Link Bandwidth Research Articles

Joint Service Caching, Resource Allocation and Computation Offloading in Three-Tier Cooperative Mobile Edge Computing System

Mobile Edge Computing (MEC) provides cloud-like computing functionalities for mobile devices (MDs) by deploying servers at the edge of future 6 G network. The cooperation among multi-servers and remote cloud offers low-latency services for MDs, meeting the demand of numerous delay-sensitive computation tasks. However, the limited storage capacity of edge servers makes it difficult to cache all kinds of services from the cloud simultaneously. Furthermore, the available bandwidth of communication links and the computation capacity of edge servers are insufficient for all MDs. In this paper, we consider a multi-server MEC scenario, where exists the competitions of multiple MDs for communication and computation resource, and various services for storage capacity. We investigate task offloading and resource allocation strategy in the three-tier MEC system, and explore the service caching strategy of the edge server. We aim at minimizing the cost of delay and energy consumption, and formulate the joint optimization problem as Markov Decision Process (MDP). Based on Double Deep Q-Network (DDQN) algorithm, the joint service caching, resource allocation and computation offloading scheme (SCRACO) is proposed to solve the problem. The simulation results demonstrate that the proposed scheme can effectively reduce total cost of the system compared with other three benchmarks.

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.

Large Scale LEO Satellite Networks for the Future Internet: Challenges and Solutions to Addressing and Routing

This document analyses the problems and challenges of addressing and routing to Low Earth Orbit (LEO) satellites used for the future Internet. It focuses on the scenario where Inter-Satellite-Links (ISL) are used for massive LEO satellite constellation. Such LEO satellite network is the key to the Non-terrestrial Network (NTN) integration with 5G and beyond. The paper describes two categories of solutions. One is based on IPv6 and another is based on a new protocol, New IP. The two categories of solutions are a) using new types of addresses for LEO satellites and b) new concepts for routing with these addresses. The semantic addressing scheme leverages the characteristics of satellites with known orbit elements; and simplifies the satellite identification by using limited indexes. The routing method combines semantic addressing with source routing and generates a new semantic routing scheme. Compared with traditional methods, the new proposals dramatically reduce the workload in satellite, such as table size, Ternary Content-addressable memory (TCAM) lookups, and packet header size. Thus it is more suitable to networks in space where the harsh environment limits the hardware performance, power consumption, link bandwidth and system complexity.

Approach for Designing Real-Time IoT Systems

Along with the rapid development of Internet of Things (IoT) technology over the past few years, opportunities for its implementation in service areas that require real-time requirements have begun to be recognized. In this regard, one of the most important criteria is to maintain Quality of Service (QoS) parameters at an appropriate and sufficiently high level. The QoS level should ensure the delivery of data packets in the shortest time possible while preventing critical parameters relevant to real-time transmission from being exceeded. This article proposes a new methodology for designing real-time IoT systems. The premise of the proposed approach is to adapt selected solutions used in other types of systems working with real-time requirements. Some analogy to embedded systems with a distributed architecture has been noted and used in this regard. The main differences from the concept of built-in systems can primarily be seen in the communication layer. The methodology proposed in this article is based on the authors’ proposed model of real-time system functional specification and its mapping to the IoT architecture. In addition, the developed methodology makes extensive use of selected IoT architecture elements described in this article, as well as selected task scheduling methods and communication protocols. The proposed methodology for designing RTIoT systems is based on dedicated transmission serialization methods and dedicated routing protocols. These methods ensure that the time constraints for the assumed bandwidth of IoT links are met by appropriately prioritizing transmissions and determining communication routes. The presented approach can be used to design a broad class of RTIoT systems.

