Best-effort Service Research Articles

Dual-Band Aerial Networks for Priority-Based Traffic

Unmanned aerial vehicle networks suffer from lackluster performance due to line-of-sight issues as well as resource scarcity. Network slicing, multi-band transmission, and numerology show great potential in mitigating such limitations. In this work, we propose the use of the sub-6 GHz and mmWave bands, the latter of which requires careful consideration of line-of-sight, with numerology for aerial network slicing to provision time-critical services and broadband access. Accordingly, we formulate a user admission control policy to regulate band access after which we formulate a joint resource block and power allocation problem, a mixed-integer non-linear programming problem, to minimize the quality-of-service gap of the throughput-dependent broadband users as a best-effort service and meet the time-critical service requirements. We propose a low-complexity algorithm, PREDICT, to tackle the formulated problem and present extensive simulation results to validate the advantages of our scheme.

A novel multiple access communication protocol for LoRa networks without LoraWAN

Current <span>studies predict there will be approximately 75 billion internet of things (IoT) devices connected to the internet by 2025. Such enormous IoT networks will require efficient communications systems to support those networks running well. We consider one of the emerging communications technologies that is widely used for wireless wide-area network, i.e., long range (LoRa). By default, LoRa wide area networking (LoRaWAN) has been equipped with a medium access control (MAC) protocol, however, several studies showed that LoRaWAN might not be the most excellent choice for certain applications of low-power wide area network, for example, peer-to-peer and mesh networks. In this work, we propose an application layer LoRa multi-communication (LMC) protocol to resolve multiple access problems in LoRa networks without employing LoRaWAN. The algorithm was embedded and tested on energy-constraint devices as building blocks of most IoT systems. Our examination in a controlled environment showed that the proposed algorithm achieved 94% averaged packet reception ratio (PRR) and 33.3 hours longer averaged battery lifetime compared to the default operation (without LMC algorithm) of LoRa networks. The proposed algorithm also introduced longer averaged time on air (ToA) compared to the default LoRa network mainly due to its best-effort service scenario applied to the proposed algorithm.</span>

Towards a Smart Environment: Optimization of WLAN Technologies to Enable Concurrent Smart Services

In this research paper, the spatial distributions of five different services-Voice over Internet Protocol (VoIP), Video Conferencing (VC), Hypertext Transfer Protocol (HTTP), and Electronic Mail-are investigated using three different approaches: circular, random, and uniform approaches. The amount of each service varies from one to another. In certain distinct settings, which are collectively referred to as mixed applications, a variety of services are activated and configured at predetermined percentages. These services run simultaneously. Furthermore, this paper has established a new algorithm to assess both the real-time and best-effort services of the various IEEE 802.11 technologies, describing the best networking architecture as either a Basic Service Set (BSS), an Extended Service Set (ESS), or an Independent Basic Service Set (IBSS). Due to this fact, the purpose of our research is to provide the user or client with an analysis that suggests a suitable technology and network configuration without wasting resources on unnecessary technologies or requiring a complete re-setup. In this context, this paper presents a network prioritization framework for enabling smart environments to determine an appropriate WLAN standard or a combination of standards that best supports a specific set of smart network applications in a specified environment. A network QoS modeling technique for smart services has been derived for assessing best-effort HTTP and FTP, and the real-time performance of VoIP and VC services enabled via IEEE 802.11 protocols in order to discover more optimal network architecture. A number of IEEE 802.11 technologies have been ranked by using the proposed network optimization technique with separate case studies for the circular, random, and uniform geographical distributions of smart services. The performance of the proposed framework is validated using a realistic smart environment simulation setting, considering both real-time and best-effort services as case studies with a range of metrics related to smart environments.

