Dynamic addition and deletion of controllers is inevitable. Motivated from this, in this paper, we attempt to transform the CPP into a CSP. We show that, in the dynamic SDN environment, researchers need to pay attention to CSP issues. The clear advantage of CSP over CPP is that, in the dynamic SDN ecosystem, we can answer fundamental questions about the control plane performance, i. e. , minimum number of controllers, their workload distribution, and placement/locations. Furthermore, we can meet strict, application-specific, QoS constraints.
Aiming at the problem of failure recovery in current networks, a fast failure recovery method based on equivalent cooperative routing is proposed. Firstly, the transmission path between the source and destination nodes is divided into several non-overlapping path segments. Next, backup paths are deployed for each link in the path segment through segmented routing technology, which ensures fast routing recovery after failure. Additionally, in order to avoid damaging the QoS of the data stream through the failure recovery process, the transmission is guaranteed by the intersegment QoS complement. The experimental results show that the proposed method has a low failure recovery delay under a relatively small flow table cost.
As an important service model for advanced computing, SaaS uses a defined protocol that manages services and applications. The popularity of advanced computing has reached a level that has led to the generation of large data sets, which is also called Big data. Big data is evolving with great velocity, large volumes, and great diversity. Such an amplification of data has brought into question the existing database tools in terms of their capabilities. Previously, storage and processing of data were simple tasks; however, it is now one of the biggest challenges in the industry. Experts are paying close attention to big data. Designing a system capable of storing and analyzing such data in order to extract meaningful information for decision-making is a priority. The Apache Hadoop, Spark, and NoSQL databases are some of the core technologies that are being used to solve these issues. This paper contributes to the solutions to the issues of big data storage and processing. It presents an analysis of the current technologies in the industry that could be useful in this context. Efforts have been focused on implementing a novel Trinity model, which is built using the lambda architecture with the following technologies: Hadoop, Spark, Kafka, and MongoDB.
An adaptive dynamic load balancing algorithm based on QoS is proposed to improve the performance of load balancing in distributed file system, combining the advantages of a variety of load balancing algorithms. The new algorithm uses a tuple containing the number of files and the total file size as the QoS measure for the requested task. The master node sets a threshold for the requested task based on the QoS to filter storage nodes that meet the requirements of the task. In order to guarantee the reliability of the new algorithm, we consider the impact of CPU utilization, memory usage, disk IO occupancy rate, network bandwidth usage and hard disk usage on load balancing performance when calculating the real-time load balancing of storage nodes. The heterogeneity of the network is considered when the master node schedule task assignments to ensure the fairness of the algorithm. The comprehensive evaluation value is determined based the performance load ratio, which is calculated from the real-time load value of the storage node and a performance value after normalization. The master node assigns tasks to the storage node with the highest comprehensive evaluation value. The storage nodes provide adaptive feedback based on changes in the degree of connectivity, rather than periodic update of the load information. The actual distributed file system environment is set up on the server cluster, the performance of the new algorithm is tested through a contrast experiment. The experimental results show that the new algorithm can effectively reduce the average response time of the system, improve throughput, and enable the system load to reach a good balance.
In recent years, data mining and machine learning technologies have made great progress driven by enormous volumes of data. Meanwhile, the wireless-channel measurement data appears large in volume because of the large-scale antenna numbers, increased bandwidth, and versatile application scenarios. With powerful data mining and machine learning methods and large volumes of data, we can extract valuable and hidden rules from the wireless channel. Motivated by this, we propose a channel-modeling method using PCA in this paper. Its principle is to utilize the features and structures extracted from the CIR data collected by measurements, and then model the wireless channel of the targeted measurement scenario. In addition, a noise removing method using a BP neural network is designed for the proposed model, which can recognize and remove the noise of the polluted CIR accurately. The performance of the proposed scheme is investigated with the actual measured CIR data, and its superiority is verified.
Deep space networks, satellite networks, ad hoc networks, and the Internet can be modeled as DTNs (Delay Tolerant Networks). As a fundamental problem, the maximum flow problem is of vital importance for routing and service scheduling in networks. However, there exists no permanent end-to-end path since the topology and the characteristics of links are time-variant, resulting in a crucial maximum flow problem in DTNs. In this paper, we focus on the single-source-single-sink maximum flow problem of buffer-limited DTNs, followed by a valid algorithm to solve it. First, the BTAG (Buffer-limited Time Aggregated Graph) is constructed for modeling the buffer-limited DTN. Then, on the basis of BTAG, the two-way cache transfer series and the relevant transfer rules are designed, and thus a BTAG-based maximum flow algorithm is proposed to solve the maximum flow problem in buffer-limited DTNs. Finally, a numerical example is given to demonstrate the effectiveness of the proposed algorithm.
