Fair Bandwidth Allocation Research Articles

A distributed end-to-end fair bandwidth allocation algorithm for multi-path networks

Multi-path transmission significantly improves network performance, yet it complicates the problem of fair resource allocation. While traditional fair bandwidth allocation schemes thrive in single-path settings, they often falter when applied to multi-path environments, highlighting the challenge of achieving fair bandwidth sharing in such networks. To tackle this issue, the concept of "max-min similarity" of queuing delays has been introduced based on insight into the intrinsic interactions between queuing packets, delays, and bandwidth allocation, which leads to a formal definition of max-min fair bandwidth allocation for multi-path environments. Theoretical analysis shows that the max-min similarity of queuing delays is a sufficient condition for max-min fair bandwidth allocation in single bottleneck environments. A novel distributed end-to-end fair bandwidth allocation algorithm, named DMFBA, is then proposed, which separates the control into flow-level and transmission path-level. In achieving max-min similarity in queuing delays by dynamically adjusting the distribution of flows’ queuing packet quotas across paths it achieves the goal of max-min fair bandwidth allocation. Two sets of numerical simulation experiments were conducted and the results show that DMFBA has less overhead and faster convergence than the traditional utility fair algorithms.

Enforcing Fairness in the Traffic Policer Among Heterogeneous Congestion Control Algorithms

Traffic policing is widely used by ISPs to limit their customers’ traffic rates. It has long been believed that a well-tuned traffic policer offers a satisfactory performance for TCP. However, we find this belief breaks with the emergence of new congestion control (CC) algorithms: flows using new CC algorithms can easily occupy the majority of bandwidth, starving traditional TCP flows. We confirm this problem with experiments and reveal its root cause as follows. Without a buffer in traffic policers, congestion only causes packet losses, while new CC algorithms are loss-resilient. When being policed, they will not reduce the sending rate until an unacceptable loss ratio for TCP is reached, resulting in low throughput for competing TCP flows. Simply adding a buffer to the traffic policer improves fairness but incurs high latency. To this end, we propose FairPolicer, which can achieve fair bandwidth allocation without sacrificing latency. FairPolicer regards a token as a basic unit of bandwidth and fairly allocates tokens to active flows in a round-robin manner. To avoid bandwidth waste when flows come and go, FairPolicer puts all available tokens in a global bucket and maintains the amount of residual bucket space rather than the number of available tokens. To scale to massive concurrent flows, FairPolicer uses a Count-Min Sketch structure to maintain per-flow data with a small memory footprint. Testbed experiments show that FairPolicer can allocate bandwidth in a max-min fair manner and achieve much lower latency than other kinds of rate limiters.

SDN-Based Congestion Control and Bandwidth Allocation Scheme in 5G Networks

5G cellular networks are already more than six times faster than 4G networks, and their packet loss rate, especially in the Internet of Vehicles (IoV), can reach 0.5% in many cases, such as when there is high-speed movement or obstacles nearby. In such high bandwidth and high packet loss network environments, traditional congestion control algorithms, such as CUBIC and bottleneck bandwidth and round-trip propagation time (BBR), have been unable to balance flow fairness and high performance, and their flow rate often takes a long time to converge. We propose a congestion control algorithm based on bottleneck routing feedback using an in-network control mode called bottleneck routing feedback (BRF). We use SDN technology (OpenFlow protocol) to collect network bandwidth information, and BRF controls the data transmission rate of the sender. By adding the bandwidth information of the bottleneck in the option field in the ACK packet, considering the flow fairness and the flow convergence rate, a bandwidth allocation scheme compatible with multiple congestion control algorithms is proposed to ensure the fairness of all flows and make them converge faster. The performance of BRF is evaluated via Mininet. The experimental results show that BRF provides higher bandwidth utilization, faster convergence rate, and fairer bandwidth allocation than existing congestion control algorithms in 5G cellular networks.