Autonomous Traffic-Aware Scheduling for Industrial Wireless Sensor-Actuator Networks

Recent years have witnessed rapid adoption of low-power Wireless Sensor-Actuator Networks (WSANs) in process industries. To meet the critical demand for reliable and real-time communication in harsh industrial environments, the industrial WSAN standards make a set of specific design choices, such as employing the Time-Slotted Channel Hopping (TSCH) technique. Such design choices distinguish industrial WSANs from traditional Wireless Sensor Networks, which were designed for best-effort services. Recently, there has been increasing interest in developing new methods to enable autonomous transmission scheduling for industrial WSANs that run TSCH and the Routing Protocol for Low-Power and Lossy Networks (RPL). Our study shows that the current approaches fail to consider the traffic loads of different devices when assigning time slots and channels, which significantly compromises network performance when facing high data rates. In this article, we introduce a novel Autonomous Traffic-Aware transmission scheduling method for industrial WSANs. The device that runs ATRIA can detect its traffic load based on its local routing information and then schedule its transmissions accordingly without the need to exchange information with neighboring devices. Experimental results show that ATRIA provides significantly higher end-to-end network reliability and lower end-to-end latency without introducing additional overhead compared with a state-of-the-art baseline.

Connection-Oriented and Connectionless Remote Entanglement Distribution Strategies in Quantum Networks

Quantum network is a promising platform for numerous quantum applications, including distributed quantum computing, secure communication, and improved sensing. Implementing entanglement distribution between remote quantum nodes plays a vital role in realizing the quantum network's capabilities, and it requires effective remote entanglement distribution protocols. To meet this requirement, we propose two strategies, namely connection-oriented and connectionless, to achieve end-to-end entanglement distribution inspired by the designs of classical communication. For the connection-oriented strategy, an exclusive quantum connection is pre-established between the source and destination nodes. For the connectionless strategy, entanglement swapping is performed hop-by-hop tentatively. In this way, the best-effort service is provided for the source node to establish long-distance entanglement with the destination node. Analogous to classical communication, the proposed strategies have their advantages and disadvantages. Our simulation results show that the connectionless strategy is more efficient and robust than the connection-oriented strategy in terms of throughput. However, the connection-oriented strategy provides reliable end-to-end entanglement distribution but at the expense of higher overhead. Moreover, the application scenarios of these two strategies are discussed. This work paves the road for designing end-to-end entanglement distribution protocols in quantum networks.

An efficient and reliable service customized routing mechanism based on deep learning in IPv6 network

Best-effort service model of traditional routing is gradually hard to meet the personalized demands under the rapid development of network technologies (e.g. 5G and IPv6). Therefore, service customization should be considered. In this work, a service customized routing mechanism based on deep learning in IPv6 network is proposed, which includes deep learning-based service customization module, reliability evaluation module, and routing calculation module. The first module uses neural network to learn the complex service customization function, which can quickly output win-win customized service strategies based on user demands. The second module can quantify the reliability of service routing paths, where not only the link status of IPv6 Neighbor Unreachable Detection (NUD) is considered, but also propose link performance weights to ensure the reliability of differentiated service performance. The third module uses the gray wolf optimization algorithm to calculate an optimal routing path to forward services with the customized strategies as the constraints and the maximum reliability and minimum cost as the goal. Finally, the mechanism is tested on the IPv6 Source Address Validation Improvement (SAVI) platform, which can reduce the execution time by 12.25% and improve the average routing reliability, user and ISP satisfaction by 9.0%, 40.45% and 7.4%, respectively.

The process mining method approach to analyze users’ behavior of internet in the Local Area Network of Sriwijaya University

The Sriwijaya University internet network management unit does not yet have a standard formulation for implementing Bandwidth Management & Bandwidth Allocation. To provide the best service, they apply the Best-Effort Service concept. As a result, it requires a relatively large network capacity and bandwidth provision so that it has an impact on costs. Therefore, it is necessary to know how users use internet bandwidth as the basic principle of Bandwidth Management & Bandwidth Allocation. This study has completed how to determine the behavior of internet users on the campus LAN as a reason to evaluate internet bandwidth usage. With the Process Mining method, process mapping has been carried out for all access to internet usage from all faculties. As a result, the factors that characterize and need to be considered in bandwidth management are obtained. In order of significance are Total Access Length, Average Variance, Number of User Case IDs, Number of Non-Academic Ports, Number of Academic Ports, Number of Access Frequency, Number of Events, and Number of Ports of Service.

