Novel Reliability Methodology for Virtual Solutions by Paul Reeser and Carolyn Johnson
Abstract – Reliability is crucial in the fiercely-competitive mobile network service provider environment. 5G application virtualization and cloud deployment introduce an entirely new dimension to the vendor requirements process. The ability to separate software from hardware, and select different vendors for each, creates the need to rationally allocate the quantifiable reliability requirements between these layers of separation in a multi-vendor virtualized environment. In this work, we define a comprehensive set of service-level reliability metrics, and develop a novel methodology to allocate these metrics to the service delivery platform layers using natural tunable parameters.
Travelling Maintenance System Design for Wide-Area Telecommunication Networks by Kouji Hirata, Hiroshi Yamamoto, Shohei Kamamura, Toshiyuki Oka, Yoshihiko Uematsu, Hideaki Maeda and Miki Yamamoto
Abstract – This paper proposes a traveling maintenance method as a new network maintenance model. For failure recovery, the proposed method utilizes permissible time, which keeps high availability, ensured by shared backup resources. In the proposed method, even though a failure occurs in a communication facility, maintenance staff waits for occurrence of successive failures in other communication facilities during the permissible time instead of immediately tackling the failure. Then the maintenance staff successively go to the communication facilities that have faulty devices and repair them. By doing so, the proposed method can reduce the amount of time that the maintenance staff consumes for fault recovery. Furthermore, this paper provides a system design that aims at optimizing the proposed traveling maintenance according to system requirements determined by the design philosophy of telecommunication networks. Through simulation experiments, we show the effectiveness of the proposed method.
Framework and Implementation of Online Smartphone Traffic Classification According to Quality Sensitivity by Norihiro Fukimoto, Kouji Nakamura, Masaki Suzuki, Yasuhiko Hiehata and Masanori Miyazawa
Abstract – Currently smartphones have become the most prevalent device used on the Internet and they generate a substantial portion of Internet traffic. Smartphones can run a wide variety of applications, each generating distinctive traffic. From the perspective of network operation, classification of these types of traffic in order to prioritize quality-sensitive traffic over best-effort traffic has the potential to improve network performance. Network operators generally treat web browsing, Voice over Internet Protocol (VoIP), video streaming, and other interactive traffic as quality-sensitive traffic. This quality-sensitive traffic is generally generated by user interactions on the current foreground application, hence its communication quality is likely to affect user’s quality of experience (QoE) directly. On the other hand, traffic generated when the smartphone is inactive has a low potential for affecting user’s QoE. As a traffic classification technique, machine learning is popular and demonstrates high accuracy; however, it is disadvantageous in that it involves gathering enough training data sets for supervised training. We propose a framework for online smartphone traffic classification that classifies traffic according to whether it is generated when the smartphone is active or inactive. A key feature of the proposed framework is automation of training data collection for machine learning. An application on the smartphone periodically estimates whether that smartphone is active or not, then it uploads the estimation results with timestamps as a source of training data sets. We also implemented a prototype of the proposed framework and assessed the feasibility of the proposal.
On the SINR Distribution of SWIPT MU-MIMO with Antenna Selection by Hadi Saki, Gilles Charbit, and Mohammed Shikh-Bahaei
Abstract – In this paper, we provide a closed-form approximation of the full-duplex simultaneous wireless information and power transfer (SWIPT) multi-user MIMO (MU-MIMO) system signal-to-interference-and-noise ratio (SINR) distributions for the received signal at the sensor nodes (SNs) with perfect and imperfect channel state information (CSI) and transmit antenna selection scheme at the transmitter. We also studied the SINR distributions of the uplink with Zero-Forcing beamforming (ZFBF) and antenna selection at the aggregator (AGG). The downlink SINR with perfect and imperfect CSI are modeled by multivariate Beta type II distributions and the uplink with perfect and imperfect CSI are modeled with multivariate Wishart distributions. W e compared the proposed analytical results to the Monte-Carlo simulations and we obtained a perfect match.
Implementation of a C-UNB Module for NS-3 and Validation for DLMS-COSEM Application Layer Protocol by Abhijeet Sahu and Ana E. Goulart
Abstract – The number of sensors and embedded devices in an urban area can be on the order of thousands. New low-power wide area (LPWA) wireless network technologies have been proposed to support this large number of asynchronous, low-bandwidth devices. Among them, the Cooperative Ultra-Narrowband (C-UNB) is a clean-slate cellular network technology to connect these devices to a remote site or data collection server. C-UNB employs small bandwidth channels, and a lightweight random access protocol. In this paper, a new application is investigated – the use of C-UNB wireless networks to support the Advanced Metering Infrastructure (AMI), in order to facilitate the communication between smart meters and utilities. To this end, we adapted a mathematical model for C-UNB, and implemented a network simulation module in NS-3 to represent C-UNB’s physical and medium access control layer. For the application layer, we implemented the DLMS-COSEM protocol, or Device Language Message Specification – Companion Specification for Energy Metering. Details of the simulation module are presented, and we conclude that it supports the results of the mathematical model.