SF-Partition-Based Clustering and Relaying Scheme for Resolving Near–Far Unfairness in IoT Multihop LoRa Networks

Long range (LoRa) is one of the most successful low-power wide-area networking technologies because it is ideally suited for long-distance, low-bit rate, and low-power communications in the unlicensed sub-GHz spectrum utilized for Internet of things (IoT) networks. The effectiveness of LoRa depends on the link budget (i.e., spreading factor (SF), bandwidth (BW), and transmission power (TX)). Due to the near-far effect, the allocation of a link budget to LoRa devices (LDs) in large coverage regions is unfair between them depending on their distance to the GW. Thus, more transmission opportunities are given to some LDs to the detriment of other LD's opportunities. Numerous studies have been conducted to address the prevalent near-far fairness problem. Due to the absence of a tractable analytical model for fairness in the LoRa network, however, it is still difficult to solve this problem completely. Thus, we propose an SF-partition-based clustering and relaying (SFPCR) scheme to achieve enormous LD connectivity with fairness in IoT multihop LoRa networks. For the SF partition, the SFPCR scheme determines the suitable partitioning threshold point for bridging packet delivery success probability gaps between SF regions, namely, the lower SF zone (LSFZ) and the higher SF zone (HSFZ). To avoid long-distance transmissions to the GW, the HSFZ constructs a density-based subspace clustering that generates clusters of arbitrary shape for adjacent LDs and selects cluster headers by using a binary score representation. To support reliable data transmissions to the GW by multihop communications, the LSFZ offers a relay LD selection that ideally chooses the best relay LD to extend uplink transmissions from LDs in the HSFZ. Through simulations, we show that the proposed SFPCR scheme exhibits the highest success probability of 65.7%, followed by the FSRC scheme at 44.6%, the mesh scheme at 34.2%, and lastly the cluster-based scheme at 29.4%, and it conserves the energy of LDs compared with the existing schemes.

Satellite IoT Based Road Extraction from VHR Images Through Superpixel-CNN Architecture

In the past few decades, technology has progressively become ineluctable in human lives, primarily due to the growth of certain fields like space technology, Big Data, the Internet of Things (IoT), and machine learning. Space technology has revolutionized communication mechanisms while creating opportunities for various research areas, including remote sensing (RS)-inspired applications. On the other hand, IoT presents a platform to use the power of the internet over a whole range of devices through a phenomenon known as social IoT. These devices generate a humongous amount of data that requires handling and managing by big data technology incorporated with deep learning techniques to reduce the manual workload of an operator. Moreover, deep learning architectures like convolutional neural networks (CNNs) have presented a scope to extract the underlying features from the large-scale input images in providing better solutions for tasks such as automatic road detection that come at the cost of time and memory overhead. In this context, we have proposed a three-layer edge-fog-cloud-based intelligent satellite IoT architecture that uses the superpixel-based CNN approach. At the fog layer, the superpixel-based simple linear iterative cluster (SLIC) algorithm uses the images captured by the satellites of the edge level to produce the smaller-sized superpixel images that can be transferred even in a low bandwidth link. The CNN module at the cloud level is then trained with these superpixel images to predict the road networks from these RS images. Two popular road datasets: the DeepGlobe Road dataset and the Massachusetts Road dataset, have been considered to prove the usefulness of the proposed SLIC-CNN architecture in satellite-based IoT platforms to address the problems like RS image-based road extraction. The proposed architecture achieves better performance accuracy than the classical CNN while reducing the incurred overhead by a noticeable limit. • Presents a superpixel-inspired deep learning-based architecture that optimizes the time and memory overhead. • Presents the three-layer edge-fog-cloud-based satellite IoT architecture to deploy the proposed SLIC-CNN framework. • Discusses the efficiency of the proposed deep architecture to work in a low bandwidth environment for road segmentation. • Highlights the application areas such as emergency alarm systems that can use the architecture to operate in real-time.

