High Link Utilization Research Articles

Hierarchical Cross Traffic Scheduling Based on Time-Aware Shapers for Mobile Time-Sensitive Fronthaul Network

To solve the problem of jitter and low network throughput caused by the impact of background flows on IQ traffic in mobile fronthaul network, this paper proposed a new scheduling model for background flows, named hierarchical crossover traffic scheduling mechanism based on time-aware shaper (HC-TAS) by improving the traditional counterpart. Then, in this new model, we designed an inbound scheduling algorithm based on frame length matching and an outbound scheduling algorithm based on queue status, making sure that smaller data frames will not be blocked by large data frames. This greatly improves the utilization of timeslots in the scheduling process and reduces the jitter impact of background flows. To verify its performance, we conducted experiments in a simulated fronthaul network conforming to IEEE 802.1CM. The experimental results show that, under the condition that the jitter is guaranteed to be zero, compared with two mainstream scheduling schemes, Comb-FITting and TAS + Preemption, our proposed scheme can achieve lower maximum end-to-end delay and higher link utilization. The proposed HC-TAS meets the requirements of low jitter and high bandwidth utilization in 5G fronthaul network, and the research results provide a technical basis for the application and development of general time-sensitive networks as well.

PDLB: Path Diversity-aware Load Balancing with adaptive granularity in data center networks

Modern data center topologies often take the form of a multi-rooted tree with rich parallel paths to provide high bandwidth. However, various path diversities caused by traffic dynamics, link failures and heterogeneous switching equipment widely exist in the production datacenter network. Therefore, the multi-path load balancer in data center should be robust to these diversities. Although prior fine-grained schemes such as RPS and Presto make the best use of available paths, However, they are prone to experiencing packet reordering problem under the asymmetric topology. The coarse-grained solutions such as ECMP and LetFlow effectively avoid packet reordering, but easily lead to under-utilization of multiple paths. To cope with these inefficiencies, we propose a load balancing mechanism called PDLB, which adaptively adjusts flowcell granularity according to path diversity. PDLB increases flowcell granularity to alleviate packet reordering under large degrees of topology asymmetry, while reducing flowcell granularity to obtain high link utilization under small degrees of topology asymmetry. PDLB is only deployed on the sender without any modification on switch. We evaluate PDLB through large-scale NS2 simulations. The experimental results show that PDLB reduces the average flow completion time by up to ∼\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sim$$\\end{document}8-53% over the state-of-the-art load balancing schemes.

A Receiver-Driven Transport Protocol With High Link Utilization Using Anti-ECN Marking in Data Center Networks

Existing reactive or proactive congestion control protocols are hard to simultaneously achieve ultra-low latency and high link utilization across all workloads ranging from delay-sensitive flows to bandwidth-hungry ones in datacenter networks. We present an Anti-ECN (Explicit Congestion Notification) Marking Receiver-driven Transport protocol called AMRT, which achieves both near-zero queueing delay and full link utilization by reasonably increasing sending rate in the case of under-utilization. Specifically, switches mark the ECN bit of data packets once detecting spare bandwidth. When receiving the anti-ECN marked packet, the receiver generates the corresponding marked grant to trigger more data packets. The testbed and simulation experiments show that AMRT effectively reduces the average flow completion time (AFCT) by up to 42% and improves the link utilization by up to 38% over the state-of-the-art receiver-driven transmission schemes.

Asymmetry-Aware Load Balancing With Adaptive Switching Granularity in Data Center

Datacenter networks provide large bisection bandwidth by load balancing traffic over rich parallel paths in multi-rooted tree topologies. Nevertheless, production datacenters operate under various path diversities caused by traffic dynamics, hardware failures and heterogeneous switching equipment. Therefore, the load balancing schemes in data center should be resilient to network asymmetry. Prior fine-grained schemes such as RPS and Presto are prone to experience packet reordering problem under asymmetric topology since they split flows into small units which are spread across all parallel paths. The coarse-grained solutions such as ECMP and LetFlow effectively avoid packet reordering, but easily leading to under-utilization of multiple paths. To solve these problems, we propose a load balancing mechanism called AG, which adaptively adjusts switching granularity according to the asymmetric degree of multiple paths. AG increases switching granularity to alleviate packet reordering under large degrees of topology asymmetry, while reducing switching granularity to obtain high link utilization under small degrees of topology asymmetry. Moreover, we design a switch-based scheme which measures the difference of one-way delay of multiple paths to obtain accurate state of topology asymmetry with low overhead. AG is a practical switch-based solution without modification at end hosts. The experimental results of NS2 simulations and real implementation show that AG reduces the average and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$99^{th}$</tex-math> </inline-formula> flow completion time by up to 54% and 65% compared with the state-of-the-art load balancing schemes, respectively.