Inversion impact of approximate PIFO to Start-Time Fair Queueing

Programmable packet schedulers have emerged in the context of Software-Defined Networking (SDN) and diverse service requirements in today’s networks. With a programmable packet scheduler, network operators can apply different scheduling algorithms on the switches. To support a large variety of packet scheduling algorithms, the use of a packet scheduler like PIFO (Push-In First-Out) has been adopted, which serves packets based on their associated priority ranks. Recently, for the concern of priority rank scalability and implementation complexity, a new class of packet schedulers that closely approximate PIFO have been proposed. However, the approximate PIFO schemes may lead to a sub-optimal packet scheduling order and introduce packet inversions.In this paper, we evaluate the inversion impact on Start-Time Fair Queueing (STFQ), an efficient approximation of Weighted Fair Queueing to allocate bandwidth fairly and achieve low maximum delay, when it is used in the approximate PIFO schemes. Our analysis validates the deterioration on the performance goals of STFQ attributed to packet inversions and provides a theoretical bound for the discrepancy caused by these inversions in the fair and delay guarantees offered by STFQ. Based on our simulation results, we demonstrate that the packet inversions in approximate PIFO schemes impair the fairness of bandwidth allocation and the worst-case throughput difference in our simulation exhibits a bias of up to 34% of the total available bandwidth. We further show that the packet inversions result in a 3-fold increase in the maximum end-to-end packet delay in approximate PIFO schemes such as SP-PIFO and AFQ.

Fairness resource allocation based on blockchain for secure communication in integrated IoT

The emerging Space-Air-Ground, Artificial intelligence, blockchain and Vehicle-to-everything technology in Integrated Internet of Things (IoT) enables vehicles to communicate with other vehicles and roadside units (RSU), which improves communication efficiency and driving safety. Due to the rich data in the Integrated IoT, it is easy for illegal persons to steal data or deceive users. While reasonably allocating resources to complete the transmission task, it is still a challenge to ensure the communication security of vehicle users in Integrated IoT. In this paper, we study the cost of vehicle users transmitting tasks by Vehicle to Infrastructure and Vehicle to Vehicle (V2V) in Integrated IoT. In order to protect the information security, we establish the identity authentication and matching model to obtain a better channel environment of Integrated IoT. Moreover, we consider dynamic pricing based on bandwidth resource occupancy to ensure user experience and server load in Integrated IoT. Under the constraints of task tolerable delay, we fix the bandwidth on the V2V side and decouple the task decomposition and bandwidth allocation in Integrated IoT. Then, we propose algorithms Blockchain-based Vehicle Identity Authentication (BVIA) and Delay Weight Fairness Bandwidth Allocation (DWFBA). In this way, vehicle users and RSUs entering the Integrated IoT system are authenticated and matched with devices with good trust value, and the fairness of users resource allocation is guaranteed. Simulation results show that BVIA algorithm can reduce communication overhead, and DWFBA algorithm can control user costs and effectively reduce the number of task failures in Integrated IoT.

Quality of Service Aware Dynamic Bandwidth Allocation for Rate Control in WSN

Different types of data can be generated by Wireless Sensor Networks (WSNs) in both Real-Time (RT) and Non-RT (NRT) scenarios. The combination of these factors, along with the limited bandwidth available, necessitates careful management of these categories in order to reduce congestion. Due to this, a Proficient Rate Control and Fair Bandwidth Allocation (PRC-FBA) method has been created that prioritizes certain types of traffic and creates a virtual queue for them.In PRC-FBA, the Signal-to-Noise and Interference Ratio (SINR) model is applied to the problem of bandwidth allocation in WSN in an effort to find a compromise between equity and performance. Then, a brand-new bandwidth utility factor is defined with regard to equity and effectivenes. The FBA method in PRC-FBA is devoped for only improving throughput, but not considering delay. However, delay is the main factors for trasnmiitng NRT packets. This paper offers a PRC with Quality of Service (QoS) aware Dynamic Bandwidth Allocation (PRC-QDBA) approach for allocating bandwidth while prioritizing packets based on their traffic classes. This model employs a QoS associated dynamic bandwidth allocation strategy which efficiently distributes the unused time slots among the required nodes. The distribution technique is performed based on hierarchical manner utilizing a parent-child association of tree topology. The parent node receives traffic indication maps (TIMs) from the children nodes and adopts them to allocate time slots based on their demamds. If the parent node is unable to allocate the required slots, it creates a TIM that indicating the demands and transfer it to its immediate parent node. This increases the entire performance rate of RT traffic. Furthermore, this model assures the packet forwarding for previously accepted flows by allowing node transmission based on ancestral connection capabilities. Finally, simulation results demonstartes that the suggested model significantly increases the throughput and delay for bandwidth allocation while also enabling QoS support for RT traffic in WSNs.

