Bandwidth Allocation Algorithm Research Articles

Adaptive quality of service for packet loss reduction using OpenFlow meters.

Quality of Service (QoS) is a mechanism used in computer networks to prioritize, classify, and treat packets differently based on certain criteria. This helps the switching devices to schedule and reorder packets if there is congestion in the network. Edge routers experience high traffic congestion as a result of traffic aggregation from the internal network devices. A router can have multiple QoS classes configured, and each class could experience traffic at various rates. However, when a QoS class is underperforming or needs more bandwidth, some bandwidth can be borrowed or leased out to another QoS class to ensure the link is utilized to maximum capacity and the highest throughput is achieved. This article proposes a bandwidth allocation and distribution algorithm that purely uses the flow statistics from the OpenFlow switches to allocate bandwidth to different QoS classes optimally based on their current requirement. The algorithm does not guarantee in advance that the packet loss will be minimized but does guarantee the initial minimum bandwidth allocation. It adjusts the flows' rates with the aim to increase their current throughput. The algorithm uses the Software Defined Networking (SDN) controller's flow monitoring component to query the flow statistics from the switch to first approximate the traffic flow rate and then calculate the optimal bandwidth values to assign to each QoS class. The proposed algorithms will be applied to certain switches in the path with the assumption that all the switches are OpenFlow compatible. The algorithm's performance was compared with the Adaptive Quality of Service (AQoS) algorithm over various traffic scenarios. The results show that the proposed algorithm achieves an average of 9% performance gain compared to the AQoS algorithm.

WiMAX Network Optimization Using Frame Period with Channel Allocation Techniques

Internet connectivity in WiMAX networks, along with various applications, is increasing rapidly, so the connectivity of internet and data transfer speed are always challenge for effective data transmission in wireless networks. Several factors affect the performance of networks. One important factor is to choose a suitable frame period for effective data transmissions. The performance of different frame period with round-robin and strict priority are evaluated in this work. A frame period in round robin performs better than a strict priority in terms of throughput, but a strict priority performs better in terms of drop rates is observed. This paper also demonstrates that an effective frame period, when combined with a proper bandwidth allocation algorithm, yields better results. This work gives the analysis that round robin performs 83.8847% better while strict priority performs 86.0020% better than the earliest deadline first algorithms for 10 subscriber stations in terms of throughput. This work is helpful to researchers and industrialists for actual implementations in WiMAX networks.

KMSSA optimization algorithm for bandwidth allocation in internet of vehicles based on edge computing

KMSSA optimization algorithm for bandwidth allocation in internet of vehicles based on edge computing

A modified bandwidth allocation algorithm for XGPON network

Abstract In the current era of everything online, home-users are demanding high speed internet access at their figure tip. The giga-bit (XGPON) fiber based network has proven its capacity to provide higher speed internet at a light-speed. However in such network effectiveness is directly relies on the bandwidth allocation to the each of the user in timely manner. In this paper, modified bandwidth allocation algorithm is proposed to prove its capacity to support larger user with effective bandwidth allocation. The throughput result of this proposed DBA is carried out in network simulator-3 (NS-3), which is improved by 3 % over the existing DBA like Giant, Proportional DBA etc., at little increase in the delay performance of the TCONT-4 based traffic.

DITRA: an efficient event-driven multi-objective optimization algorithm for bandwidth allocation in IoT environments

DITRA: an efficient event-driven multi-objective optimization algorithm for bandwidth allocation in IoT environments

Scalable and resilient 360-degree-video adaptive streaming over HTTP/2 against sudden network drops

