Average Bandwidth Utilization Research Articles

Load balancing and topology dynamic adjustment strategy for power information system network: a deep reinforcement learning-based approach

As power information systems play an increasingly critical role in modern society, higher requirements are placed on the performance and reliability of their network infrastructure. In order to cope with the growing data traffic and network attack threats in the power information system, we select the power information system data center network as the research object and design an overall system solution based on software defined network, including the application layer, control layer and infrastructure layer. A typical fat tree network topology is simulated and analyzed. We define the load balancing and network topology dynamic adjustment problem as a Markov decision process, and design a data flow path acquisition method based on breadth-first search to construct the action space of each host. Then, a deep reinforcement learning algorithm based on deep Q-network, priority experience replay and target network is introduced to provide solutions for optimizing the performance of power information systems and responding to network attacks. Simulation results show that the proposed method is better than the traditional equal-cost multi-path algorithm in terms of average bandwidth utilization, average jitter and average packet loss, and can reduce the probability of network nodes being attacked by more than 11%.

Content-aware QoE optimization in MEC-assisted Mobile video streaming

The traditional client-based HTTP adaptation strategies do not explicitly coordinate between the clients, servers, and cellular networks. A lack of coordination leads to suboptimal user experience. In addition to optimizing Quality of Experience (QoE), other challenges in adapting HTTP adaptive streaming (HAS) to the cellular environment are overcoming unfair allocation of the video rate and inefficient utilization of the bandwidth under the high-dynamics cellular links. Furthermore, the majority of the adaptive strategies ignore important video content characteristics and HAS client information, such as segment duration, buffer size, and video duration, in the video quality selection process. In this paper, we present a content-aware hybrid multi-access edge computing (MEC)-assisted quality adaptation algorithm by taking advantage of the capabilities of edge cloud computing. The proposed algorithm exploits video content characteristics, HAS client settings, and application-layer information to jointly adapt the bitrates of multiple clients. We design separate strategies to optimize the performance of short and long duration videos. We then demonstrate the efficiency of our algorithm against client-based solutions as well as MEC-assisted algorithms. The proposed algorithm guarantees high QoE, equitably selects video rates for clients, and efficiently utilizes the bandwidth for both short and long duration videos. The results from our extensive experiments reveal that the proposed long video adaptation algorithm outperforms state-of-the-art algorithms, with improvements in average video rate, QoE, fairness, and bandwidth utilization of 0.4%–12.3%, 8%–65%, 3.3%–5.7%, and 60%–130%, respectively. Furthermore, when high bandwidth is available to competing clients, the proposed short video adaptation algorithm improves QoE by 11.1% compared to the long video adaptation algorithm.

MOBILE EDGE COMPUTING ORIENTED MULTI-AGENT COOPERATIVE ROUTING ALGORITHM: A DRL-BASED APPROACH

In the era of 5G/B5G, computing-intensive, delay-sensitive applications such as virtual reality inevitably bring huge amounts of data to the network. In order to meet the real-time requirements of applications, Mobile Edge Computing (MEC) pushes computing resources and data from the centralized cloud to the edge network, providing users with computing offload technology. However, the mismatch between the great computing requirements of computing-intensive tasks and the limited computing power of a single edge server poses a great challenge to computing offload technology. In this paper, a multi-agent cooperation mechanism for MEC and a routing mechanism based on deep reinforcement learning (DRL) are proposed. First of all, a multi-agent cooperation mechanism is proposed to realize the cooperative processing of computing-intensive and delay-sensitive applications, and the task unloading decision-making problem based on multi-agent cooperation is studied. Secondly, the cooperative processing of tasks by multi-agents involves data transmission. Considering the real-time requirements of tasks, this paper proposes an intelligent routing mechanism based on DRL to plan the optimal routing path. Finally, the simulation implementation and performance evaluation of the multi-agent cooperation mechanism and routing mechanism for MEC are carried out. The experimental results show that the intelligent routing mechanism based on DRL and graph neural network is superior to the comparison mechanism in terms of network average delay, throughput and maximum link bandwidth utilization. At the same time, the superiority of graph neural network in model generalization is verified on a new network topology National Science Foundation (NSF) Net. The results of route optimization are applied to the multi-agent cooperation mechanism, and the experimental results show that the mechanism is superior to the comparison scheme in terms of task success rate and average task response delay. The combination of these two mechanisms well solves the problem that it is difficult to deal with computing-intensive and delay-sensitive applications in mobile edge computing because of its limited resources.