Thanks to channel reciprocity, the time-division duplexing operation is typically used for distributed large-scale multiple-input multiple-output systems. With the knowledge of uplink channel state, APs(Access Points)perform the downlink precoding to cooperatively communicate with multiple UEs(User Equipments). Unfortunately, the channel reciprocity is often jeopardized by the non-symmetric transceiver RF (Radio Frequency) circuits at both sides of the link. By excluding UEs from the calibration procedure, the TLS (Total Least Squares) method is extended to the case of partial calibration, where only the APs are involved to exchange calibration signals to compensate for RF mismatches. Nevertheless, channel fluctuations between APs significantly degrade system performance. Therefore, we propose a scheme to obtain the diversity gain, by performing a calibration with the combination of the calibration signals of different channel coherent times. Simulation results confirm the effective performance and robustness of our scheme.
Differential privacy is a strong notion for protecting individual privacy in data analysis or publication, with strong privacy guaranteeing security against adversaries with arbitrary background knowledge. A histogram is a representative and popular tool for data publication and visualization tasks. Following the emergence and development of data analysis and increasing release demands, protecting the private data and preventing sensitive information from leakage has become one of the major challenges for histogram publication. In recent years, many approaches have been proposed for publishing histograms with differential privacy. This paper explores the problem of publishing histograms with differential privacy, and provides a systematical summarization of existing research efforts in this field, begining with a discussion of the basic principles and characteristics of the technology. Furthermore, we provide a comprehensive comparison of a series of state-of-the-art histogram publication schemes. Finally, we provide possible suggestions for further expansions of future work in this area.
In the age of information explosion, big data has brought challenges but also great opportunities that support a wide range of applications for people in all walks of life. Faced with the continuous and intense competition from OTT service providers, traditional telecommunications service providers have been forced to undergo enterprise transformation. Fortunately, these providers have natural and unique advantages in terms of both data sources and data scale, all of which give them a competitive advantage. Multiple foreign mainstream telecom operators have already applied big data for their own growth, from internal business to external applications. Armed with big data, domestic telecom companies are also innovating business models. This paper will introduce three aspects of big data in the telecommunications industry. First, the unique characteristics and advantages of communications industry big data are discussed. Second, the development of the big data platform architecture is introduced in detail, which incorporates five crucial sub-systems. We highlight the data collection and data processing systems. Finally, three internal or external application areas based on big data analysis are discussed, namely basic business, network construction, and intelligent tracing. Our work sheds light on how to deal with big data for telecommunications enterprise development.
With the steady growth of traffic volume in core networks, it is predicted that future optical network communication will be constrained mainly by cost and power consumption. Thus, for Internet sustainability, it will be necessary to ensure cost and power efficiency in optical networks. The aims of this study are (i) to identify the main sources of cost and power consumption in fixed-grid (SLR and MLR) and flexi-grid (OFDM) optical networks, and (ii) to compare techniques for improving cost and power efficiency in SLR/MLR-and OFDM-based networks. To this end, we conducted a comparative analysis of cost and power efficiency for the OFDM-and MLR/SLR-based networks, and considering realistic networks, evaluated the cost and power consumed by different components in the optical layer. Our results show that (i) OFDM-based networks outperform those based on MLR/SLR in terms of both cost and power-efficiency, (ii) the extra equipment cost incurred due to under-utilization of spectrum is reduced by switching to a flexi-grid network, (iii) lower power consumption per bit is obtained when the networking solution ensures a finer bit-rate granularity, and (iv) there exists a power and spectrum minimization trade-off that is network characteristic dependent.
RFID (Radio Frequency IDentification) is a pioneer technology which has depicted a new lifestyle for humanity. Nowadays we observe an increase in the number of RFID applications and no one can ignore their numerous usage. An important issue with RFID systems is providing privacy requirements of these systems during authentication. Recently in 2014, Cai et al. proposed two improved RFID authentication protocols based on R-RAPS (RFID Authentication Protocol Security Enhanced Rules). We investigate the privacy of their protocols based on Ouafi and Phan privacy model and show that these protocols cannot provide private authentication for RFID users. Moreover, we show that these protocols are vulnerable to impersonation, DoS and traceability attacks. Moreover, we present two improved efficient and secure authentication protocols to ameliorate the performance of Cai et al. ’s schemes. Our analysis illustrates that the existing weaknesses of the discussed protocols are eliminated in our proposed protocols.
The wireless networks have a very bright future in networks and communications because of which they have taken a high interest of the researchers. As the users increased the purpose to use MANETs, they also became more diverse and wide due to which better performance is needed in MANETs. QoS is needed for applications for an efficient communication and load balancing is a feature in the routing protocol that can help in a better use of the resources and can help to increase the performance of the network. We propose a new approach for load balancing in AOMDV routing protocol for MANETs that can enhance the network performance by selecting paths using the temporal load on the intermediate nodes and by distributing the load amongst the free nodes while transmission of data, which is proved by simulations in NS-2.