Realistic Virtual Reality is supported by 360° video, which provides viewers with an immersive watching experience. However, 360°video is bulky in size, while the transmission system has limited ability to provide bandwidth. As such, intelligent adaptive delivery solutions play a crucial role in enabling users to stream high-quality 360°video. In this research, we propose a novel approach for 360-degree video streaming over HTTP/2 that can provide consumers with a good watching experience (QoE) even in varying network circumstances and head-eye movements over time. The proposed method deploys the so-called BBAG algorithm (Buffer and Bandwidth Allocation Algorithm) using Scalable Video Coding to choose appropriate tile layers to resolve the trade-off between network and user adaptivity. With the support of HTTP/2’s stream termination capability, the delivery of late tile layers is terminated to handle abrupt interruptions. By employing multiple buffer thresholds, BBAG is able to adapt bitrates to changes in users’ perspective while watching a 360-degree video. BBAG is proven to improve QoE up to about 90% by maintaining high and stable buffer level, while enhancing average viewport bitrates by about 80% compared to state-of-the-art methods in different scenarios of network bandwidths.

Traffic offloading for postdisaster rescue in UAV‐assisted networks: A coalition formation game approach

SummaryIn postdisaster rescue scenario, unmanned aerial vehicles (UAVs) are effective tools to help ground users in disaster areas to transmit rescue‐critical data to the relief center in time due to their flexible mobilities and fast deployment. However, how UAVs choose disaster areas to take part in traffic offloading with limited bandwidth resources is challenging. This article investigates UAVs' access selection and disaster areas' bandwidth allocation scheme by jointly optimizing network throughput, bandwidth and UAVs' energy cost. The UAVs form coalitions to participate in traffic offloading cooperatively, where each UAV selects a disaster area independently. Specifically, the bandwidth resources are dynamically allocated to all disaster areas when a new coalition partition forms. The UAVs adjust their access choices for disaster areas when the bandwidth allocation changes. When no UAVs migrate to other disaster areas, the network achieves a stable state. The UAVs fly to the final selected disaster area and provide traffic offloading services. To resolve traffic offloading competitions yet enable cooperations among UAVs, we address the UAVs' access selection issue by coalition formation game. A gradient projection method is then proposed to allocate bandwidth resources which maximizes the benefit of the network. We demonstrate that the UAVs' access selection and disaster areas' bandwidth allocation algorithms are convergent. The simulation results demonstrate that our proposed cooperation order is better than the pareto and selfish orders, thereby increasing the benefit of the network.

Dynamic bandwidth allocation algorithm based on extreme learning machine in passive optical networks

Dynamic bandwidth allocation algorithm based on extreme learning machine in passive optical networks

Auction-based hybrid DBA algorithm in EPON-WiMAX converge network for 5G and beyond (5 GB) communications

Managing large data volumes requires high-capacity networks due to increasing internet users and bandwidth-intensive applications. Optical fiber, particularly Ethernet passive optical network (EPON), is vital for cost-efficient last-mile connectivity. EPON handles data traffic, while WiMAX demands extensive bandwidth due to rising mobile users. Both the networks experience bursty user-generated traffic with fluctuating bandwidth needs. That’s why, the bandwidth requirements of each subscriber fluctuates dynamically. Moreover, access networks must provide distinct service quality to support a large number of users and ensure the requirement of 5G and beyond (5 GB) communication standards. To address these difficulties, in this paper, we propose a prediction-based hybrid dynamic bandwidth allocation (PHDBA) algorithm for EPON-WiMAX converge network. Due to the increasing value of trustworthy network service access, we have implemented a statistical modeling technique. Our technique predicts the heavily loaded ONUs from a randomly generated dataset and provides an approximation of the amount of data that can be strongly loaded. To make this prediction, we have proposed a statistical modeling technique and updated the auction-based semi dynamic bandwidth allocation (SDBA) algorithm. To validate our proposed algorithm, computer simulation is used to evaluate different performance parameters in terms of end-to-end delay, jitter, throughput, and fairness. From the comparative analyses, it is proved that our proposed scheme surpasses the competition over the existing SDBA algorithm.

