Data center networks are characterized by high link speeds, low propagation delays, small switch buffers, and temporally clustered arrivals of many concurrent TCP flows fulfilling data transfer requests. However, the combination of these features can lead to transient buffer overflow and bursty packet losses, which in turn lead to TCP retransmission timeouts that degrade the performance of short-lived flows. This so-called TCP-incast problem can cause TCP throughput collapse. In this paper, we explore an application-level approach for solving this problem. The key idea of our solution is to coordinate the scheduling of short-lived TCP flows so that no data loss happens. We develop a mathematical model of lossless data transmission, and estimate the maximum good put achievable in data center networks. The results indicate non-monotonic good put that is highly sensitive to specific parameter configurations in the data center network. We validate our model using ns-2 network simulations, which show good correspondence with the theoretical results.

/pdf/solving-the-tcp-incast-problem-with-application-level-1jyruczq9j.pdf

Solving the TCP-Incast Problem with Application-Level Scheduling

Incast applications have grown in popularity with the advancement of data center technology. It is found that the TCP incast may suffer from the throughput collapse problem, as a consequence of TCP retransmission timeouts when the bottleneck buffer is overwhelmed and causes the packet losses. This is critical to the Quality of Service of cloud computing applications. While some previous literature has proposed solutions, we still see the problem not completely solved. In this paper, we investigate the three root causes for the poor performance of TCP incast flows and propose three solutions, one for each at the beginning, the middle and the end of a TCP connection. The three solutions are: admission control to TCP flows so that the flow population would not exceed the network's capacity; retransmission based on timestamp to detect loss of retransmitted packets; and reiterated FIN packets to keep the TCP connection active until the the termination of a session is acknowledged. The orchestration of these solutions prevents the throughput collapse. The main idea of these solutions is to ensure all the on-going TCP incast flows can maintain the self-clocking, thus eliminates the need to resort to retransmission timeout for recovery. We evaluate these solutions and find them work well in preventing the retransmission timeout of TCP incast flows, hence also preventing the throughput collapse.

Preventing TCP incast throughput collapse at the initiation, continuation, and termination

TCP incast solutions in data center networks: A classification and survey

In Web applications served by datacenter nowadays, the incast congestion at the front-end server seriously degrades the data request latency performance due to the vast data transmissions from a large number data servers for a data request in a short time. Previous incast congestion control methods usually consider the direct data transmissions from data servers to the front-end server, which makes it difficult to control the sending speed or adjust workloads due to the transient transmission of only a few data objects from each data server. In this paper, we propose a Swarm-based Incast Congestion Control (SICC) system. SICC forms all target data servers of one request in the same rack into a swarm. In each swarm, a data server (called hub) is selected to forward all data objects to the front-end server, so that the number of data servers concurrently connected to the front-end server is reduced, which avoids the incast congestion. Also, the continuous data transmission from hubs to the front-end server facilitates the development of other strategies to further control the incast congestion. To fully utilize the bandwidth, SICC uses a two-level data transmission speed control method to adjust the data transmission speeds of hubs. A query redirection method further reduces the request latency by balancing the transmission remaining times between hubs. Our experiments in simulation and on a real cluster demonstrate that SICC outperforms other incast control methods in improving throughput and reducing the data request latency.

https://dl.acm.org/doi/pdf/10.1145/3087556.3087559

Swarm-based Incast Congestion Control in Datacenters Serving Web Applications

Modern data centres host a myriad of cloud services and applications with stringent delay and throughput requirements. The vast majority of these applications are of type partition/aggregate, where many servers simultaneously communicate with one client to produce a result. Unfortunately, the standard TCP/IP protocol, originally proposed for the Internet one-to-one transport, falls short in this environment. This is due to the TCP throughput collapse in such environment, known as TCP Incast congestion problem. This paper revisits the Incast congestion problem and identifies its root cause: severe packet drops that result from the switch buffers overflow. We propose a method of controlling congestion, named ‘Many-To-one’ (M21TCP). The intuition is that a switch can inform all parallel senders of the maximum rate at which they can send packets that will not cause buffers overflow. M21TCP has been tested and evaluated against known previous proposals such as DCTCP, RED and ECN. Results show that M21TCP outperforms previous solutions and completely eliminates Incast for the maximum number of servers.

/pdf/m21tcp-overcoming-tcp-incast-congestion-in-data-centres-2klrngon0t.pdf

Williamson

Papers

An application-level solution for the TCP-incast problem in data center networks