Traffic-based adaptive bandwidth adjustment for flexible OTN connectivity in optical networks.

In conventional optical transport networks, the service form is the fixed bandwidth connectivity, which is not flexible for carrying bursting traffic. To support the time-varying traffic in an efficient way, researchers are studying the optical service units for building the more flexible optical transport network (OTN) connectivity, which is capable of dynamic hitless bandwidth adjustment. To better utilize the benefits of flexible connectivity, network operators need efficient algorithms to adjust the flexible connectivity bandwidth, especially in the network with a massive number of connections. In this paper, based on max-min fair bandwidth allocation criteria, we propose two traffic-based adaptive bandwidth adjustment algorithms to make bandwidth adjustment decisions, with the aim to improve bandwidth effectiveness. Simulation results show that the proposed algorithms can improve bandwidth utilization by up to 16%. Additionally, under high load conditions, it can reduce the loss in traffic bitrate by a maximum of 10%.

Achieving Per-Flow Fairness and High Utilization With Limited Priority Queues in Data Center

Modern data centers often host multiple applications with diverse network demands. To provide fair bandwidth allocation to several thousand traversing flows, Approximate Fair Queueing (AFQ) utilizes multiple priority queues in switch to approximate ideal fair queueing. However, due to limited number of queues in programmable switches, AFQ easily experiences high packet loss and low link utilization. In this paper, we propose Elastic Fair Queueing (EFQ), which leverages limited priority queues to flexibly achieve both high network utilization and fair bandwidth allocation. EFQ dynamically assigns the free buffer space in priority queues for each packet to obtain high utilization without sacrificing flow-level fairness. The results of simulation experiments and real implementations show that EFQ reduces the average flow completion time by up to 82% over the state-of-the-art fair bandwidth allocation mechanisms.

Analysis of Algorithms to Control the Congestion by Improve Energy Efficiency in WSN

Congestion is a major significant challenge in WSNs because it directly impacts energy efficiency and the network lifetime of sensor nodes in the network. This paper aims to analyze the different congestion control or avoidance routing protocols performances in WSNs with their drawbacks for identifying the upcoming scope of congestion-aware routing protocols in WSNs. This article proposed an adaptive queuing system with the WPDDRC called a proficient control (PRC) algorithm to tackle this issue. In this algorithm, two independent virtual queues are considered single physical length (lines), which accumulate the input packets from every child's node depending on the source's traffic significance and priority. A Proficient Rate Control (PRC) technique develops using traffic type priority and virtual queue conditions. Here, a PRC with fair bandwidth allocation (PRC-FBA) technique is compared and analyzed. It must handle the congestion due to the mix of RT and Non-RT (NRT) packets effectively in network.

PANDA: Preference-Based Bandwidth Allocation in Fog-Enabled Internet of Underground-Mine Things

Traditionally, sensor networks are surmised to enhance safety in underground mines by providing consistent monitoring of the conditions. However, to cope with the frequent condition changes of such a hostile environment, we need a specific network architecture for the deployed nodes to enable dynamic decision making. In this article, we argue that the fog computing paradigm can be leveraged to ensure safety in the Internet of Underground-Mine Things. We emphasize on prioritizing and speeding up the processing of sensitive data by introducing a factor named “criticality index” and ensuring the fairness of bandwidth allocation using this index to comply with the resource requirement of critical data. We formulate the interaction between the fog nodes using the notions of the Stackelberg game. Simulation-based results show that using the proposed scheme, the fog nodes are able to allocate bandwidth with less average error and offset bandwidth, compared to the benchmark schemes.

Ensuring the QoE-Related Fairness to Reduce the User Abandonment Ratio.

Nowadays, it is quite a challenge for app owners to keep users engaged with an app. Currently, the level of user abandonment is one of the key parameters that application owners are interested in. To meet these challenges, we conduct an extended study of a previously proposed solution that significantly reduces the abandonment rate of a given application. The investigated solution is based on the methods of fairness using the QoE and QoS approach. This paper shows that application abandonment ratios can be reduced by using an appropriate approach to fair bandwidth allocation. Adjusting the bandwidth allocation to users, taking into account the quality of the user experience, has a more effective effect on reducing app abandonment ratios than if quality of service is taken into account. This is because the users make the decision to abandon the application based on their feelings rather than technical parameters. In order to effectively reduce application abandonment ratios, a suitable bandwidth allocation algorithm must be used. This paper presents the impact of using different algorithms on the abandonment ratio and compares the popularly used algorithms and the previously proposed bandwidth allocation algorithm.

