Fair Share Rate Research Articles

Fair Quantized Congestion Notification in Data Center Networks

Quantized Congestion Notification (QCN) has been developed for IEEE 802.1Qau to provide congestion control at the Ethernet Layer or Layer 2 in data center networks (DCNs) by the IEEE Data Center Bridging Task Group. One drawback of QCN is the rate unfairness of different flows when sharing one bottleneck link. In this paper, we propose an enhanced QCN congestion notification algorithm, called fair QCN (FQCN), to improve rate allocation fairness of multiple flows sharing one bottleneck link in DCNs. FQCN identifies congestion culprits through joint queue and per flow monitoring, feedbacks individual congestion information to each culprit through multi-casting, and ensures convergence to statistical fairness. We analyze the stability and fairness of FQCN via Lyapunov functions and evaluate the performance of FQCN through simulations in terms of the queue length stability, link throughput and rate allocations to traffic flows with different traffic dynamics under three network topologies. Simulation results confirm the rate allocation unfairness of QCN, and validate that FQCN maintains the queue length stability, successfully allocates the fair share rate to each traffic source sharing the link capacity, and enhances TCP throughput performance in the TCP Incast setting.

Self-Configured Fair Queueing

Many core-stateless mechanisms have been proposed to resolve the scalability problem in the core routers. However, they place considerable reliance on the edge routers or anterior core routers to mark correct flow information in corresponding packets. As a result, the condition of incorrect flow information may severely degrade the fairness among different flows. To achieve the robust fairness, an approximately core-stateless fair sharing mechanism, self-configured fair queuing (SCFQ) is proposed in this paper. This mechanism is mainly composed of two rate estimators; one estimator is used to estimate the arrival rate of the malicious flows that can be adopted to emend the incorrect flow information of the arrival packets. The other one is used to estimate the fair share rate. Computer Simulation is applied to compare the fairness of the SCFQ, core-stateless fair queuing (CSFQ), linear fair sharing estimation (LFSE), deficit round robin (DRR), and first in first out (FIFO). From the simulation results, the SCFQ is capable of supporting much better fairness than that of other compared mechanisms under a variety of traffic conditions. Also, the overhead of the SCFQ is low, so it is very suitable to be deployed in high-speed core routers.

Effective fair allocation using smart market label auction with CSLF and CR-CSFQ

Core-stateless mechanisms achieve better scalability by reducing the complexity of fair queuing, which usually needs to maintain states, manage buffers, and perform flow scheduling on a per-flow basis. In this paper, we propose two core-stateless fair bandwidth allocation schemes. Both schemes do not need to maintain per-flow state. Packet is labeled using smart market model according to the characteristics of the flow to which the packet belongs. No matter TCP or UDP flows can get their fair share rate by the proposed schemes. The first scheme is called core-stateless labeling fairness (CSLF). In CSLF scheme, packets are labeled only once at the entrance of the network and estimators of number of active flows based on Bloom filter are employed at the core routers. The estimation can be used to provide a fair rate to perform auction. Packets of a flow whose rate exceeds the estimated fair share rate are dropped at a congested router. The second scheme, called congestion-responsive-CSFQ (CR-CSFQ), is one extension from CSFQ. Congestion-responsive flows should get a different treatment from non-responsive flows at each core router. Fairness can be achieved by one extra smart market label in each packet. Through simulations, CSLF and CR-CSFQ are shown to achieve fair allocation effectively.

A Novel Modified Secant Method for Computing the Fair Share Rate

In this paper, we propose a novel modified secant algorithm to compute the flow fair share rate within the framework of the core-stateless fair queueing [1]. The geometric explanation and numerical results demonstrate that the proposed algorithm possesses better performance in terms of accuracy and convergence than that proposed in [1].

Adaptive CSFQ: determining the packet dropping probability adaptively for achieving fair bandwidth allocations in SCORE networks

In stateless core (SCORE) networks, edge routers maintain per-flow state while core routers do not. Core stateless fair queuing (CSFQ) has been proposed for approximating the operation of per-flow queuing techniques in SCORE networks. However, the packet dropping probability offered by CSFQ suits only UDP flows. Thus, CSFQ cannot achieve fair bandwidth allocation for TCP flows. This paper proposes adaptive CSFQ (ACSFQ). The packet dropping probability in ACSFQ is determined adaptively based on the flow arrival rate, the fair share rate, and the current queue length. It well supports TCP flows as well as UDP flows. We compare ACSFQ to CSFQ in terms of fair bandwidth allocation. This paper also proposes how to identify and penalize unresponsive flows without managing their flow state. The method is simple to realize because only the packet dropping probability is changed.