RepFlow: Minimizing Flow Completion Times with Replicated Flows in Data Centers

Short TCP flows that are critical for many interactive applications in data centers are plagued by large flows and head-of-line blocking in switches. Hash-based load balancing schemes such as ECMP aggravate the matter and result in long-tailed flow completion times (FCT). Previous work on reducing FCT usually requires custom switch hardware and/or protocol changes. We propose RepFlow, a simple yet practically effective approach that replicates each short flow to reduce the completion times, without any change to switches or host kernels. With ECMP the original and replicated flows traverse distinct paths with different congestion levels, thereby reducing the probability of having long queueing delay. We develop a simple analytical model to demonstrate the potential improvement of RepFlow. Extensive NS-3 simulations and Mininet implementation show that RepFlow provides 50%--70% speedup in both mean and 99-th percentile FCT for all loads, and offers near-optimal FCT when used with DCTCP.Comment: To appear in IEEE INFOCOM 201

An INT‐based packet loss monitoring system for data center networks implementing Fine‐Grained Multi‐Path routing

In-band network telemetry (INT) is a newer network measurement technology that uses normal data packets to collect network information hop-by-hop with low overhead. Since incomplete telemetry data seriously degrades the performance of upper-layer network telemetry applications, it is necessary to consider the own INT packet loss. In response, LossSight, a powerful packet loss monitoring system for INT has been designed, implemented, and made available as open-source. This letter extends the previous work by proposing, implementing, and evaluating LB-LossSight, an improved version compatible with packet-level load-balancing techniques, which are currently used in modern Data Center Networks. Experimental results in a Clos network, one of the most commonly used topologies in today's data centers, confirm the high detection and localization accuracy of the implemented solution.Spanish State Research Agency (AEI), under project grant AriSe2: FINe, (Ref.PID2020-116329GB-C22 founded by MCIN/AEI/10.13039/501100011033) and the project (Natural Science Foundation of Shandong Province under Grant No. ZR2020LZH010)

An INT-based packet loss monitoring system for data center networks implementing Fine-Grained Multi-Path routing

In-band network telemetry (INT) is a newer network measurement technology that uses normal data packets to collect network information hop-by-hop with low overhead. Since incomplete telemetry data seriously degrades the performance of upper-layer network telemetry applications, it is necessary to consider the own INT packet loss. In response, LossSight, a powerful packet loss monitoring system for INT has been designed, implemented, and made available as open-source. This letter extends the previous work by proposing, implementing, and evaluating LB-LossSight, an improved version compatible with packet-level load-balancing techniques, which are currently used in modern Data Center Networks. Experimental results in a Clos network, one of the most commonly used topologies in today's data centers, confirm the high detection and localization accuracy of the implemented solution.Spanish State Research Agency (AEI), under project grant AriSe2: FINe, (Ref.PID2020-116329GB-C22 founded by MCIN/AEI/10.13039/501100011033) and the project (Natural Science Foundation of Shandong Province under Grant No. ZR2020LZH010)

ATP: a Datacenter Approximate Transmission Protocol

Many datacenter applications such as machine learning and streaming systems do not need the complete set of data to perform their computation. Current approximate applications in datacenters run on a reliable network layer like TCP. To improve performance, they either let sender select a subset of data and transmit them to the receiver or transmit all the data and let receiver drop some of them. These approaches are network oblivious and unnecessarily transmit more data, affecting both application runtime and network bandwidth usage. On the other hand, running approximate application on a lossy network with UDP cannot guarantee the accuracy of application computation. We propose to run approximate applications on a lossy network and to allow packet loss in a controlled manner. Specifically, we designed a new network protocol called Approximate Transmission Protocol, or ATP, for datacenter approximate applications. ATP opportunistically exploits available network bandwidth as much as possible, while performing a loss-based rate control algorithm to avoid bandwidth waste and re-transmission. It also ensures bandwidth fair sharing across flows and improves accurate applications' performance by leaving more switch buffer space to accurate flows. We evaluated ATP with both simulation and real implementation using two macro-benchmarks and two real applications, Apache Kafka and Flink. Our evaluation results show that ATP reduces application runtime by 13.9% to 74.6% compared to a TCP-based solution that drops packets at sender, and it improves accuracy by up to 94.0% compared to UDP