Function Selection Algorithm for Service Function Chaining in NDN by Yoshiaki Shiraiwa and Hidenori Nakazato
Abstract – As the number of IoT devices increases with the popularity of IoT, the data traffic to and from IoT devices put a heavy load on the network and the cloud. In this work, to solve this problem, we consider deploying “functions” which process IoT data for IoT services on the calculation resources in the network, and by adapting Service Function Chaining (SFC) to these functions, we realize in-network processing of IoT data. Furthermore, in this research, SFC for IoT data will be realized using Named Data Networking (NDN), which is a content oriented communication protocol. In this scenario, we propose a novel function selection method in SFC that decides which functions will be assigned to the IoT service. In our proposed method, we aim to minimize the execution time of SFC while balancing the load of functions, and realize efficient SFC.
Collaborative Caching for Dynamic Map Dissemination in Vehicular Networks by Rui Wang, Subir Biswas, Sushantha Das and Jayanthi Rao
Abstract – This paper introduces a vehicular content caching mechanism for disseminating navigational maps while minimizing cellular network bandwidth usage. The key concept is to collaboratively cache the dynamic components of navigational maps in roadside units (RSUs) and vehicles such that the majority of dissemination can be accomplished using V2V and V2I communication links. In this way, the usage of cellular links from the vehicles can be minimized. Following the development of a dynamic map object model, the paper develops a collaborating caching framework, which is then evaluated using ONE, a delay tolerant network simulator. Simulation experiments were conducted for different protocols and network parameters. The results indicate that compared to infrastructure-only caching strategies, the proposed vehicle-involved collaborative caching mechanism is able to reduce the bandwidth usage of cellular networks and the delivery delay for obtaining dynamic map data.
A Hybrid (Active-Passive) Clustering Technique for VANETs by Garret Moore and Peixiang Liu
Abstract – Clustering serves a vital role in the operation of Vehicular Ad hoc Networks (VANETs) by continually grouping highly mobile vehicles into logical hierarchical structures. These moving clusters help stabilize a global topology for message routing, and enable multi-channel operations: utilizing a long-range control channel for control data, and a short-range service channel for intra-cluster communications. Clustering techniques are partitioned in research into two categories: active and passive. Active techniques rely on periodic beacon messages from all vehicles containing location, velocity, and direction information. However, in areas of high vehicle density, congestion may occur on the long-range channel used for beacon messages limiting the scale of the VANET. Passive techniques use embedded information in the packet headers of existing traffic to handle clustering. In passive clustering, vehicles not transmitting traffic may cause cluster heads to contain stale and malformed clusters. This paper proposes a hybrid (active-passive) clustering technique, where the passive technique is used as a congestion control strategy when congestion is detected in the network. In this case, cluster members halt their periodic beacon messages on the control channel and embed position information in the packet header of traffic over the service channel to update the cluster head of their position. This multi-channel technique dynamically reduces the channel load of the control channel in urban VANET scenarios, increasing the scalability of the VANET. Simulation results show that the hybrid technique is effective at controlling congestion and reducing message delay in an urban VANET environment when compared to active clustering techniques.
Evaluating an Adaptive Web Traffic Routing Method for the Cloud by Gandhimathi Velusamy and Ricardo Lent
Abstract – The low maintenance requirement, capacity scalability, and pay-as-you-go properties of cloud computing are attractive for the virtualized deployment of diverse web services. Web traffic is typically handled by multiple server mirrors that are spatially dispersed to satisfy the expectations of a large number of worldwide users. Since the energy consumption of each server depends on its workload, the use of web routing opens the possibility of reducing operational costs through the exploitation of the regional and temporal differences in energy pricing at the mirroring sites. On the downside, the shared nature of the cloud and the network brings potential latency issues that could impact the quality of service of many applications. In this paper, we report on experimental results obtained from a web service system that uses learning automata to make dynamic routing decisions based on a cost and quality-of-service criteria in the cloud. The experiments were conducted using a network of 24 nodes running in the CloudLab with time-varying energy prices that were modeled from real data.