LoRaWAN Network Downlink Routing Control Strategy Based on the SDN Framework and Improved ARIMA Model

In order to improve the downlink communication performance of the traditional LoRa wide area network (LoRaWAN), a LoRaWAN downlink routing control strategy based on the software defined networks (SDN) framework and the improved auto-regressive integrated moving average (ARIMA) model is proposed. The SDN architecture is used to monitor the network traffic, and the link bandwidth occupancy rate is calculated based on the monitored downlink traffic. Taking into account the impact of data volatility on the accuracy of the prediction results, the Savitzky–Golay (S–G) smoothing filter and the sliding window method are introduced for data pre-processing. Stationarity processing is carried out for the time series data in the window, and the ARIMA model is developed to predict the downlink bandwidth occupancy rate. The triangle module operator is then used to incorporate multiple path parameters to finally calculate the selectivity of different paths, and the optimal path for LoRaWAN downlink communication is then provided. Simulation and experimental results show that the root mean square error of the improved ARIMA prediction model is reduced by 87% compared with the standard ARIMA model. The proposed routing control strategy effectively reduces the service transmission delay and packet loss rate. In the LoRaWAN test environment, as the downlink load rate increases, the average link bandwidth occupancy rate of this solution increases by 12% compared with the traditional method.

A Cooperative Routing Scheme Using Inter-Satellite Links to Assist Data Downloading for LEO Satellite Networks.

Low earth orbit (LEO) satellite networks can provide Internet service to users in areas where cellular networks are difficult to deploy. One critical function of satellites is to transfer data from satellite networks to ground core network through earth stations (ESs). The Ka-band multiple-input multiple-output (MIMO) can be used to establish feeder links with larger bandwidth between satellites and ESs. However, propagation at the Ka band is subjected to rain attenuation and various atmospheric fading mechanisms, which severely reduce the maximum link capacity. As a result, the insufficient capacity of feed link becomes the throughput bottleneck of satellite networks. In order to increase network throughput, it is important to fully use feeder link resources. In this paper, we propose a cooperation scheme to route packets to ESs through a well-resourced feeder link, such that the bandwidth of the feeder links can be fully utilized and the throughput of data downloading at the ESs is maximized. Firstly, we model the satellite network system and the feeder link based on MIMO technology. Then, a Maximum-Flow-Minimum-Cost (MCMF) routing algorithm consisting of two Linear Programs (LPs) is presented to compute maximum-flow routings for data download. Eventually, a variety of simulations are conducted to assess the proposed scheme, which shows that the cooperative routing scheme outperforms the existing SiRRS method in terms of throughput and delay.

Enabling circuit-switching in modern on-chip networks

Contemporary flow control mechanisms, especially virtual channels, are employed by modern on-chip networks to allow better utilization of the link bandwidth through buffering and packet switching. Despite of being the de facto option in designs, they are also the sources of large energy footprint and long per-hop latency. On the other hand, dated and simpler flow controls such as circuit switching can draw far less power while offering an end-to-end latency analogous to wire delay. In this paper, we therefore take a further step from utilizing a single flow control mechanism within the network to allowing circuit-switching to work alongside virtual channels, which provides latency-competitive and energy-efficient on-chip network designs. Furthermore, to enable circuit switching in modern networks-on-chip, we present two independent proposals so that circuits can be formed in a network either temporally or spatially. In our evaluations, we find that both of our proposals can help achieve a very competitive latency per flit, for up to 30% lower with the temporal approach and for up to 10% lower with the spatial approach, while also dramatically suppressing the energy footprint with either approach by up to 27% and 25%, respectively.

DRL-M4MR: An intelligent multicast routing approach based on DQN deep reinforcement learning in SDN