Security-Aware CAN-FD Message Packing in Intelligent Automotive Cyber–Physical Systems

Controller area network with flexible data-rate (CAN-FD) has received great attention in automotive cyber–physical systems (ACPSs) due to its high bandwidth and long payload. However, CAN-FD adopts a broadcast message transmission mechanism and lacks security protection, making it extremely vulnerable to cyberattacks. CAN-FD message packing (packing signals into messages) with low bandwidth occupancy (utilization) under security constraints is the prerequisite for running intelligent applications in ACPS. In this work, we implement a two-stage CAN-FD message packing solution to reduce bandwidth utilization and improve signal acceptance rate under security constraints. The first stage solves the message packing problem of minimizing bus bandwidth utilization under security constraints. The second stage aims at improving the signal acceptance rate by repacking signals. Experimental results show that the first stage reduces average bus bandwidth utilization by 135% compared with the unpacking solution, and the second stage improves the average signal acceptance rate by 5% than existing advanced methods.

Intersatellite Laser Link Planning for Reliable Topology Design in Optical Satellite Networks: A Networking Perspective

The development of reusable rockets makes it possible to launch a massive number of low earth orbit satellites for Internet access. These satellites are expected to be connected by inter-satellite links (ISLs) to provide global Internet service to end-users on the surface of the earth. From the infrastructure’s perspective, the ISLs can be implemented with lasers, forming the optical satellite networks. The design of terrestrial optical networks is usually traffic-driven, meaning more links/bandwidth are deployed to the traffic-intensive areas. But, the orbiting nature of satellites determines that laser ISLs are also moving at high velocities, making the presence of corresponding bandwidth loosed coupled with a specific area. As a result, the traffic-driven network design methodology will be inapplicable to the optical satellite networks. In this work, we model the optical satellite networks and define the laser ISL planning problem, Accordingly, we propose an ISL removal algorithm to remove parts of the ISLs from the grid-mesh topology to improve the average bandwidth utilization ratio (i.e., bandwidth efficiency) while maintaining networks’ reliability and availability. We conduct a simulation study based on the Starlink constellation to evaluate the grid-mesh topology and to investigate the proposed algorithm’s impact on the network performance. Results show that the bandwidth resource in the grid-mesh topology can hardly be used efficiently and removing certain ISLs properly can improve the bandwidth efficiency of optical satellite networks significantly. We also gain another interesting insight that maintaining too many ISLs will not improve, but degrade the network availability.

Reinforcement Learning-Based Service-Oriented Dynamic Multipath Routing in SDN

The increasing quality and various requirements of network services are guaranteed because of the advancement of the emerging network paradigm, software-defined networking (SDN), and benefits from the centralized and software-defined architecture. The SDN not only facilitates the configuration of the network policies for traffic engineering but also brings convenience for network state obtainment. The traffic of numerous services is transmitted within a network, whereas each service may demand different network metrics, such as low latency or low packet loss rate. Corresponding quality of service policies must be enforced to meet the requirements of different services, and the balance of link utilization is also indispensable. In this research, Reinforcement Discrete Learning-Based Service-Oriented Multipath Routing (RED-STAR) has been proposed to understand the policy of distributing an optimal path for each service. The RED-STAR takes the network state and service type as input values to dynamically select the path a service must be forwarded. Custom protocols are designed for network state obtainment, and a deep learning-based traffic classification model is also integrated to identify network services. With the differentiated reward scheme for every service type, the reinforcement learning model in RED-STAR gradually achieves high reward values in various scenarios. The experimental results show that RED-STAR can adopt the dynamic network environment, obtaining the highest average reward value of 1.8579 and the lowest average maximum bandwidth utilization of 0.3601 among all path distribution schemes in a real-case scenario.