Discrete-time simulation for performance modelling of FIFO single-server queuing system

Optimal queue management is crucial for improving the network operation since packet loss is commonly attributed to the likelihood of buffer overflow at intermediate routers on end-to-end communication path. In this paper, a transient discrete-time simulation model is proposed based on queuing theory served by single-server system and single first-in first-out (FIFO) queue. This queuing service framework effectively enforces the best-effort service to the streams of incoming data packets across the network with disparate quality of service (QoS) requirements. We plot the network performance measures such as queue length, delay, server utilisation and packet lifetime to validate the practical wireless systems. Besides, we employ conventional multiple-objective genetic algorithm to model the trade-off among key queuing system parameters including queuing delay, packet drop rate and network bandwidth. Finally, the efficiency of our queuing optimisation model is significantly compared with previous works in terms of bandwidth utilisation, delay, and packet loss rate.

Internet Transport Economics: Model and Analysis

With the rise of video streaming and cloud services, the Internet has evolved into a content-centric service platform. Due to the best-effort service model of the Internet, the quality of service (QoS) of Internet services however cannot be guaranteed. Furthermore, characterizing QoS is challenging since it depends on the autonomous business decisions such as capacity planning, routing strategies and peering agreements of network providers. To quantify the QoS for Internet-based services, we regard the Internet infrastructure as a transport system for data packets and study the Internet ecosystem and the economics of transport services collectively provided by the autonomous network providers. In contrast to the traditional transport economics that studies the movement of people and goods over space and time, our focus in the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Internet transport economics</i> is the movement of streams of data packets that create information services. In particular, we model the supply of network capacities and demands of throughput driven by network protocols and establish a macroscopic network equilibrium under which both the end-to-end delays and drop rates of Internet routes can be derived. We show that this equilibrium solution always exists and its uniqueness can be guaranteed under various realistic scenarios. We analyze the impacts of user demands and resource capacities on the network equilibrium and provide implications of Netflix-Comcast type of peering on the QoS of users. We demonstrate that our framework can be used as a building block to understand the routing strategies under a Wardrop equilibrium and to enable further studies such as Internet peering and in-network caching.

A price-aware congestion control protocol for cloud services

In current infrastructure-as-a service (IaaS) cloud services, customers are charged for the usage of computing/storage resources only, but not the network resource. The difficulty lies in the fact that it is nontrivial to allocate network resource to individual customers effectively, especially for short-lived flows, in terms of both performance and cost, due to highly dynamic environments by flows generated by all customers. To tackle this challenge, in this paper, we propose an end-to-end Price-Aware Congestion Control Protocol (PACCP) for cloud services. PACCP is a network utility maximization (NUM) based optimal congestion control protocol. It supports three different classes of services (CoSes), i.e., best effort service (BE), differentiated service (DS), and minimum rate guaranteed (MRG) service. In PACCP, the desired CoS or rate allocation for a given flow is enabled by properly setting a pair of control parameters, i.e., a minimum guaranteed rate and a utility weight, which in turn, determines the price paid by the user of the flow. Two pricing models, i.e., a coarse-grained VM-Based Pricing model (VBP) and a fine-grained Flow-Based Pricing model (FBP), are proposed. The optimality of PACCP is verified by both large scale simulation and small testbed implementation. The price-performance consistency of PACCP are evaluated using real datacenter workloads. The results demonstrate that PACCP provides minimum rate guarantee, high bandwidth utilization and fair rate allocation, commensurate with the pricing models.