RaceCC: A rapidly converging explicit congestion control for datacenter networks

Congestion control (CC) in datacenter networks has three primary goals: high link utilization, low queuing delay, and rapid convergence to fairness. Most of the host-driven CC schemes perform well in achieving the first two goals, while it is difficult to converge to fairness rapidly. Switch-driven CC schemes can be a compelling alternative to achieve fairness since switches explicitly provide feedback to hosts. However, we found that existing switch-driven CC schemes converge slowly or sometimes could not guarantee low queues.Based on these observations, in this paper, we propose RaceCC, a RApidly Converging Explicit CC to achieve the three primary goals simultaneously. As a switch-driven CC scheme, RaceCC enables flows to reach a fair rate after the first RTT and guarantees high link utilization and low queue length. To converge rapidly, RaceCC adjusts flow rates through an intuitive MIMD method with additive-decrease for short queues and precise update for increase. Meanwhile, RaceCC can be implemented with several simple operations. We theoretically analyze the stability of RaceCC and evaluate its performance through micro-benchmark and large-scale simulations. The results show that RaceCC reduces the overall average and tail flow completion time (FCT) by 20% ∼ 57% and 15% ∼ 63%, compared to DCQCN, TIMELY, HPCC, PowerTCP, RCP and RoCC.

DIT: A Dynamic Bandwidth Isolated Transmission System for Large-Scale Inter-DC Wireless Communication Network

Large companies are increasingly providing their services over interdomain data centers. Such large-scale wireless communication networks carry various applications, including both online time-sensitive services and offline bandwidth-sensitive services. These applications share the same physical links but have different bandwidth requirements at different time, making the coscheduling problem a key challenge in improving the overall network utility. Most existing commercial systems reserve peak value bandwidth for online time-sensitive services to ensure the quality of service and use the leftover bandwidth to transmit offline data in a best-effort manner. Such systems cannot obtain high link utilizations because of the nonstationarity of online traffic. To solve this problem, we present a dynamic isolated transmission (DIT) system in this paper, where a sliding- k algorithm is designed to predict online traffic, and a bottleneck bypass routing scheme is proposed to schedule the mixed traffic in a shared inter-DC wireless communication network. We conduct a series of experiments in an experimental inter-DC wireless communication network to analyze for efficient network operation of the DIT and compare its performance with typical existing fix-bandwidth-based solutions including Microsoft’s SWAN. The results show that DIT outperforms existing solutions, improving the interdomain link utilization by 18% and the intradomain link utilization by 65%.

Reducing tail latency with coding-based packet spraying in edge datacenters

Modern cloud computing applications have stringent low-latency and high-throughput requirements to meet the increasingly diverse demands from customers. In edge datacenters, the popular load balancing scheme based on packet spraying, i.e., random packet spraying (RPS), makes good use of multiple equal-cost paths to ensure high link utilization and efficient transmission. However, RPS performs poorly under the bursty traffic scenario due to packet loss or even timeout. Therefore, to reduce the tail latency caused by retransmission, we design a coding-based random packet spraying named CRPS. Specifically, the source host transmits forward error correction (FEC) encoded packets and dynamically adjusts the data redundancy based on the packet loss rate. Then the switch randomly sprays the encoded packets across all equal-cost multiple paths to implement parallel transmission. In this way, once enough encoded packets from any parallel paths arrive at the destination host, the original packets can be decoded immediately to address the adverse impact of packet loss. The NS-2 simulation results show that CRPS effectively reduces the probability of timeout and significantly improves the tail flow completion time (FCT) by up to 72% compared with the state-of-the-art multipath transmission schemes.

Sustainable power conversion topology based STATCOM for reactive power compensation

The stability of AC voltage is one of the vital drawbacks affecting the power transfer capability. FACTS devices open a new control technology for mitigating this problem to a great extent. But the size, DC link utilization, and stability of the controlling device is still a major concern. The main thrust is to improve the quality of reactive current injected for voltage enhancement in the power system. This paper proposes a new inverter based STATCOM with fewer components and high DC link utilization. This will reduce voltage stresses across the devices. The required DC link voltage is being curtailed by 1.57 times. When compared to multilevel level inverters, less number of switches and one high-frequency switching is required per phase. The simultaneous switching of devices in the same leg eliminates the shoot-through problem. The proposed method improves the quality of injected current for reactive power compensation and load voltage has attained THD (total harmonic distortion) 2%. MATLAB- Simulink platform is used for simulation and verified with experimental results.