HOPASS: A two-layer control framework for bandwidth and delay guarantee in datacenters

In data center networks (DCNs), flows with different objectives coexist and compete for limited network resources (such as bandwidth and buffer space). Without harmonious resource planning, chaotic competition among these flows would lead to severe performance degradation. Furthermore, low latency is critical for many emerging applications such as augmented reality(AR), virtual reality(VR) and telepresence, making the network control problem even more challenging. To address the above issues, this paper novelly proposes a receiver-driven two-layer control framework called HOPASS, which incorporates a slow control layer and a fast control layer to strike a balance among multiple network sharing objectives and achieve low latency. The slow control layer ensures bandwidth guarantee in an aggregated flow level by solving a multi-objective network utility maximization (NUM) problem using an online learning approach. Then the results will be dispatched to the data plane by configuring weights in the switches with weighted fair queue functionality. Under the configuration dictated by the slow control layer, the fast control layer leverages the token packets sent by receivers to dynamically probe and reserve network capacity, so that it can proactively prevent network congestion and guarantee low latency data delivery. To evaluate the proposed framework, we have implemented HOPASS in ns-3 and conduct extensive experiments under various network scenarios. The simulation results show that HOPASS achieves near-optimal performance in terms of bandwidth allocation in multi-objective scenarios and also guarantees low end-to-end delay. Moreover, it outperforms DCTCP and NewReno in terms of average bandwidth utilization and global total network utility at the aggregated flow level. Therefore, we conclude that HOPASS provides an effective framework for DCNs when considering both multi-objective optimization and a low latency network.

Fuzzy-Based Quality Adaptation Algorithm for Improving QoE from MPEG-DASH Video

Video clients employ HTTP-based adaptive bitrate (ABR) algorithms to optimize users’ quality of experience (QoE). ABR algorithms adopt video quality based on the network conditions during playback. The existing state-of-the-art ABR algorithms ignore the fact that video streaming services deploy segment durations differently in different services, and HTTP clients offer distinct buffer sizes. The existing ABR algorithms use fixed control laws and are designed with predefined client/server settings. As a result, adaptation algorithms fail to achieve optimal performance across a variety of video client settings and QoE objectives. We propose a buffer- and segment-aware fuzzy-based ABR algorithm that selects video rates for future video segments based on segment duration and the client’s buffer size in addition to throughput and playback buffer level. We demonstrate that the proposed algorithm guarantees high QoE across various video player settings and video content characteristics. The proposed algorithm efficiently utilizes bandwidth in order to download high-quality video segments and to guarantee high QoE. The results from our experiments reveal that the proposed adaptation algorithm outperforms state-of-the-art algorithms, providing improvements in average video rate, QoE, and bandwidth utilization, respectively, of 5% to 18%, about 13% to 30%, and up to 45%.

Differentiated services and its importance in fault tolerant WDM optical network

The focus of this paper is on developing and evaluating a practical methodology for determining if and when different types of traffic can be safely managed within different class of services and satisfying reliability criteria. Our approach relies on an analytical model developed under a number of simplifying assumptions, which we test using several real traffic traces under fault condition corresponding to different types of services for clients and satisfying reliability degree for the connection request of link failure and searching of backup path dynamically that can give rise to substantial performance deviations with different class of service that is high, medium and low class. The results show that a hop-by-hop, link-state routing protocol, like Open shortest path first, can be extended to efficiently support class-based Quality of Service traffic differentiation. We have used auxiliary graph based open shortest path heuristic (AGBOSPH) technique to select back up path as a restoration strategy. It is found that average bandwidth utilization for large scale network is 12.62% less than previous studies. At the time of starting both have similar performance but under restoration AGBOSPH is more satisfactory and give excellent performance for higher scale of network. The proposed algorithm shows improvements at all traffic loads. At heavy loads the improvement is more pronounced and average improved performance for high degree of traffic load is of the order of 28.56%.