Making slotted ALOHA efficient and fair using reinforcement learning

Reinforcement learning (RL) has been proposed as a technique that allows nodes to learn to coordinate their transmissions in order to attain much higher channel utilization. Several RL-based approaches have been proposed to improve the performance of slotted ALOHA; however, all these schemes have assumed that immediate feedback is available at the transmitters regarding the outcome of their transmissions. This paper introduces ALOHA-dQT, which is the first channel-access protocol based on the use of RL in the context of slotted ALOHA that takes into account the use of explicit acknowledgments from receivers to senders. As such, ALOHA-dQT is the first RL-based approach for channel access that is suitable for wireless networks that do not rely on centralized repeaters or base stations. ALOHA-dQT achieves high utilization by having nodes broadcast short summaries of the channel history as known to them along with their packets. Simulation results show that ALOHA-dQT leads to network utilization above 75%, with fair bandwidth allocation among nodes.

Distributed Traffic Engineering for Multi-Domain SDN Without Trust

In software defined networking, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">flat</i> design of distributed control plane enables the management of multi-domain networks that are incapable of deploying a root controller. However, it is very difficult to avoid policy conflicts between independent local controllers due to the lack of centralized arbitration. Moreover, domains without trust may not be always cooperative and could even cheat to maximize their own interests. In this article, we first consider the cooperative scenario and address the problem of traffic engineering in a flat distributed control plane. We propose a fully distributed algorithm, called <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DisTE</monospace> , which can provide max-min fair bandwidth allocation for flows and maximize resource utilization. <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DisTE</monospace> also preserves the local topology of each domain and achieves policy consistency by multiple rounds of synchronization. We then consider the non-cooperative scenario, where selfish domains may discriminate bandwidth requests from other domains or overstate theirs owns to squeeze more bandwidths.

Fair and near-optimal coflow scheduling without prior knowledge of coflow size

Achieving the minimum average coflow completion time(CCT) and the isolation guarantees for multi-tenant, is considered a challenge in a cloud environment. This is because the minimum average CCT and isolation guarantees are two conflicting targets, and they cannot be achieved simultaneously. Prior solutions have implemented a single target either minimizing the average CCT or isolation guarantees. The prior solutions are also limited to clairvoyant scheduling. They also assume the availability of the complete knowledge of coflow sizes before the communication starts. In this paper, we propose an efficient scheduling algorithm smallest-height-first DRF(SHFDRF) for near-optimal scheduling and isolation guarantees without prior knowledge of coflow size. SHFDRF achieves the long-term isolation guarantees and the minimum average CCT by the smallest height first and the monopolistic dominant resource fairness bandwidth allocation strategy. The smallest height first and the monopolistic dominant resource fairness bandwidth allocation strategy can also improve link utilization and system throughput. The trace-driven simulation shows that SHFDRF enables communication stages to 1.28 $$\times $$ , 2.27 $$\times $$ , and 6.28 $$\times $$ faster on the 95th percentile compared to DRF, NCDRF, and Per-Flow Fairness. Even compared with minimum CCT, the completion time of coflow only slowed down by 13.9% on the 95th percentile. Overall, the performance of SHFDRF is acceptable, and it can be applied to the actual datacenter without the limitation of complete prior knowledge.

Adaptive Queue Management and Traffic Class Priority Based Fairness Rate Control in Wireless Sensor Networks