SOFI dataset: Symptom-fault relationship for IP-network

This article describes a dataset of network performance and well-known faults. It is a dataset of symptom-fault causal relationships for an IP-based network. This information is periodically gathered from edge nodes of an emulated enterprise network. The dataset contains about 649 h of network monitoring data, 10 of which refer to artificially induced faults. Those faults are impact failures as faulty cables, fiber cuts, high link utilization, line card failure, link down, misconfigurations, protocol issues, and distribution layer going down. Data were methodically structured by an authoring tool. It is an original dataset created on purpose because of no availability of this dataset type. It is of interest to researchers in data mining, fault diagnosis, and overall network management.

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.

Aeolus: A Building Block for Proactive Transport in Datacenter Networks

As datacenter network bandwidth keeps growing, proactive transport becomes attractive, where bandwidth is proactively allocated as ``credits'' to senders who then can send ``scheduled packets'' at a right rate to ensure high link utilization, low latency, and zero packet loss. Consequently, proactive solutions such as ExpressPass, NDP, Homa, etc., have been proposed recently. While promising, a fundamental challenge is that proactive transport requires at least one-RTT for credits to be computed and delivered. In this paper, we show such one-RTT ``pre-credit'' phase could carry a substantial amount of flows at high link-speeds, but none of existing proactive solutions treats it appropriately. We present Aeolus, a solution focusing on ``pre-credit'' packet transmission as a building block for proactive transports. Aeolus contains unconventional design principles such as scheduled-packet-first (SPF) that de-prioritizes the first-RTT packets, instead of prioritizing them as prior work. It further exploits the preserved, deterministic nature of proactive transport as a means to recover lost first-RTT packets efficiently. Aeolus is compatible with all existing proactive solutions and readily implementable with commodity switches. We have integrated Aeolus into ExpressPass, NDP and Homa, and shown, via both implementation and simulations, that the Aeolus-enhanced solutions deliver significant performance or deployability advantages. For example, it improves the average FCT of ExpressPass by 56%, cuts the tail FCT of Homa by 20x, while achieving similar performance as NDP without switch modifications.

Mitigating Packet Reordering for Random Packet Spraying in Data Center Networks

Modern data center networks are usually constructed in multi-rooted tree topologies, which require the highly efficient multi-path load balancing to achieve high link utilization. Recent packet-level load balancer obtains high throughput by spraying packets to all paths, but it easily leads to the packet reordering under network asymmetry. The flow-level or flowlet-level load balancer avoids the packet reordering, while reducing the link utilization due to their inflexibility. To solve these problems, we design a Queueing Delay Aware Packet Spraying (QDAPS), that effectively mitigates the packet reordering for packet-level load balancer. QDAPS selects paths for packets according to the queueing delay of output buffer, and lets the packet arriving earlier be forwarded before the later packets to avoid packet reordering. Moreover, we adopt the “power-of- n-choices” paradigm on QDAPS to alleviate the impact of herd behavior under multiple forwarding engines. We compare QDAPS with ECMP, LetFlow and RPS through NS2 simulation and Mininet implementation. The test results show that QDAPS reduces flow completion time (FCT) by ~30%-50% over the state-of-the-art load balancing mechanism.

Efficient User-Level Multi-Path Utilization in RDMA Networks

RDMA has become one of the most prominent networking technologies in DCNs by providing high bandwidth and ultra-low latency, especially for data-intensive applications. An important challenge with RDMA is to exploit multi-path for high throughput and reliability. Several studies have been proposed to utilize multi-path in RDMA networks, but they commonly require modification of RDMA NICs, which makes it hard to deploy them in practice. In this paper, we propose a user-level multi-path RDMA (UL-MPRDMA) scheme, in which a flow is partitioned into sub-flows, and transferred via multiple connections to make full use of multiple paths in DCNs. UL-MPRDMA quickly responds to sudden network trouble and congestion by performing dynamic sub-flow scheduling, and also effectively avoids the performance degradation problem due to the limited memory of RDMA NICs without the intervention of CPU. We implement UL-MPRDMA on a real test-bed with commercial RDMA NICs, and show that UL-MPRDMA can achieve 30% higher link utilization than an existing RDMA transport technique.