Bandwidth allocation algorithm based on differential BRP models in ethernet PON

With the emergence of new services such as high-definition video, cloud computing and virtual reality, the load's bandwidth prediction ability is necessary to support soaring services request bandwidth with diversified characteristics and requirements. In this paper, a novel bandwidth allocation algorithm based on differential bandwidth request prediction (BRP) models is proposed. This algorithm firstly combines linear prediction and nonlinear prediction. According to different characteristics of services, the linear prediction model and the Kalman-filter prediction model are used to predict request bandwidths of corresponding services. By the differential BRP models, the proposed algorithm can greatly better the accuracy of service's request bandwidth prediction and the efficiency of allocating bandwidth. The simulation results demonstrate that the proposed models and algorithm can reduce network's average delay, increase throughput and bandwidth utilization.

Flexible Multi-Thread Dynamic Bandwidth Allocation Algorithm in VPONs Based on LR WDM/TDM PON

With the smooth upgrade of the access network, multiple tuning-time devices will coexist in the long-reach access network, and the subsequent problems of high round-trip time (RTT) and optical network unit (ONU) tuning delay need to be solved urgently. In this paper, a multi-thread multiple tuning-time devices coexistence (MT-MTDC) bandwidth allocation algorithm is proposed. This algorithm can solve the problems of high RTT and ONU tuning delay in virtual passive optical network (VPON) based on long-reach wavelength division multiplexing/time division multiplexing passive optical network (LR WDM/TDM PON). Firstly, Multi-threaded polling mechanism is introduced into multi-mode coexistence VPON. Next, the number of wavelengths and threads is selected adaptively to ensure high bandwidth utilization. Then, the mechanism of dynamically adjusting the thread window is proposed. The mechanism strengthens the collaboration ability between threads and solves the degradation problem of the multi-thread algorithm. Furthermore, the construction of tuning buffer and the setting of time flag effectively solve the problem of more frequent ONU tuning pressure and ONU transmission conflict caused by multi-threaded polling algorithm. Finally, by comparing with the multi-thread longest-first first-available (MT-LFFA) algorithm and the multi-tuning-time ONU scheduling (MOS) algorithm, the proposed algorithm demonstrates its effectiveness in terms of polling cycle time, average tuning delay, bandwidth utilization and average packet delay.

Comparative analysis of LTE backbone transport techniques for efficient broadband penetration in a heterogeneous network morphology

In the bid to bring about a solution to the nagging problem associated with the provision of ubiquitous broadband access, Next Generation Network (NGN) popularly referred to as Long Term Evolution (LTE) network with appropriate network integration technique is recommended as solution. Currently, Internet Protocol/Multi-Protocol Label Switching (IP/MPLS) is the transport technique in LTE backbone infrastructure. This technique, however, suffers significantly in the event of failure of IP path resulting in delay and packet loss budgets across the network.The resultant effect is degradation in users’ quality of service (QoS) experience with real-time services. A competitive alternative is the Internet Protocol /Asynchronous Transfer Mode (IP/ATM). This transport technique provides great dynamism in the allocation of bandwidth and supports varying requests of multimedia connections with diverse QoS requirements. This paper, therefore, seeks to evaluate the performance of these two transport techniques in a bid to establish the extent to which the latter technique ameliorates the aforementioned challenges suffered by the previous technique. Results from the simulation show that the IP/ATM transport scheme is superior to the IP/MPLS scheme in terms of average bandwidth utilization, mean traffic drop and mean traffic delay in the ratio of 9.8, 8.7 and 1.0% respectively.

Effective Resource Management in SDN Enabled Data Center Network Based on Traffic Demand

Software-defined networking (SDN) changes the way forwarding devices works and makes data center network simplified and efficient to work. Energy is the main driving force behind the activity of the data center network. The network elements that are in low load condition can be shifted in switch- OFF condition after migrating their active traffic on some other element. This traffic analysis-based energy-saving approach will increase the overall utilization of the system. This paper presents a switch- ON/OFF procedure along with the re-routing procedure to achieve the desired activity. The modules are tested over some well-known data center topologies, such as Fat-Tree and Bcube. The simulation study is carried out with the classical SDN controller instances, e.g., OpenDayLight, POX, Beacon, and NOX. The results are compared with some similar energy-efficient data center approaches, such as MNLR DCN and WDM Optical Switched DCN. The proposed approach presents an enhancement of 77%, 37%, and 63% in achieved energy saving, average round trip delay, and average bandwidth utilization, respectively.