Traditional multicast routing methods have some problems in constructing a multicast tree. These problems include limited access to network state information, poor adaptability to dynamic and complex changes in the network, and inflexible data forwarding. To address these defects, the optimal multicast routing problem in software-defined networking (SDN) is tailored as a multiobjective optimization problem, and DRL-M4MR, an intelligent multicast routing algorithm based on the deep Q network (DQN) deep reinforcement learning (DRL) method is designed to construct a multicast tree in a software-defined network. First, combining the characteristics of SDN global network-aware information, the multicast tree state matrix, link bandwidth matrix, link delay matrix and link packet loss rate matrix are designed as the state space of the reinforcement learning agent to solve the problem in that the original method cannot make full use of network status information. Second, the action space of the agent is all the links in the network, and the action selection strategy is designed to add the links to the current multicast tree in four cases. Third, single-step and final reward function forms are designed to guide the agent to make decisions to construct the optimal multicast tree. The double network architectures, dueling network architectures and prioritized experience replay are adopted to improve the learning efficiency and convergence of the agent. Finally, after the DRL-M4MR agent is trained, the SDN controller installs the multicast flow entries by reversely traversing the multicast tree to the SDN switches to implement intelligent multicast routing. The experimental results show that, compared with existing algorithms, the multicast tree constructed by DRL-M4MR can obtain better bandwidth, delay, and packet loss rate performance after training, and it can make more intelligent multicast routing decisions in a dynamic network environment. Code and DRL model are available at https://github.com/GuetYe/DRL-M4MR.

A DRL-Driven Intelligent Optimization Strategy for Resource Allocation in Cloud-Edge-End Cooperation Environments

Complex dynamic services and heterogeneous network environments make the asymmetrical control a curial issue to handle on the Internet. With the advent of the Internet of Things (IoT) and the fifth generation (5G), the emerging network applications lead to the explosive growth of mobile traffic while bringing forward more challenging service requirements to future radio access networks. Therefore, how to effectively allocate limited heterogeneous network resources to improve content delivery for massive application services to ensure network quality of service (QoS) becomes particularly urgent in heterogeneous network environments. To cope with the explosive mobile traffic caused by emerging Internet services, this paper designs an intelligent optimization strategy based on deep reinforcement learning (DRL) for resource allocation in heterogeneous cloud-edge-end collaboration environments. Meanwhile, the asymmetrical control problem caused by complex dynamic services and heterogeneous network environments is discussed and overcome by distributed cooperation among cloud-edge-end nodes in the system. Specifically, the multi-layer heterogeneous resource allocation problem is formulated as a maximal traffic offloading model, where content caching and request aggregation mechanisms are utilized. A novel DRL policy is proposed to improve content distribution by making cache replacement and task scheduling for arriving content requests in accordance with the information about users’ history requests, in-network cache capacity, available link bandwidth and topology structure. The performance of our proposed solution and its similar counterparts are analyzed in different network conditions.

Frequency Response Tailoring of a Phase Modulated Link With Stimulated Brillouin Scattering

Phase-modulated analog photonic links with Mach Zehnder interferometers are used for signal transport and to reduce transmitter complexity. However, the frequency response of these links suffers from dispersion-induced transmission nulls, severely limiting the operational bandwidth. To overcome this challenge, we propose a method to compensate for the transmission dips of phase-modulated links. The operating bandwidth of an analog photonic link formed by a phase modulator and a Mach Zehnder interferometer is increased by superimposing a stimulated Brillouin scattering gain response over the transmission null, resulting in compensation of the null response. The proposed approach is simulated and experimentally verified for different delays of the interferometer and multiple null compensation is also demonstrated. Tunability of the null compensation is also shown for the L, S, C, X, Ku, and Ka bands. Furthermore, link gain improvement is demonstrated through balanced detection.

NLPC: A nimble low-priority congestion control algorithm for high-speed and lossy networks

Thousands of bursty and concurrent flows persistently overwhelm the bottleneck bandwidth. The quality of experience of low-latency applications (e.g. Web, AR/VR, IoT, VoIP, etc.) is inevitably influenced by the background applications (e.g. file-sharing, system updates) which tend to overfill the buffer of bottleneck links. To solve the above issues, Less-than-Best-Effort (LBE) protocols were proposed to support the data transmission of background applications. Therein, Low Extra Delay Background Transport (LEDBAT) is one of the most widely deployed LBE protocols. To achieve the low-priority feature, LEDBAT senders saturate the residual bandwidth of bottleneck links while keeping the low queuing delay. However, through the simulation, we observed that LEDBAT performs a severe throughput degradation in high-speed and lossy networks. To resolve this issue, an enhanced LEDBAT, named NLPC, is presented in this paper. NLPC introduces a variable gain value, which can dynamically adapt to the network congestion degree, to update the sending rate. Extensive simulations in NS-2 and some small-scale real-world experiments indicate that in high-speed and lossy networks, NLPC increases throughput up to 40% in comparison with LEDBAT, and maintains the low-priority feature as the other LBE protocols.