A Two-Tier Collection and Processing Scheme for Fog-Based Mobile Crowdsensing in the Internet of Vehicles

In view of the rapid development of the Internet of Vehicles (IoV) and wireless communication technology, intelligent transportation systems play an important role in improving urban road safety, promoting the behavioral interaction between users and networks, improving the service quality, and controlling the network cost. Based on the universality and real-time nature of the IoV, data collectors can cooperate with users to sense and collect relevant data within the driving range of vehicles. To achieve this goal, this article proposes a two-tier sensing scheme around the optimization of the sensing mechanism and data processing. In the routing layer, we build a weighted graph model based on vehicle fog, and we propose a new routing strategy to maximize each vehicle’s utilization. We consider that the sensing information collected by multiple vehicles could be repetitive, which would lead to many unnecessary communication flows in the network. Therefore, in the data processing layer, we consider resource consumption in the whole fog, assign different tasks to the sensing vehicles, and filter similar information on the relay nodes of the routing paths to reduce the waste of resources in IoV. Finally, we compare and analyze our two-tier scheme with related approaches, and the results show that our scheme has higher link utilization and lower resource consumption for a high-speed mobile network environment in IoV.

An emulation-based evaluation of TCP BBRv2 Alpha for wired broadband

Google published the first release of the Bottleneck Bandwidth and Round-trip Time (BBR) congestion control algorithm in 2016. Since then, BBR has gained a widespread attention due to its ability to operate efficiently in the presence of packet loss and in scenarios where routers are equipped with small buffers. These characteristics were not attainable with traditional loss-based congestion control algorithms such as CUBIC and Reno. BBRv2 is a recent congestion control algorithm proposed as an improvement to its predecessor, BBRv1. Preliminary work suggests that BBRv2 maintains the high throughput and the bounded queueing delay properties of BBRv1. However, the literature has been missing an evaluation of BBRv2 under different network conditions.This paper presents an experimental evaluation of BBRv2 Alpha (v2alpha-2019-07-28) on Mininet, considering alternative active queue management (AQM) algorithms, routers with different buffer sizes, variable packet loss rates and round-trip times (RTTs), and small and large numbers of TCP flows. Emulation results show that BBRv2 tolerates much higher random packet loss rates than loss-based algorithms but slightly lower than BBRv1. The results also confirm that BBRv2 has better coexistence with loss-based algorithms and lower retransmission rates than BBRv1, and that it produces low queuing delay even with large buffers. When a Tail Drop policy is used with large buffers, an unfair bandwidth allocation is observed among BBRv2 and CUBIC flows. Such unfairness can be reduced by using advanced AQM schemes such as FQ-CoDel and CAKE. Regarding fairness among BBRv2 flows, results show that using small buffers produces better fairness, without compromising high throughput and link utilization. This observation applies to BBRv1 flows as well, which suggests that rate-based model-based algorithms work better with small buffers. BBRv2 also enhances the coexistence of flows with different RTTs, mitigating the RTT unfairness problem noted in BBRv1. Lastly, the paper presents the advantages of using TCP pacing with a loss-based algorithm, when the rate is manually configured a priori. Future algorithms could set the pacing rate using explicit feedback generated by modern programmable switches.

Trade-off queuing delay and link utilization for solving bufferbloat

Abstract To solve the bufferbloat problem, active queue management (AQM) has been recommended but existing AQM algorithms suffer from poor adaptability to dynamic traffics. We develop a novel adaptive AQM algorithm named TODU for a better Trade-Off between queuing Delay and link Utilization. TODU aims to maintain a stable queue length against dynamical network traffics by adapting the drop probability to balance the expected link utilization and queuing delay. We compare TODU with benchmark algorithms in Network Simulator-2 and the results demonstrate that TODU achieves a better trade-off between low queuing latency and high link utilization in a dynamical environment.