A Distributed NFV-Enabled Edge Cloud Architecture for ICN-Based Disaster Management Services.

In disaster management services, the dynamic binding between roles and individuals for creating response teams across multiple organizations to act during a disaster recovery time period is an important task. Existing studies have shown that IP-based or traditional telephony solutions are not well-suited to deal with such group communication. Research has also shown the advantages of leveraging information centric networking (ICN) in providing essential communication in disaster management services. However, present studies use a centralized networking architecture for disaster management, in which disaster information is gathered and processed at a centralized management center before incident responses are made and warning messages are sent out. The centralized design can be inefficient in terms of scalability and communication. The reason is that when the network is very large (i.e., country level), the management for disaster services becomes very complicated, with a large number of organizations and offices. Disaster data are required to be transmitted over a long path before reaching the central management center. As a result, the transmission overhead and delay are high. Especially when the network is fragmented and network connectivity from a disaster-affected region to the central management center is disconnected, the service may be corrupted. In this paper, we designed and implemented a distributed edge cloud architecture based on ICN and network function virtualization (NFV) to address the above issues. In the proposed architecture, disaster management functions with predefined disaster templates were implemented at edge clouds closed to local regions to reduce the communication overhead and increase the service availability. The real implementation and performance evaluation showed that the proposed architecture achieves a significant improvement in terms of average bandwidth utilization, disaster notification delivery latency, routing convergence time, and successful request ratio compared to the existing approaches.

Comparative Study for Different Provisioning Policies for Load Balancing in Cloudsim

Distributing application requests across applications located in different datacenters with in cloud equally must be provided by cloud load balancing. In this paper, we compare different provisioning policies within cloud for virtual machines and workloads, where we are focusing on how to distribute the processing power between virtual machines and how to distribute workload among virtual machines. Cloudsim is the simulation plate form used to test the different distributions scenarios to check the performance on makespan, average turnaround time, bandwidth utilization and CPU utilization. Result showed the difference in performance between the three tested provisioning schemes, where the space-shared gives better readings for the selected performance metrics.

Dynamic bandwidth allocation based on multiservice in software-defined wavelength-division multiplexing time-division multiplexing passive optical network

The wavelength-division multiplexing passive optical network (WDM-PON) is a potential technology to carry multiple services in an optical access network. However, it has the disadvantages of high cost and an immature technique for users. A software-defined WDM/time-division multiplexing PON was proposed to meet the requirements of high bandwidth, high performance, and multiple services. A reasonable and effective uplink dynamic bandwidth allocation algorithm was proposed. A controller with dynamic wavelength and slot assignment was introduced, and a different optical dynamic bandwidth management strategy was formulated flexibly for services of different priorities according to the network loading. The simulation compares the proposed algorithm with the interleaved polling with adaptive cycle time algorithm. The algorithm shows better performance in average delay, throughput, and bandwidth utilization. The results show that the delay is reduced to 62% and the throughput is improved by 35%.

Analysis of superframe duration adjustment scheme for IEEE 802.15.4 networks

The challenge of the IEEE 802.15.4 beacon-enabled mode is how to improve throughput and bandwidth utilization in contention access period (CAP) and contention free period (CFP), respectively. This article proposes a scheme to improve IEEE 802.15.4 medium access control, called superframe duration adjustment scheme (SUDAS), which analyzes the overall of the IEEE 802.15.4 not only CAP but also CFP. SUDAS is expected to effectively allocate guaranteed time slot to the requested devices, it adjusts the length of the slot in superframe duration based on the length of the packet data. This article also presents a comprehensive Markov chain analysis for SUDAS, especially for star topology, to predict the probability of successful transmission, network goodput, average bandwidth utilization, as well as total network energy consumption. The validity of the analytical model is proven by closely matching the simulation experiments. SUDAS performs better than other algorithms in terms of the probability of successful packet transmission, network goodput, average bandwidth utilization, and total energy consumption.