데이터 센터 내의 다중경로 전송을 위한 동적 부하 균형 기법

학위논문 (석사)-- 서울대학교 대학원 : 공과대학 컴퓨터공학부, 2019. 2. 권태경 .Various applications require the data center networks to carry their traffic efficiently. The data center networks usually have a hierarchical topology and exhibit distinct traffic patterns, which is different from the traditional Internet. These features have driven the data center networks to reduce the flow completion time (FCT) and to achieve high throughput. One of the possible solutions is balancing network loads across multiple paths by leveraging transport mechanisms like Equal-Cost MultiPath (ECMP) routing. ECMP allows flows to exploit multiple paths by hashing the metadata of the flows. However, due to the random nature of hash functions, ECMP often distributes the traffic unevenly, which makes it hard to utilize the links' full capacity. Thus, we propose an adaptive load balancing mechanism for multiple paths in data centers, called MaxPass, to complement ECMP. A sender adaptively selects and dynamically changes multiple paths depending on the current network status like congestion. To monitor the network status, the corresponding receiver transmits a probe packet periodically to the senderits loss indicates a traffic congestion. We implemented MaxPass using commodity switches and carry out the quantitative analysis on the ns-2 simulator to show that MaxPass can improve the FCT and the throughput.데이터 센터 내에서 동작하는 다양한 어플리케이션들은 네트워크 트래픽을 보다 효율적으로 사용할 것을 요구한다. 데이터 센터 네트워크는 기존의 인터넷과는 다른 다중 루트 계층적 토폴로지로 구성되어 있으며, 다양한 트래픽 패턴을 가지고 있다. 이러한 특징으로 인해 데이터 센터 네트워크는 짧은 플로우 처리 완료 시간 (Flow Completion Time)과 높은 처리량 (Throughput)을 요구한다. 데이터 센터가 요구하는 조건들을 만족시키기 위한 방법 중 하나로는 등가 다중 경로 (Equal-Cost Multi-Path)와 같은 라우팅 기법을 활용하여 네트워크 부하를 서로 다른 링크에 분산시키는 것이 있다. 등가 다중 경로 라우팅 기법은 플로우의 메타 데이터를 해싱하여 플로우가 여러 경로를 이용할 수 있도록 한다. 그러나 랜덤한 결과를 도출하는 해시 함수의 특성에 따라 등가 다중 경로 라우팅 기법은 종종 트래픽을 고르게 분배하지 못함으로써, 링크의 전체 용량을 활용하기에는 한계가 있다. 따라서 본 논문에서는 데이터 센터 네트워크 환경에 맞는 새로운 부하 균형 배분 기법인 맥스패스 (MaxPass)를 제안한다. 맥스패스 내에서 데이터 송신자는 현재 네트워크 상태에 따라 경로를 동적으로 선택하고 변경한다. 데이터 수신자는 현재 네트워크 상태를 파악하기 위해 탐색 패킷을 주기적으로 송신자에게 보내고, 탐색 패킷 드랍 여부에 따라 혼잡도를 파악한다. 본 논문은 실제 스위치에서 맥스패스를 구현하였으며, ns-2 시뮬레이션을 기반한 실험을 통해 제안한 기법에 관하여 정량적 수치 분석을 수행하고, 플로우 처리 완료 시간과 링크 처리량의 성능 향상이 있음을 보여 준다.Chapter 1 Introduction 1 Chapter 2 Background 5 2.1 Data Center Network Topology . . . . . . . . . . . . . . . . . 5 2.2 Multipath Routing . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.3 Multipath Transport Protocol . . . . . . . . . . . . . . . . . . . 7 2.4 Credit-based Congestion Control . . . . . . . . . . . . . . . . 8 Chapter 3 MaxPass 10 3.1 Design Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 3.2 Switch Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 3.3 Path Probing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 3.4 Adaptive Path Selection . . . . . . . . . . . . . . . . . . . . . . .15 3.5 Feedback Control Algorithm . . . . . . . . . . . . . . . . . . . 15 3.6 Credit Stop . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .17 Chapter 4 Evaluation 20 4.1 Ns-2 Simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 4.1.1 Load Balancing . . . . . . . . . . . . . . . . . . . . . . . . . . 22 4.1.2 Throughput . . . . . . . . . . . . . . . . . . . . . . . . . . . . .23 4.1.3 Flow Completion Time (FCT) . . . . . . . . . . . . . . . .25 4.2 Testbed Experiments . . . . . . . . . . . . . . . . . . . . . . . . . 25 Chapter 5 Related Work 28 5.1 Centralized . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .28 5.2 Decentralized/Distributed . . . . . . . . . . . . . . . . . . . . . .30 Chapter 6 Conclusion 32 초록 38Maste