A QoS-Based routing algorithm over software defined networks

Video streaming requires reliable transmission and a steady network to satisfy the video users and applications QoS needs. The network service must provide and meet a certain level of performance to accommodating these QoS, specially in Smart City environments, but current Internet best-effort service does not guarantee QoS. Software-defined networking (SDN) is an approach intended to separate the network control and data (forwarding) planes to get better management and optimization, which clearly enhance the planning and development of smart urban communities. In this paper, we propose a dynamic QoS algorithm over SDN to select the optimum path that meets video QoS in order to optimize the quality of experience (QoE). We differentiate the traffic based on video resolution QoE parameters to Standard Definition (SD) and High Definition (HD). The results demonstrate that the proposed method obtained better viewing quality for an smart community environment and increases the overall network throughput.

Designing a Energy Efficient Node Disjoint Multipath Routing technique to achieve energy efficiency in Mobile Ad Hoc Networks

Mobile Ad Hoc Networks (MANETs) are wireless networks which comprise of mobile nodes with limited energy resources. Every node cooperates to perform routing and expends energy on a frequent basis. Nodes are mobile thus link breakages are common and new routes need to be established quickly. Traditional routing protocols tend to find shortest routes to destination providing best-effort delivery service. However due to limited energy and bandwidth resources shortest path routes may not suffice and may usually degrade the performance of the network. In this paper an Energy aware Node Disjoint routing technique END-AODV is proposed that is based on the traditional AODV protocol. The technique designed is based on the argument that node disjoint multipath routing can conserve energy more efficiently as compared to link disjoint routing. Link Disjoint routing leads to overuse of a subset of nodes thus decreasing the overall network lifetime. The technique proposed incorporates energy drain rate metric to establish energy aware routes which are node disjoint in nature. Simulation results with ns2.34 simulator show efficiency of the proposed technique in terms of packet delivery ratio, average energy consumption per data bit delivered, network lifetime and average end to end delay.

Uncertainty-Aware Resource Provisioning for Network Slicing

Network slicing allows Mobile Network Operators to split the physical infrastructure into isolated virtual networks (slices), managed by Service Providers to accommodate customized services. The Service Function Chains (SFCs) belonging to a slice are usually deployed on a best-effort premise: nothing guarantees that network infrastructure resources will be sufficient to support a varying number of users, each with uncertain requirements. Taking the perspective of a network Infrastructure Provider (InP), this article proposes a resource provisioning approach for slices, robust to a partly unknown number of users with random usage of the slice resources. The provisioning scheme aims to maximize the total earnings of the InP, while providing a probabilistic guarantee that the amount of provisioned network resources will meet the slice requirements. Moreover, the proposed provisioning approach is performed so as to limit its impact on low-priority background services, which may co-exist with slices in the infrastructure network. Taking all these constraints into account leads to an integer programming problem with many nonlinear constraints. These constraints are first relaxed to get an integer linear programming formulation of the slice resource provisioning problem. This problem is then solved considering the slice resource provisioning demands jointly. A suboptimal approach is finally proposed where slice resource provisioning demands are considered sequentially. Both solutions are compared to provisioning schemes that do not account for best-effort services sharing the common infrastructure network, as well as uncertainties in the slice resource demands.

Optimization of Flow Allocation in Asynchronous Deterministic 5G Transport Networks by Leveraging Data Analytics