Joint User Grouping and Resource Allocation for LEO Satellite Multicast

Similar to the dilemma encountered by terrestrial wireless networks, low Earth orbit (LEO) satellite networks are also facing the problem of increasing pressure caused by the growth of wireless traffic. Wireless multicast technology is an effective technique on improving the spectrum efficiency by sending the same signal to multiple users who request the same service. In this article, the LEO satellite transmission system is considered and a maximum system capacity problem is formulated. To solve the original nonconvex problem efficiently, a joint user grouping and resource allocation scheme is proposed. There are two parts in the scheme: on the one hand, we propose an improved K-means++ algorithm for user grouping; on the other hand, we proposed a joint power and bandwidth allocation algorithm using the two stage iteration method. Compared with the baseline approach, our proposed scheme can improve the capacity 10% higher at least.

Edge Video Analytics With Adaptive Information Gathering: A Deep Reinforcement Learning Approach

With growing popularity of enormous public safety and transportation infrastructure cameras, there are increasing demands for automatic mobile video analytics. The emerging multi-access edge computing (MEC) technology has been recently applied to improve the accuracy-latency tradeoff of mobile video analytics. In this paper, we study an MEC-enabled multi-device video analytics system and formulate the problem as a Markov decision process (MDP) to meet two practical challenges: i) the absence of ground truth in real-time and ii) the content-varying degradation-accuracy relation. In particular, we aim to design an online joint frame degradation and bandwidth allocation algorithm with the time-varying function and limited feedback from each device. Thanks to the MDP formulation and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> -step return technique, the long-term goal offers adaptive information gathering and thus improves the average accuracy and latency. For sample efficiency, we decompose the MDP problem into discrete degradation adaptation subproblems and continuous bandwidth allocation subproblems. Based on the decomposition, we propose a deep reinforcement learning (DRL) based framework, referred to as DBAG, to solve the decomposed subproblems. DBAG integrates model-based optimization and model-free DRL to solve the MDP problem with a discrete-continuous hybrid action space. Under various network setups and public datasets, DBAG greatly improves the accuracy-latency tradeoff.

Computational Intelligence and Deep Learning for Next-Generation Edge-Enabled Industrial IoT

In this paper, we investigate how to deploy computational intelligence and deep learning (DL) in edge-enabled industrial IoT networks. In this system, the IoT devices can collaboratively train a shared model without compromising data privacy. However, due to limited resources in the industrial IoT networks, including computational power, bandwidth, and channel state, it is challenging for many devices to accomplish local training and upload weights to the edge server in time. To address this issue, we propose a novel multi-exit-based federated edge learning (ME-FEEL) framework, where the deep model can be divided into several sub-models with different depths and output prediction from the exit in the corresponding sub-model. In this way, the devices with insufficient computational power can choose the earlier exits and avoid training the complete model, which can help reduce computational latency and enable devices to participate into aggregation as much as possible within a latency threshold. Moreover, we propose a greedy approach-based exit selection and bandwidth allocation algorithm to maximize the total number of exits in each communication round. Simulation experiments are conducted on the classical Fashion-MNIST dataset under a non-independent and identically distributed (non-IID) setting, and it shows that the proposed strategy outperforms the conventional FL. In particular, the proposed ME-FEEL can achieve an accuracy gain up to 32.7&#x0025; in the industrial IoT networks with the severely limited resources.

Adaptive Bandwidth Allocation for Massive MIMO Systems Based on Multiple Services