Congestion control in Wireless Sensor Networks (WSNs) is one of the key areas of research and different algorithms have been proposed using either of the notions of fair rate allocation, traffic class priority, and queue management. Use of the any one of the above is not adequate to address the challenges. Hence, in this paper, we have proposed a novel congestion control algorithm using the combined notions of fair allocation of bandwidth, prioritizing traffic classes, and Adaptive Queue Management (ADQM). The proposed Weighted Priority based Fair Queue Gradient Rate Control (WPFQGRC) scheme achieves the fair distribution of spare bandwidth by considering the traffic class priority, average queue size, and the connected loads of a node. Average queue size at every node is adapted based on the proposed notion of the gradient of the differential of Global Priority (GP) with respect to the differential of queue size. The output rate of a given node is computed based on the GP of the node and the average queue size. The spare bandwidth of a node is fairly distributed by taking into account the connected load of the given node. The proposed algorithm is developed to suit to a general topology of WSN, however for the sake of illustration, we have considered a tree topology network that deals with both Real-Time (RT) and Non-Real Time (NRT) traffic classes. The proposed algorithm is implemented in NS3 platform in Linux environment and the performance of the algorithm is evaluated in terms of throughput, packet loss, packet delay, traffic class patterns, node mobility, and the average queue size. The performance of the proposed algorithm is found to be superior to that of Yaghmaee et al. ’s, Brahma et al. ’s, Monowar et al. ’s, Sarode et al. ’s, Difference of Differentials Rate Control (DDRC), Weighted Priority based DDRC (WPDDRC), and Priority based Fairness Rate Control (PFRC) algorithms respectively.

A Case for Pricing Bandwidth: Sharing Datacenter Networks With Cost Dominant Fairness

Unlike other resources such as CPU or memory in a virtual machine, inter-virtual-machine (inter-VM) bandwidth has not been explicitly priced in datacenter networks. In this article, we argue that tenants of an IaaS cloud computing platform should be given the flexibility to pay more for explicitly priced datacenter bandwidth beyond traditional virtual machines, in order to achieve better (or more predictable) application performance. We show that a much simpler design principle can be followed to allocate bandwidth fairly, and desirable properties related to fairness can be more easily achieved, compared with state-of-the-art proposals. We call such a design principle cost dominant fairness, which stipulates that bandwidth should be allocated based on the total cost that a tenant incurs for running its applications in the cloud. Guided by the principle of cost dominant fairness, we explore the design space of pricing inter-VM bandwidth, as well as achieving fair bandwidth sharing among multiple tenants. Through our study, we believe that it is best to assign per-VM-pair weights based on individualized prices. We present a distributed bandwidth allocation algorithm that is theoretically supported by a network utility maximization formulation, and practically implemented as a shim layer at each virtual machine. We are also concerned with practical issues of billing, where discounts are needed to ensure that a tenant only pays for the bandwidth share that it is allocated. Finally, we have evaluated our pricing framework and per-VM-pair weighted fair bandwidth allocation in the Mininet emulation testbed and simulations.

High Throughput Data Relay in UAV Wireless Networks

As a result of their high mobility and reduced cost, Unmanned Aerial Vehicles (UAVs) have been found to be a promising tool in wireless networks. A UAV can perform the role of a base station as well as a mobile relay, connecting distant ground terminals. In this paper, we dispatch a UAV to a disaster area to help relay information for victims. We involve a bandwidth efficient technique called the Dual-Sampling (DS) method when planning the UAV flight trajectory, trying to maximize the data transmission throughput. We propose an iterative algorithm for solving this problem. The victim bandwidth scheduling and the UAV trajectory are alternately optimized in each iteration, meanwhile a power balance mechanism is implemented in the algorithm to ensure the proper functioning of the DS method. We compare the results of the DS-enabled scheme with two non-DS schemes, namely a fair bandwidth allocation scheme and a bandwidth contention scheme. The DS scheme outperforms the other two non-DS schemes regarding max-min average data rate among all the ground victims. Furthermore, we derive the theoretical optimal performance of the DS scheme for a given scenario, and find that the proposed approach can be regarded as a general method to solve this optimization problem. We also observe that the optimal UAV trajectory for the DS scheme is quite different from that of the non-DS bandwidth contention scheme.

Blockage Aware Fair Scheduling with Differentiated Service Support in mmWave WPANs/WLANs

Millimeter Wave (mmWave) communications are evolving as a potential and promising technology to address the ever increasing mobile data rate requirements. This paper addresses fair scheduling in directional antenna based mmWave wireless personal and local area networks with non-uniform traffic demand. Due to their small wavelength, mmWave signals are susceptible to blockage. The proposed fair schedulers handle the link blockage problem through relaying, and are capable of providing end-to-end fair bandwidth allocation to relay flows. To achieve differentiated and fair service allocation to various regions of the network while using only limited flow related information, a service tag based scheduler is proposed in this paper. Then, the performance bounds on the minimum throughput and unfairness of this scheduler are obtained. To approximate the performance of the service tag based scheduler while minimizing the control overhead, a heuristic fair scheduler is also proposed. Results from extensive simulations conducted in a mmWave WPAN deployed to support high data rate applications show the performance advantages of the proposed schedulers, compared to existing fair schedulers, in terms of throughput and fairness.