Time-Sensitive Networking (TSN) and Deterministic Networking (DetNet) technologies are increasingly recognized as key levers of the future 5G transport networks (TNs) due to their capabilities for providing deterministic Quality-ofService and enabling the coexistence of critical and best-effort services. Additionally, they rely on programmable and costeffective Ethernet-based forwarding planes. In this article, we address the flow allocation problem in 5G backhaul networks realized as asynchronous TSN networks, whose building block is the Asynchronous Traffic Shaper. We propose an offline solution, dubbed Next Generation Transport Network Optimizer (NEPTUNO), that combines exact optimization methods and heuristic techniques and leverages data analytics to solve the flow allocation problem. NEPTUNO aims to maximize the flow acceptance ratio while guaranteeing the deterministic Qualityof-service requirements of the critical flows. We carried out a performance evaluation of NEPTUNO in terms of the degree of optimality, execution time, and flow rejection ratio. Furthermore, we compare NEPTUNO with two online baseline solutions. Online methods compute the flows allocation configuration right after the flow arrives at the network, whereas offline solutions like NEPTUNO compute a long-term configuration allocation for the whole network. Our results highlight the potential of the data analytics for the self-optimization of the future 5G TNs.

Discrete-time simulation for performance modelling of FIFO single-server queuing system

Optimal queue management is crucial for improving the network operation since packet loss is commonly attributed to the likelihood of buffer overflow at intermediate routers on end-to-end communication path. In this paper, a transient discrete-time simulation model is proposed based on queuing theory served by single-server system and single first-in first-out (FIFO) queue. This queuing service framework effectively enforces the best-effort service to the streams of incoming data packets across the network with disparate quality of service (QoS) requirements. We plot the network performance measures such as queue length, delay, server utilisation and packet lifetime to validate the practical wireless systems. Besides, we employ conventional multiple-objective genetic algorithm to model the trade-off among key queuing system parameters including queuing delay, packet drop rate and network bandwidth. Finally, the efficiency of our queuing optimisation model is significantly compared with previous works in terms of bandwidth utilisation, delay, and packet loss rate.

Self-Adjusting DBA Algorithm for Next Generation PONs (NG-PONs) to Support 5G Fronthaul and Data Services

In this article, we propose a novel dynamic bandwidth allocation (DBA) algorithm for NG-PON networks to jointly support 5G fronthaul and best-effort data services in the same PON channel. The proposed self-adjusting DBA adjusts dynamically the allocation intervals to the current required fronthaul throughput based on the requests reported from the ONUs. It is suitable for dynamic 5G scenarios where, for energy efficiency reasons, the fronthaul connections are dynamically set up and torn down over time: when a new 5G fronthaul connection is set up, the maximum latency of the current connections is guaranteed while when a current 5G fronthaul connection is torn down, the freed transmission resources become available for data services. The only requirement is that the capacity of the channel in the NG-PON network is enough for the throughput of all 5G fronthaul connections supported by the channel. In this way, the proposed self-adjusting DBA algorithm has the advantage of requiring a much lower management coordination between the 5G infrastructure and the NG-PON infrastructure than the one required when the throughput of the 5G fronthaul connections is supported as a guaranteed service.

Tiered dynamic resource reservation based on time slot and bandwidth reduction

The main contradiction that limits the development of video services is its high-bandwidth, low-latency network requirements and the complex and variable core network environment and Best-Effort service model. In order to solve this problem, this paper proposes a tiered dynamic resource reservation algorithm for video services based on time slot and bandwidth reduction under SDN core network framework. The algorithm introduces a grading policy on the basis of the bandwidth-reducing dynamic resource reservation algorithm, and adopts bandwidth reduction when the bandwidth requirement of the video service cannot be satisfied. At the same time, the bandwidth is reserved for high-priority conference requests. The simulation results show that the algorithm can further improve the success rate of high-priority video service resource reservation.

