Jianxin Wang

Performance Enhancement of Multipath TCP for Wireless Communications With Multiple Radio Interfaces

Multipath transmission control protocol (MPTCP) allows a TCP connection to operate across multiple paths simultaneously and becomes highly attractive to support the emerging mobile devices with various radio interfaces and to improve resource utilization as well as connection robustness. The existing multipath congestion control algorithms, however, are mainly loss-based and prefer the paths with lower drop rates, leading to severe performance degradation in wireless communication systems, where random packet losses occur frequently. To address this challenge and improve the performance of MPTCP in wireless networks, this paper proposes a new mVeno algorithm, which makes full use of the congestion information of all the subflows belonging to a TCP connection in order to adaptively adjust the transmission rate of each subflow. Specifically, mVeno modifies the additive increase phase of Veno so as to effectively couple all subflows by dynamically varying the congestion window increment based on the receiving ACKs. The weighted parameter of each subflow for tuning the congestion window is determined by distinguishing packet losses caused by random error of wireless links or by network congestion. We implement mVeno in a Linux server and conduct extensive experiments both in test bed and in real WAN to validate its effectiveness. The performance results demonstrate that compared with the existing schemes, mVeno increases the throughput significantly, achieves load balancing, and can keep the fairness with regular TCP.

ARS: Cross-layer adaptive request scheduling to mitigate TCP incast in data center networks

In data center networks, many network-intensive applications typically suffer TCP incast throughput collapse when bursty concurrent TCP flows share a single bottleneck link. To address the TCP incast problem, we first reveal theoretically and empirically that controlling the number of concurrent flows is much more effective in reducing the incast probability than controlling the congestion window. We further propose a novel cross-layer design called Adaptive Request Schedule (ARS), which dynamically adjusts the number of concurrent TCP flows by batching application requests according to the congestion state acquired from transport layer. ARS is deployed only at the aggregator-side, while making no modification on hundreds or thousands of workers. Broad applicability is another advantage of ARS. We integrated ARS transparently (i.e., without modification) with DCTCP and TCP NewReno on NS2 simulation and a physical testbed, respectively. The experimental results show that ARS significantly reduces the incast probability across different TCP protocols and that the network goodput can be increased consistently by on average 6x under severe congestion.

Tuning the Aggressive TCP Behavior for Highly Concurrent HTTP Connections in Data Center

Modern data centers host diverse HTTP-based services, which employ persistent TCP connections to send HTTP requests and responses. However, the ON/OFF pattern of HTTP traffic disturbs the increase of TCP congestion window, potentially triggering packet loss at the beginning of ON period. Furthermore, the transmission performance becomes worse due to severe congestion in the concurrent transfer of HTTP response. In this work, we first reveal that the TCPs aggressive behavior in increasing congestion window causes TCP timeouts and throughput collapse. We further present the design and implementation of TCP-TRIM, which employs probe packets to smooth the aggressive increase of congestion window in persistent TCP connection, and leverages congestion detection and control at end-host to limit the growth of switch queue length under highly concurrent TCP connections. The experimental results of at-scale simulations and real implementations show that TCPTRIM reduces the completion time of HTTP response by up to 80%, while introducing little deployment overhead only at the end hosts.

Tuning the Aggressive TCP Behavior for Highly Concurrent HTTP Connections in Intra-Datacenter

Modern data centers host diverse hyper text transfer protocol (HTTP)-based services, which employ persistent transmission control protocol (TCP) connections to send HTTP requests and responses. However, the ON/OFF pattern of HTTP traffic disturbs the increase of TCP congestion window, potentially triggering packet loss at the beginning of ON period. Furthermore, the transmission performance becomes worse due to severe congestion in the concurrent transfer of HTTP response. In this paper, we provide the first extensive study to investigate the root cause of performance degradation of highly concurrent HTTP connections in data center network. We further present the design and implementation of TCP-TRIM, which employs probe packets to smooth the aggressive increase of congestion window in persistent TCP connection and leverages congestion detection and control at end-host to limit the growth of switch queue length under highly concurrent TCP connections. The experimental results of at-scale simulations and real implementations demonstrate that TCP-TRIM reduces the completion time of HTTP response by up to 80%, while introducing little deployment overhead only at the end hosts.

