Virtualized Congestion Control

Cronkite-Ratcliff, Bryce; Bergman, Aran; Vargaftik, Shay; Ravi, Madhusudhan; McKeown, Nick; Abraham, Ittai; Keslassy, Isaac

doi:10.1145/2934872.2934889

Cited by 79 publications

(42 citation statements)

References 41 publications

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“…5) Performance Isolation: Performance isolation is necessary in cloud environments where multiple tenants use shared network resources [74]- [76]. Isolation prevents selfish or malicious behavior that aims to either unfairly obtain more resources, such as by creating many flows or using custom aggressive transport protocols [77], [78], or to cause disruptions.…”

Section: Performance Isolation §Iii-c5mentioning

confidence: 99%

Datacenter Traffic Control: Understanding Techniques and Trade-offs

Noormohammadpour¹,

Raghavendra²

2017

Preprint

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Datacenters provide cost-effective and flexible access to scalable compute and storage resources necessary for today's cloud computing needs. A typical datacenter is made up of thousands of servers connected with a large network and usually managed by one operator. To provide quality access to the variety of applications and services hosted on datacenters and maximize performance, it deems necessary to use datacenter networks effectively and efficiently. Datacenter traffic is often a mix of several classes with different priorities and requirements. This includes user-generated interactive traffic, traffic with deadlines, and long-running traffic. To this end, custom transport protocols and traffic management techniques have been developed to improve datacenter network performance.In this tutorial paper, we review the general architecture of datacenter networks, various topologies proposed for them, their traffic properties, general traffic control challenges in datacenters and general traffic control objectives. The purpose of this paper is to bring out the important characteristics of traffic control in datacenters and not to survey all existing solutions (as it is virtually impossible due to massive body of existing research). We hope to provide readers with a wide range of options and factors while considering a variety of traffic control mechanisms. We discuss various characteristics of datacenter traffic control including management schemes, transmission control, traffic shaping, prioritization, load balancing, multipathing, and traffic scheduling. Next, we point to several open challenges as well as new and interesting networking paradigms. At the end of this paper, we briefly review inter-datacenter networks that connect geographically dispersed datacenters which have been receiving increasing attention recently and pose interesting and novel research problems.

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Section: Performance Isolation §Iii-c5mentioning

confidence: 99%

Datacenter Traffic Control: Understanding Techniques and Trade-offs

Noormohammadpour¹,

Raghavendra²

2017

Preprint

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“…Moreover, TCP proxy goes against TCP end-to-end semantics. For instance, a barrier-based application may believe that all its packets were ACKed, and advance to the next phase, while they were not actually received, potentially causing errors in the application [3] [6]. Furthermore, WAN optimizer adopts compression to speed up transfers but this may add additional latency on service and require ISPs to offer co-operating decompression appliances [3].…”

Section: Motivationsmentioning

confidence: 99%

“…Rather than proposing a new congestion control algorithm, our work investigates if congestion control can be implemented in a overlay manner. AC/DC [11] and vCC [6] are frontiers which converts default congestion control into operatordefined datacenter TCP congestion control. However, these two schemes target in intra-DC network environment and lack effective approaches to raise sending rate.…”

Section: Related Workmentioning

confidence: 99%

“…Furthermore, the split-connection approach used in TCP proxy would break a TCP connection into several sub-connections, destroying TCP end-to-end semantics. Applications may receive an ACK for the data which are actually still in transmitting, potentially violate the sequential processing order [3] [6]. On the other hand, WAN optimizers perform compression on data which may add additional latency and require additional decompression appliances in ISPs for decompressing data from optimizers [3].…”

Section: Introductionmentioning

confidence: 99%

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TCon: A Transparent Congestion Control Deployment Platform for Optimizing WAN Transfers

Zhang

Cui

Tso

et al. 2017

Lecture Notes in Computer Science

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Nowadays, many web services (e.g., cloud storage) are deployed inside datacenters and may trigger transfers to clients through WAN. TCP congestion control is a vital component for improving the performance (e.g., latency) of these services. Considering complex networking environment, the default congestion control algorithms on servers may not always be the most efficient, and new advanced algorithms will be proposed. However, adjusting congestion control algorithm usually requires modification of TCP stacks of servers, which is difficult if not impossible, especially considering different operating systems and configurations on servers. In this paper, we propose TCon, a lightweight , flexible and scalable platform that allows administrators (or operators) to deploy any appropriate congestion control algorithms transparently without making any changes to TCP stacks of servers. We have implemented TCon in Open vSwitch (OVS) and conducted extensive test-bed experiments by transparently deploying BBR congestion control algorithm over TCon. Test-bed results show that the BBR over TCon works effectively and the performance stays close to its native implementation on servers, reducing latency by 12.76% on average.

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“…With the development of computer networks, more and more colorful businesses are presented to us, and these services require the support of data center. The amount of data that the data center network (DCN) needs to process is getting larger and larger, and an effective congestion control method can ensure the data center to operate normally. Therefore, DCN's congestion control has become one of the hot topics in the field of computer networks.…”

Section: Introductionmentioning

confidence: 99%

A congestion control method of SDN data center based on reinforcement learning

Jin

Xin

et al. 2018

Int J Communication

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Summary With the development of cloud computing and big data, the internal communication business in data center has increased dramatically, and then the traffic in data center has also significantly increased. The bandwidth of data center is difficult to meet the bandwidth requirements of those intensive applications, and data center is facing a risk of network congestion. Under the background of the development of network intelligence, software‐defined network (SDN) should demonstrate its intelligence as a future network architecture. In this paper, we introduce reinforcement learning into the SDN data center to implement congestion control based on flow. We improve the Q‐learning and Sarsa algorithms and propose two methods of congestion control based on the algorithms. Test results show that these two congestion control methods can control congestion effectively. And Sarsa method has a better performance of link utilization. The average link utilization of the Sarsa method is 2.4% higher than the Q‐learning method and is 4.48% higher than the on‐demand method.

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Virtualized Congestion Control

Cited by 79 publications

References 41 publications

Datacenter Traffic Control: Understanding Techniques and Trade-offs

Datacenter Traffic Control: Understanding Techniques and Trade-offs

TCon: A Transparent Congestion Control Deployment Platform for Optimizing WAN Transfers

A congestion control method of SDN data center based on reinforcement learning

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