Measuring Millimeter Wave Based Link Bandwidth Fluctuations During Indoor Immersive Experience

This letter presents the results of motion-tracking synchronized millimeter wave (mmWave) link bandwidth fluctuations while a user is engaged in immersive augmented/virtual reality applications. Our system, called MITRAS, supports extensive exploration of human-induced impacts on mmWave link bandwidth during immersive experience. MITRAS adopts the packet train measurement application to track link bandwidth fluctuations. Meanwhile, the user movements are tracked using an Oculus Quest 2 headset. Through investigating the impacts of human movements on link bandwidth fluctuations, we further propose a link state prediction model to shed light on higher layer protocol design for immersive applications over mmWave links.

A Novel Decomposed Optical Architecture for Satellite Terrestrial Network Edge Computing

Aiming at providing a high-performance terrestrial network for edge computing in satellite networks, we experimentally demonstrate a high bandwidth and low latency decomposed optical computing architecture based on distributed Nanoseconds Optical Switches (NOS). Experimental validation of the decomposed computing network prototype employs a four-port NOS to interconnect four processor/memory cubes. The SOA-based optical gates provide an ON/OFF ratio greater than 60 dB, enabling none-error transmission at a Bit Error Rate (BER) of 1 × 10−9. An end-to-end access latency of 122.3 ns and zero packet loss are obtained in the experimental assessment. Scalability and physical performance considering signal impairments when increasing the NOS port count are also investigated. An output OSNR of up to 30.5 dB and an none-error transmission with 1.5 dB penalty is obtained when scaling the NOS port count to 64. Moreover, exploiting the experimentally measured parameters, the network performance of NOS-based decomposed computing architecture is numerically assessed under larger network scales. The results indicate that, under a 4096-cube network scale, the NOS-based decomposed computing architecture achieves 148.5 ns end-to-end latency inside the same rack and zero packet loss at a link bandwidth of 40 Gb/s.

A Heuristic Fuzzy Based 5G Network Orchestration Framework for Dynamic Virtual Network Embedding

Network slicing has become an unavoidable requirement for allocating 5G mobile network resources when sharing resources among devices that have varying needs. As a result, the virtual network slices get resources from the shared physical infrastructure that matches their needs. In order to maximize the use of shared resources, it is critical to provide an efficient virtual network embedding strategy for mapping each user’s requests to a physical infrastructure. Virtual network embedding primarily deals with the two most important network parameters—node mapping and link mapping. This paper proposes the heuristic fuzzy algorithm for node mapping and Dijikstra’s algorithm for link mapping. The proposed fuzzy based multi-criteria decision making technique uses membership functions for node parameters to prepare node mapping. By determining the shortest path, Dijikstra’s algorithm is used to provide link mapping. The proposed strategy is tested under dynamic physical infrastructure conditions for validation. The average acceptance ratio, costrevenue ratio, and average utilization of node capacity and link bandwidth are used to evaluate the performance of the proposed strategy. In addition, the obtained results are compared to the literature to show that the proposed strategy is effective.

Coherent Wireless Link at 300 GHz With 160 Gbit/s Enabled by a Photonic Transmitter