Reducing transport latency for short flows with multipath TCP

Abstract Multipath TCP (MPTCP) has been an emerging transport protocol that provides network resilience to failures and improves throughput by splitting a data stream into multiple subflows across all the available multiple paths. While MPTCP is generally beneficial for throughput-sensitive large flows with large number of subflows, it may be harmful for latency-sensitive small flows. MPTCP assigns each subflow a congestion window, making short flows susceptible to timeout when a flow only contains a few packets. This condition becomes even worse when the paths have heterogeneous characteristics as packet reordering occurs and the slow paths can be used with MPTCP, causing the increased end-to-end delay and the lower application Goodput. Thus, it is important to choose the appropriate subflows for each MPTCP connection to achieve the good performance. However, the subflows in MPTCP are determined before a connection is established, and they usually remain unchanged during the lifetime of that connection. To address this issue, we propose DMPTCP, which dynamically adjusts the subflows according to application workloads. Specifically, DMPTCP first utilizes the idea of TCP modeling to estimate the latency on the path under scheduling and the data amount sent on the other paths simultaneously, and then decides the set of subflows to be used for certain application periodically with the goal of reducing completion time for short flows and achieving a higher throughput for long flows. We implement DMPTCP in a Linux server and conduct extensive experiments both in NS3 and in Linux testbed to validate its effectiveness. Our evaluation shows that DMPTCP decreases the completion time by over 46.55% compared to conventional MPTCP for short flows while increases the Goodput up to 21.3% for long-lived flows.

Task-aware TCP in Data Center Networks

In modern data centers, many flow-based and task-based schemes have been proposed to speed up the data transmission in order to provide fast, reliable services for millions of users. However, existing flow-based schemes treat all flows in isolation, contributing less to or even hurting user experience due to the stalled flows. Other prevalent task-based approaches, such as centralized and decentralized scheduling, are sophisticated or unable to share task information. In this work, we first reveal that relinquishing bandwidth of leading flows to the stalled ones effectively reduces the task completion time. We further present the design and implementation of a general supporting scheme that shares the flow-tardiness information through a receiver-driven coordination. Our scheme can be flexibly and widely integrated with the state-of-the-art TCP protocols designed for data centers, while making no modification on switches. Through the testbed experiments and simulations of typical data center applications, we show that our scheme reduces the task completion time by 70% and 50% compared with the flow-based protocols (e.g. DCTCP, L2DCT) and task-based scheduling (e.g. Baraat), respectively. Moreover, our scheme also outperforms other approaches by 18% to 25% in prevalent topologies of data center.

Flow-Aware Adaptive Pacing to Mitigate TCP Incast in Data Center Networks

In data center networks, many network-intensive applications leverage large fan-in and many-to-one communication to achieve high performance. However, the special traffic patterns, such as micro-burst and high concurrency, easily cause TCP Incast problem and seriously degrade the application performance. To address the TCP Incast problem, we first reveal theoretically and empirically that alleviating packet burstiness is much more effective in reducing the Incast probability than controlling the congestion window. Inspired by the findings and insights from our experimental observations, we further propose a general supporting scheme Adaptive Pacing (AP), which dynamically adjusts burstiness according to the flow concurrency without any change on switch. Another feature of AP is its broad applicability. We integrate AP transparently into different TCP protocols (i.e., DCTCP, L2DCT and D2TCP). Through a series of large-scale NS2 simulations, we show that AP significantly reduces the Incast probability across different TCP protocols and the network goodput can be increased consistently by on average 7x under severe congestion.

FSQCN: Fast and Simple Quantized Congestion Notification in Data Center Ethernet

Currently, Quantized Congestion Notification (QCN) has been accepted as the standard layer 2 congestion control protocol in Data Center Ethernet. Although the good performance of QCN has been validated in many experiments, we find that QCN has two drawbacks. First, the incomplete binary search in QCN fails to find the proper sending rate, leading to complicate supplement mechanisms. Second, in face of unknown network environment, the rate setting of QCN is inconsistent. Consequently, QCN spends much time on acquiring the spare bandwidth. To address theses problems, we propose the Fast and Simple QCN (FSQCN), following the same framework as QCN. FSQCN complements the binary search and removes other complicate supplement mechanisms in QCN. Thus, FSQCN is much simpler than QCN. Moreover, FSQCN resets the sending rate to the link rate explicitly when the switch detects spare bandwidth. Extensive simulations validate that FSQCN controls the queue length well like QCN and responds faster than QCN.