Real-Time Scheduling of Massive Data in Time Sensitive Networks With a Limited Number of Schedule Entries

Time sensitive networks support deterministic schedules over Ethernet networks. Due to their high determinism, high reliability and high bandwidth, they have been considered as a good choice for the backbone network of industrial internet of things. In industrial applications, the backbone network connects multiple industrial field networks together and has to carry massive real-time packets. However, the off-the-shelf time-sensitive network (TSN) switches can deterministically schedule no more than 1024 real-time flows due to the limited number of schedule table entries. The excess real-time flows have to be delivered by best-effort services because the switch only supports the two scheduling services. The best-effort services can reduce average delay, but cannot guarantee the hard real-time constraints of industrial applications. To make the limited number of schedule table entries support more real-time flows, first, we relax scheduling rules to reduce the requirement for schedule table entries and formulate the process of transmitting packets as a satisfiability modulo theories (SMT) specification. Then, we divide the SMT specification into multiple optimization modulo theories (OMT) specifications so that the execution time of solvers can be reduced to an acceptable range. Second, we propose fast heuristic algorithms that combine schedule tables and packet injection control to eliminate scheduling conflicts. Finally, we conduct extensive evaluations. The evaluation results indicate that, compared to existing algorithms, our proposed algorithm requires only one-twentieth the number of schedule entries to schedule the same flow set.

QROUTE: An Efficient Quality of Service (QoS) Routing Scheme for Software-Defined Overlay Networks

Many computer network applications impose constraints for multiple quality of service (QoS) metrics, e.g., delay, packet loss, bandwidth, and jitter. These QoS constraints cannot be guaranteed by the Internet due to its best-effort service model. Overlay networks have been an effective technique at the application layer to support multiple QoS constraints of networking applications. In software-defined overlay networks, software-defined networking (SDN) paradigm is introduced in the overlay networks to enable centralized and efficient routing of traffic in the overlay networks, thus, enabling better QoS. One of the main challenges in software-defined overlay networks is the fast-changing overlay link QoS characteristics. However, the existing routing algorithms for satisfying multiple QoS constraints in software-defined overlay networks involve high route computation time and thus these routing algorithms cannot adapt to the fast-changing overlay link QoS characteristics. Moreover, as we scale the size of overlay networks, the size of forwarding tables increases exponentially. This is because the existing routing schemes for ensuring multiple QoS constraints use both the source and the destination address for data-plane forwarding. This leads to pushing a huge amount of forwarding table entries by the controller through the network and thus limiting the size of the overlay network. We propose an efficient routing scheme, QROUTE, for satisfying multiple QoS constraints in software-defined overlay networks. QROUTE consists of a control plane routing algorithm which has significantly low route computation time because of employing a novel directed-acyclic-graph (DAG) based approach. QROUTE also reduces the forwarding entries in the data plane by using a QoS-metric-based forwarding scheme. We extensively evaluate QROUTE using traces from a global overlay service provider. We also examine QROUTE on a testbed of P4-BMv2 switches controlled by the ONOS controller using P4Runtime protocol. We find that QROUTE outperforms other state-of-the-art QoS routing schemes in route computation time, size of the forwarding tables and meeting the QoS requirements of various applications.

A novel reconfigurable router for QoS guarantees in real-time NoC-based MPSoCs

Abstract This paper presents a proposal and implementation of a multi-mode full-reconfigurable router for Network-on-Chip (NoC). First, the router supports a hybrid packet-switching architecture that is dynamically reconfigurable to exchange between wormhole and virtual cut-through switching schemes at run-time. Therefore, it reaches a higher average performance than wormhole switching, while decreasing the implementation cost in comparison with the virtual cut-through switching. Second, the router is equipped a Quality-of-Services (QoS)-driven arbiter. Therefore, the proposed solution not only guarantees the guaranteed-throughput service without reserving resources but also enhances the average performance for the best-effort service by using network resources efficiently based on the priority inheritance arbitration mechanism. Third, the router is enhanced with the dynamically deadline-aware rerouting mechanism. In contention situation, the router can configure the routing computation unit to reroute the packet to another path so as to reduce the waiting interval of the blocked packets. The router was designed at the Register Transfer Level and modeled using VHDL language and then synthesized with Xilinx Virtex-7 FPGA technology. The experimental results prove that the proposed router is reliable and can improve the average performance of different QoS loads significantly compared with the generic routers while the area and power overhead are acceptable.