Aiming at the characteristics of resource periodicity in massive MIMO systems and bandwidth allocation without comprehensive consideration of user service QoS and channel state information, resulting in poor user satisfaction and low bandwidth utilization, this paper proposes an adaptive bandwidth allocation method based on user services. This method comprehensively considers factors, such as user service QoS, channel state information, and resource periodicity, to adaptively allocate bandwidth for users using different services. Firstly, based on the service priority, the user priority is dynamically adjusted according to the current channel state information and the continuous periodicity of the allocation, and the user is scheduled.; Secondly, the dynamic priority is combined with the minimum guaranteed time slot to establish the objective function of adaptive bandwidth allocation. Finally, chaos theory, Levy flight, and reverse learning are integrated to improve the bald eagle optimization algorithm. The improved bald eagle algorithm is used to solve the problem, and the optimal solution to bandwidth allocation is obtained. The simulation shows that compared with the traditional bandwidth allocation method based on user service quality perception, the bandwidth allocation algorithm based on the minimum rate requirement, and the ant colony-based allocation algorithm, the bandwidth allocation method proposed in this paper improves the system utility value, bandwidth utilization rate, throughput, and user satisfaction by 23.70%, 4.22%, 6.55%, and 4.28%, respectively, and better meets the business needs of users.

A Distance-Weighted Dynamic Bandwidth Allocation Algorithm for Improved Performance in Long-Reach Passive Optical Networks for Next Generation Networks

In recent years, there has been an increasing trend towards extending the coverage of passive optical networks (PONs) over large geographical areas. Long-reach PONs (LRPONs) are capable of extending the distance covered by PONs from 20 km to 100 km, leading to cost savings in the network operation by reducing the number of central offices. They have become widely deployed due to their ability to provide high-speed, long-distance data transmission over optical fibers. In addition, the next generation of optical access networks are expected to provide high-capacity mobile and wireless backhauling over a wide coverage area. However, this extended reach also requires the design of efficient dynamic bandwidth allocation (DBA) schemes to address the performance degradation caused by the increased propagation delay in LRPONs. The DBA schemes commonly used for upstream traffic transmission in traditional PONs are not well-suited for use in LRPONs due to their inefficiency in bandwidth utilization due to the increased round-trip time (RTT) between the optical line terminal (OLT) and the optical network unit (ONU). In this study, we present an efficient DBA algorithm, the Distance-Weighted Bandwidth Allocation DWDBA Algorithm, specifically enhanced for multi-wavelength LRPONs. Our DBA algorithm utilizes a scheduling policy that assigns weight vectors to Optical Network Units (ONUs) based on their distance from the Optical Line Terminal (OLT), sorting them accordingly without penalizing any ONU due to their distance. The DWDBA takes the laser tuning time into consideration. We conducted extensive simulations to evaluate the performance of the proposed algorithm under various scenarios and compared it to the IPACT algorithm. The results of the simulations show that the proposed algorithm outperformed the IPACT algorithm in terms of bandwidth utilization and queue delay.

Optimization of the Decision Criterion for Increasing the Bandwidth Utilization by Means of the Novel Effective DBA Algorithm in NG-PON2 Networks

In this paper, the reasons for the bandwidth and wavelength utilization in future next-generation passive optical networks are presented, and the possibilities for realization and utilization of extended dynamic wavelength and bandwidth algorithms for the second next-generation passive optical networks (NG-PON2) are analyzed. Next, principles of the effective dynamic bandwidth allocation are introduced in detail, focused on the importance of the decision criterion optimization. To achieve a better bandwidth utilization of dedicated wavelengths in NG-PON2 networks, this paper is focused on the novel effective dynamic bandwidth allocation algorithm with adaptive allocation of wavelengths to optical network units as well as the optimization of the decision criterion. The algorithm and the proposed method are tested and evaluated through simulation with actual traffic data. For analyzing novel extended dynamic wavelength and bandwidth algorithms used for various cases of wavelength allocation in NG-PON2 networks, the effective dynamic bandwidth allocation algorithm analysis is realized in the enhancement of simulation program. Finally, an optimization of the decision criterion defining a minimum bandwidth utilization of the actual wavelength is executed for NG-PON2 networks based on the hybrid time and wavelength division multiplexing technique.