The increasing demand for high-capacity wireless communication requires data links at millimeter waves and terahertz frequencies, respectively. At those frequencies, electronic and photonic technologies compete to prove powerful transmitters and receivers. In this work, we demonstrate a wireless link at 300 GHz using a fiber-coupled PIN photodiode as the transmitter. Thus, the whole emitter side is based on components and techniques from standard fiber-optical communication, which inherently enable broadband data channels. We investigated two antenna designs with amplitude modulated and coherent data signals. Despite similar characteristics in terms of output power and carrier bandwidth, the quality of the data signals differed significantly. In addition, we found that the bit-error ratio (BER) scales non-monotonically with the optical input power of the photodiode, which is proportional to the terahertz output power. Depending on the modulation format and the symbol rate, we identified the optimal driving conditions of the photodiode. For amplitude modulation at 5 Gbit/s, we achieved error-free transmission with a BER of 7.5 × 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−13</sup> . QPSK modulation was error-free up to 64 Gbit/s. The highest line rate of 160 Gbit/s was achieved with 32QAM modulation. This corresponds to 133 Gbit/s net data rate after forward-error correction with 20% overhead. The highest spectral efficiency was achieved with 64QAM at 8 GBaud, i.e., 48 Gbit/s line rate. The presented results highlight the high bandwidth of photonic wireless THz links. Furthermore, the carefully analysis helps to improve the quality of future wireless links in the 300 GHz band.

Adaptive subcarrier intensity modulation for free space optical communication

Increased capacity demands and radio frequency (RF) congestion impacts on current communication networks have brought greater attention to free-space optical (FSO) communication as a viable augmentation technology for terrestrial, aerial, and space-based communication infrastructure. As a complementary alternative to RF communication systems, FSO can support high link bandwidths and provide high data security without RF spectral constraints. The performance of FSO links, however, can be significantly impacted by receive power variation caused by propagation and scattering losses along with losses due to atmospheric turbulence. Depending on the FSO application, these loss mechanisms can dynamically change, impacting link performance at different time scales. We investigate subcarrier phase-shift keying (PSK) and quadrature amplitude modulation (QAM) intensity modulation that can be adapted to dynamically changing link conditions to optimize bandwidth utilization. Using custom subcarrier intensity modulation (SIM) modems, the performance of binary PSK (BPSK), QPSK, 8PSK, 16APSK, and 16-QAM waveforms is reported. The impact of adaptive equalization is also characterized, and the initial performance of a subcarrier multiplexed system is presented. This work represents the first experimental evaluation of SIM waveforms using a laboratory scintillation playback system based on scintillation recorded over real-world propagation paths.

FAST: Fourier domain adaptive optics simulation tool for bidirectional ground-space optical links through atmospheric turbulence.

Free space optical links between the ground and space may be severely degraded by atmospheric turbulence. Adaptive Optics, a technique allowing partial correction of this degradation, is beginning to see use in the field with the potential to achieve more robust and higher bandwidth links. Here we present a simulation tool, FAST, which utilises an analytical Fourier domain Adaptive Optics model developed for astronomy. Using the reciprocity principle, the simulation may be applied either to downlink post-compensated or uplink pre-compensated beams. We show that FAST gives similar results to full end-to-end simulations with wave-optical propagation whilst being between 10 and 200 times faster, enabling the characterisation of optical links with complex Adaptive Optics systems in timely fashion.

Imitation Learning Based Heavy-Hitter Scheduling Scheme in Software-Defined Industrial Networks

To realize flexible networking and on-demand topology reconstructing, Software-Defined Industrial Networks(SDIN) are increasingly embracing the flat structure. Similar to SDN, SDIN suffers from low traffic scheduling efficiency caused by large and imbalanced flows, known as the heavy hitters problem. Due to such heavy hitters, industrial networks may fail to satisfy application's QoS requirements, which results in more severe damages. To improve flow scheduling efficiency under heavy hitters, this article introduces a novel Imitation Learning-based Flow Scheduling (ILFS) method. ILFS utilizes P4-based In-band Network Telemetry(INT) technology to collect fine-grained, real-time traffic data from SDIN's data plane. In the control plane, it integrates the Generative Adversarial Imitation Learning (GAIL) model with a Soft Actor Critic (SAC) to preserve the experiences of flow, thereby better scheduling large flows. Our experiments thoroughly compare ILFS's performance with several state-of-the-art traffic scheduling strategies. The results indicate that ILFS successfully controls the link bandwidth utilization between 10% and 80% and significantly improves the average network throughput and link utilization rate.