Statistical Learning Based Congestion Control for Real-Time Video Communication

With the increasing demands on interactive video applications, how to adapt video bit rate to avoid network congestion has become critical, since congestion results in self-inflicted delay and packet loss which deteriorate the quality of real-time video service. The existing congestion control is hard to simultaneously achieve low latency, high throughput, good adaptability and fair bandwidth allocation, mainly because of the hardwired control strategy and egocentric convergence objective. To address these issues, we propose an end-to-end statistical learning based congestion control, named Iris. By exploring the underlying principles of self-inflicted delay, we reveal that congestion delay is determined by sending rate, receiving rate and network status, which inspires us to control video bit rate using a statistical-learning congestion control model. The key idea of Iris is to force all flows to converge to the same queue load, and adjust the bit rate by the model. All flows keep a small and fixed number of packets queuing in the network, thus the fair bandwidth allocation and low latency are both achieved. Besides, the adjustment step size of sending rate is updated by online learning, to better adapt to dynamically changing networks. We carried out extensive experiments to evaluate the performance of Iris, with the implementations of transport layer (UDP) and application layer (QUIC) respectively. The testing environment includes emulated network, real-world Internet and commercial LTE networks. Compared against TCP flavors and state-of-the-art protocols, Iris is able to achieve high bandwidth utilization, low latency and good fairness concurrently. Especially over QUIC, Iris is able to increase the video bitrate up to 25%, and PSNR up to 1dB.

Stability of a Subcritical Fluid Model for Fair Bandwidth Sharing with General File Size Distributions

This work concerns the asymptotic behavior of solutions to a (strictly) subcritical fluid model for a data communication network, where file sizes are generally distributed and the network operates under a fair bandwidth-sharing policy. Here we consider fair bandwidth-sharing policies that are a slight generalization of the [Formula: see text]-fair policies introduced by Mo and Walrand [Mo J, Walrand J (2000) Fair end-to-end window-based congestion control. IEEE/ACM Trans. Networks 8(5):556–567.]. Since the year 2000, it has been a standing problem to prove stability of the data communications network model of Massoulié and Roberts [Massoulié L, Roberts J (2000) Bandwidth sharing and admission control for elastic traffic. Telecommunication Systems 15(1):185–201.], with general file sizes and operating under fair bandwidth sharing policies, when the offered load is less than capacity (subcritical conditions). A crucial step in an approach to this problem is to prove stability of subcritical fluid model solutions. In 2012, Paganini et al. [Paganini F, Tang A, Ferragut A, Andrew LLH (2012) Network stability under alpha fair bandwidth allocation with general file size distribution. IEEE Trans. Automatic Control 57(3):579–591.] introduced a Lyapunov function for this purpose and gave an argument, assuming that fluid model solutions are sufficiently smooth in time and space that they are strong solutions of a partial differential equation and assuming that no fluid level on any route touches zero before all route levels reach zero. The aim of the current paper is to prove stability of the subcritical fluid model without these strong assumptions. Starting with a slight generalization of the Lyapunov function proposed by Paganini et al., assuming that each component of the initial state of a measure-valued fluid model solution, as well as the file size distributions, have no atoms and have finite first moments, we prove absolute continuity in time of the composition of the Lyapunov function with any subcritical fluid model solution and describe the associated density. We use this to prove that the Lyapunov function composed with such a subcritical fluid model solution converges to zero as time goes to infinity. This implies that each component of the measure-valued fluid model solution converges vaguely on [Formula: see text] to the zero measure as time goes to infinity. Under the further assumption that the file size distributions have finite pth moments for some p &gt; 1 and that each component of the initial state of the fluid model solution has finite pth moment, it is proved that the fluid model solution reaches the measure with all components equal to the zero measure in finite time and that the time to reach this zero state has a uniform bound for all fluid model solutions having a uniform bound on the initial total mass and the pth moment of each component of the initial state. In contrast to the analysis of Paganini et al., we do not need their strong smoothness assumptions on fluid model solutions and we rigorously treat the realistic, but singular situation, where the fluid level on some routes becomes zero, whereas other route levels remain positive.