Real-time batch processing at a GPU-based edge with a passive optical network

In recent years, advances in deep learning technology have significantly improved the research and services relating to artificial intelligence. Real-time object recognition is an important technique in smart cities, in which low-cost network deployment and low-latency data transfer are key technologies. In this study, we focus on time- and wavelength-division multiplexed passive optical network (TWDM-PON)-based inference systems to deploy cost-efficient networks that accommodate several network cameras. A significant issue for a graphics processing unit (GPU)-based inference system via a TWDM-PON is the optimal allocation of the upstream wavelength and bandwidth to enable real-time inference. However, an increase in the batch size of the arrival data at the edge servers, thereby ensuring low-latency transmission, has not been considered previously. Therefore, this study proposes the concept of an inference system in which a large number of cameras periodically upload image data to a GPU-based server via the TWDM-PON. Moreover, we propose a cooperative wavelength and bandwidth allocation algorithm to ensure low-latency and time-synchronized data arrivals at the edge. The performance of the proposed scheme is verified through a computer simulation.

Joint Power and Bandwidth Allocation in Collocated MIMO Radar Based on the Quality of Service Framework

The simultaneous multi-beam working mode of the collocated multiple-input and multiple-output (MIMO) radar enables the radar to track multiple targets simultaneously. A joint power and bandwidth allocation algorithm in a collocated MIMO radar based on the quality of service (QoS) framework is proposed for the multi-target tracking problem with different threat levels. Firstly, a posterior Cramer–Rao lower bound (PCRLB) concerning the power and bandwidth is derived. In addition, the optimal objective functions of power and bandwidth are designed based on the QoS framework, and the problem is solved using the convex relaxation technique and the cyclical minimization algorithm. The numerical results show that the proposed algorithm has better tracking accuracy and achieves more reasonable resource allocation compared to strategies such as average allocation.

Joint Client Selection and Bandwidth Allocation Algorithm for Federated Learning

Joint Client Selection and Bandwidth Allocation Algorithm for Federated Learning

Joint user association and caching placement for cache-enabling UAV networks

Cache-enabling unmanned aerial vehicles (UAVs) are considered for storing popular contents and providing downlink data offloading in cellular networks. In this context, we formulate a joint optimization problem of user association, caching placement, and backhaul bandwidth allocation for minimizing content acquisition delay with consideration of UAVs' energy constraint. We decompose the formulated problem into two subproblems: i) user association and caching placement and ii) backhaul bandwidth allocation. We first obtain the optimal bandwidth allocation with given user association and caching placement by the Lagrangian multiplier approach. After that, embedding the backhaul bandwidth allocation algorithm, we solve the user association and caching placement problem by a three-dimensional (3D) matching method. Then we decompose it into two two-dimensional (2D) matching problems and develop low-complexity algorithms. The proposed scheme converges and exhibits a low computational complexity. Simulation results demonstrate that the proposed cache-enabling UAV framework outperforms the conventional UAV-assisted cellular networks in terms of content acquisition delay and the proposed scheme achieves significantly lower content acquisition delay compared with other two benchmark schemes.

Bandwidth Allocation Algorithm for Makespan Optimization in a Fog-Cloud Environment: Monitoring Application

Fog computing technology has emerged to handle a large amount of data generated by the Internet of Things (IoT) terminals and cope with latency-sensitive application requests by allocating computation and storage resources at the edge of the Internet. In many IoT applications, the data acquisition procedures must apply the Directed Acyclic Graph (DAG) to get real-time results. The principal goal of DAG scheduling is to reduce total completion time without breaking priority constraints by properly allocating tasks to processors and arranging task execution sequencing. In this paper, we propose a bandwidth-aware workflow allocation (BW-AWA) that schedules tasks by priority to the resource and optimizes the total execution time (Makespan) in the entire computing system. The task allocation process needs to consider the dependency between tasks. The proposed approach is tested with a monitoring application case study, and the results are compared to well-known approaches to demonstrate its effectiveness in optimizing the Makespan.