Comprehensive Performance Assessment of Bipartition Upstream Bandwidth Assignment Schemes in GPON

With the emerging diversified services, the gigabit-capable passive optical network (GPON) has emerged as one of the most important technologies to support the next-generation broadband access networks. Dynamic bandwidth assignment (DBA) avoids transmission collision in the upstream direction and improves the efficiency of the PON upstream bandwidth by dynamically adjusting the bandwidth among the optical network units (ONUs) in response to the ONU burst traffic requirement. Instead of using GATE and REPORT control messages in Ethernet PON DBA, the buffer REPORT/GRANT message is embedded into a GPON encapsulation mode (GEM)/GPON transmission convergence (GTC) frame, and all ONUs are essentially polled simultaneously within a GEM/GTC frame in GPON DBA. This paper proposes a bipartition DBA (BI-DBA) mechanism in GPON that divides the transmission cycle time into two groups and adjusts the bandwidth dynamically. Two cases are considered: in the first case, there is a first group for high priorities T-CONT 1 and T-CONT 2 and a second group for low priorities T-CONT 3 and T-CONT 4, called class based scheduling (CBDBA). In the second case, the first and the second group are created according to the number of ONUs, called group based scheduling (GBDBA). Moreover, the T-CONT 2 traffic has a prediction mechanism and recycles the remaining unused bandwidth for the low-priority T-CONTs. The GBDBA has better system performances in terms of the average bandwidth utilization, packet latency, and drop probability; on the other hand, the CBDBA has better packet delay performance when the proportion of T-CONT 1 traffic is higher and has better fairness performance.

Adaptive Limited Dynamic Bandwidth Allocation Scheme to Improve Bandwidth Sharing Efficiency in Hybrid PON Combining FTTH and Wireless Sensor Networks

This paper proposes a dynamic bandwidth allocation algorithm that improves the network performance and bandwidth sharing efficiency in the upstream channels of a hybrid passive optical network (PON) that combines a fiber-to-the-home (FTTH) access network and wireless sensor networks (WSNs). The algorithm is called the adaptive limited dynamic bandwidth allocation (ALDBA) algorithm. Unlike existing algorithms, the ALDBA algorithm is not limited to controlling just FTTH access networks, it also supports WSNs. For the proposed algorithm, we investigate the difference in the lengths of generated data packets between the FTTH terminals and sensor nodes of WSN to effectively evaluate the end-to-end average packet delay, bandwidth utilization, time jitter, and upstream efficiency. Two variants of the proposed algorithm and a limited service (LS) scheme, which is an existing well-known algorithm, are compared under non-uniform traffic conditions without taking into consideration priority scheduling. We demonstrate the proposed scheme through simulation by generating a realistic network traffic model, called self-similar network traffic. We conducted a detailed simulation using several performance parameters to validate the effectiveness of the proposed scheme. The results of the simulation showed that both ALDBA variants outperformed the existing LS scheme in terms of average packet delay, bandwidth utilization, jitter, and upstream efficiency for both low and high traffic loads.

스마트 이동 단말을 이용한 P2P 기반 그룹 통신 관리

차세대 광대역 무선 네트워크의 성능이 급격히 증가함에 따라 스마트 모바일 플랫폼 기반으로 다양한 서비스 (교육, 비디오 컨퍼런싱, 온라인 게임 등)가 사용자에게 제공되고 있다. 하지만, 대부분의 이러한 서비스들은 중앙 집중식 구조로 서비스가 제공되고 있기 때문에, 트래픽 관리에 대한 확장성 있는 통신 서비스를 제공하는데 어려움이 있다. 기존의 이러한 문제점을 해결하기 위해, 본 논문에서는 스마트 이동 단말을 이용한 P2P 기반 그룹 통신 관리 서비스 구조를 제시한다. 좀 더 구체적으로, P2P 기반의 그룹 사용자 관리와 트래픽 관리를 위한 그룹 통신 관리 프로토콜과 알고리즘을 설계하였다. 제안된 방법을 통해 확장성 있는 멀티미디어 스트리밍 서비스를 사용자에게 제공할 수 있다. 이를 증명하기 위해 평균 전송 지연시간 (Average Transmission Delay) 및 대역폭 이용률 (Bandwidth Utilization) 에 대한 수학적인 분석을 수행하였다.