Scalable Application- and User-aware Resource Allocation in Enterprise Networks Using End-Host Pacing

Providing scalable user- and application-aware resource allocation for heterogeneous applications sharing an enterprise network is still an unresolved problem. The main challenges are as follows: (i) How do we define user- and application-aware shares of resources? (ii) How do we determine an allocation of shares of network resources to applications? (iii) How do we allocate the shares per application in heterogeneous networks at scale? In this article, we propose solutions to the three challenges and introduce a system design for enterprise deployment. Defining the necessary resource shares per application is hard, as the intended use case, the user’s environment, e.g., big or small display, and the user’s preferences influence the resource demand. We tackle the challenge by associating application flows with utility functions from subjective user experience models, selected Key Performance Indicators, and measurements. The specific utility functions then enable a mapping of network resources in terms of throughput and latency budget to a common user-level utility scale. A sensible distribution of the resources is determined by formulating a multi-objective mixed integer linear program to solve the throughput- and delay-aware embedding of each utility function in the network for a max-min fairness criteria. The allocation of resources in traditional networks with policing and scheduling cannot distinguish large numbers of classes and interacts badly with congestion control algorithms. We propose a resource allocation system design for enterprise networks based on Software-Defined Networking principles to achieve delay-constrained routing in the network and application pacing at the end-hosts. The system design is evaluated against best effort networks in a proof-of-concept set-up for scenarios with increasing number of parallel applications competing for the throughput of a constrained link. The competing applications belong to the five application classes web browsing, file download, remote terminal work, video streaming, and Voice-over-IP. The results show that the proposed methodology improves the minimum and total utility, minimizes packet loss and queuing delay at bottlenecks, establishes fairness in terms of utility between applications, and achieves predictable application performance at high link utilization.

A-ECN Minimizing Queue Length for Datacenter Networks

Recently, Explicit Congestion Notification (ECN) has been leveraged by DCTCP and its successive protocols to reduce the latency in datacenters. However, we find that DCTCP may induce the queue underflow and high latency in multi-queue scenarios and even single-queue environments. Our analysis concludes that the problem is caused by improper ECN marking thresholds of DCTCP. In this paper, an Adaptive ECN (A-ECN) marking scheme is proposed to enhance the performance of the ECN marking scheme of DCTCP.A-ECN is designed to minimize queue length as well as keeping high link utilization. More importantly, A-ECN can adaptively adjust ECN marking thresholds in different scenarios to achieve good generality. Therefore, network operators can directly deploy A-ECN in various environments regardless of underlying bandwidth, packet schedulers and the number of concurrent flows. At last, the testbed evaluation results show that A-ECN can minimize queue length and maintain high throughput. The flow completion times (FCT) for short flows could be reduced by up to 40% compared to the ECN marking scheme of DCTCP.

Distributed Bottleneck-Aware Coflow Scheduling in Data Centers

With the booming development of data parallel frameworks, the coflow abstraction has been greatly favored by data center transport designs, for its prominent ability in capturing application-level semantics. To accelerate job completion, coflow completion time (CCT) is a most important metric, and coflow scheduling is the most effective and widely-adopted means of optimizing CCT. However, most existing coflow scheduling mechanisms neglect the ubiquitous in-network bottlenecks and schedule coflows based on non-blocking giant switch hyperthesis. Such a practice is likely to result in undesired link contention inside the fabric, finally impairing CCT performance. To address this problem, we propose the Distributed Bottleneck-Aware coflow scheduling algorithm called DBA, which approximates the minimum remaining time first (MRTF) heuristic on all fabric-wide links. In this way, core link bandwidths are allocated to coflows as expected and the CCT performance will not be violated. As an evolutionary algorithm, DBA enhances the traditional dual decomposition method thus converges to the optimal bandwidth allocation very fast. Extensive simulations verify DBA's outstanding CCT performance as well as high link utilization. Furthermore, DBA introduces very little overhead and is robust to routing strategies, parameter variations and computation delays.

Adia: Achieving High Link Utilization with Coflow-Aware Scheduling in Data Center Networks

Link utilization has received extensive attention since data centers become the most pervasive platform for data-parallel applications. A specific job of such applications involves communication among multiple machines. The recently proposed coflow abstraction depicts such communication through a group of parallel flows, and captures application performance through corresponding communication requirements. Existing techniques to improve link utilization, however, either restrict themselves to achieving work conservation, or merely focus on flow-level metrics and ignore coflow-level performance. In this paper, we address the coflow-aware scheduling problem with the objective of maximizing link utilization. Through theoretic analyses, we formulate the coflow-aware scheduling problem as a NP-hard open shop scheduling problem with heterogeneous concurrency. We design Adia, a hierarchical scheduling framework to conduct both inter- and intra- link scheduling. The design of Adia leverages priority-based scheduling while guarantees work-conserving and starvation-free bandwidth allocation at the same time. We also prove Adia's algorithm is two-approximate in terms of link utilization. Extensive simulation results on ns3 further show that Adia outperforms both per-flow mechanisms coflow schemes in terms of link utilization, and achieves similar coflow performance in comparison with the state-of-art coflow scheduling schemes.