Network traffic characteristics of data centers in the wild

Benson, Theophilus; Akella, Aditya; Maltz, David A.

doi:10.1145/1879141.1879175

Cited by 1,765 publications

(1,150 citation statements)

References 26 publications

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“…2 (a). Using the self-similarity properties [8], [12], [13], [14], we can further notice that the inter-arrival times of packets that belong to the same flow also follow the same distribution as shown in Fig. 2 (b).…”

Section: Estimating the Number Of Remaining Flowsmentioning

confidence: 88%

“…For example, HP ProCurve 5406zl switch has a flow setup rate of 275 flows per second [4]. Meanwhile, the median flow arrival rate in a datacenter with 1500 servers is around 10 5 flows per second [7] and the worst flow arrival rate in a data center with 100 edge switches is around 10 7 flows per second [8].…”

Section: Motivationmentioning

confidence: 99%

“…The ratio of intra rack traffic and extra rack traffic in the experiment is 2 to 3 [8]. Intra rack traffic is the communication among severs in the same rack using the pass-through ToR switch.…”

Section: A Experimental Setupmentioning

confidence: 99%

“…Since the number of hosts varies from 120 to 480, we need to determine appropriate size for the flow table in order to simulate general eviction workloads. From the workloads reported in [4], [7], [8], we decided to use 40 to 50 entries for the flow table size. It is worthy to note that the size of a flow table in the switch is limited and the evictions from the table occur frequently in a real environment.…”

Section: A Experimental Setupmentioning

confidence: 99%

See 3 more Smart Citations

A Dynamic Timeout Control Algorithm in Software Defined Networks

Kim¹,

Lee²,

Lee³

et al. 2014

IJFCC

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Abstract-While Software Defined Networking (SDN) has received a considerable amount of attention, improving the scalability of an SDN controller has always been a major concern. One of the main reasons why the controller suffers from this scalability problem is that it is fairly often overwhelmed by a large number of flow setup requests from the SDN switches to the controller. These requests cause decrease in the number of switches that the controller can deal with. Since these requests are usually generated when flows arrive at the switch and their corresponding entries do not exist in the flow table due to eviction, minimizing the number of evictions also reduces the number of requests to the controller. This paper addresses the scalability problem and proposes the algorithm improving the scalability of the SDN controller by dynamically controlling the timeout value of each flow without modifying the switches. In the proposed approach, the controller collects various traffic parameters from the switches and predicts the inter-arrival times of packets in a flow. Based on the information, it dynamically adjusts the timeout value of each flow to reserve spaces in the flow table for newly arrived flows in advance. As a result, this avoids the evictions and reduces the number of flow setup requests to the controller. The benchmarking results show that the proposed algorithm reduces the number of packets to the controller by 9.9 %.

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Section: Estimating the Number Of Remaining Flowsmentioning

confidence: 88%

Section: Motivationmentioning

confidence: 99%

Section: A Experimental Setupmentioning

confidence: 99%

Section: A Experimental Setupmentioning

confidence: 99%

See 2 more Smart Citations

A Dynamic Timeout Control Algorithm in Software Defined Networks

Kim¹,

Lee²,

Lee³

et al. 2014

IJFCC

View full text Add to dashboard Cite

show abstract

“…It creates traffic profiles with different distributions of flow inter-arrival times and flow sizes. This allows us to evaluate Dahu's performance over a wide range of traffic patterns, including those based on existing literature [6,16]. Modeling the AIMD behavior of TCP flow bandwidth instead of per-packet behavior means that the simulator does not model TCP timeouts, retransmits, switch buffer occupancies and queuing delay in the network.…”

Section: Simulatormentioning

confidence: 99%

Dahu: Commodity switches for direct connect data center networks

Radhakrishnan

Tewari

Kapoor

et al. 2013

Architectures for Networking and Communications Systems

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Solving "Big Data" problems requires bridging massive quantities of compute, memory, and storage, which requires a very high bandwidth network. Recently proposed direct connect networks like HyperX [1] and Flattened Butterfly [20] offer large capacity through paths of varying lengths between servers, and are highly cost effective for common data center workloads. However data center deployments are constrained to multi-rooted tree topologies like Fat-tree [2] and VL2 [16] due to shortest path routing and the limitations of commodity data center switch silicon.In this work we present Dahu 1 , simple enhancements to commodity Ethernet switches to support direct connect networks in data centers. Dahu avoids congestion hot-spots by dynamically spreading traffic uniformly across links, and forwarding traffic over non-minimal paths where possible. By performing load balancing primarily using local information, Dahu can act more quickly than centralized approaches, and responds to failure gracefully. Our evaluation shows that Dahu delivers up to 500% improvement in throughput over ECMP in large scale HyperX networks with over 130,000 servers, and up to 50% higher throughput in an 8,192 server Fat-tree network.

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Datacenter Networks and Relevant Standards

Fonseca

Boutaba

2015

Cloud Services, Networking, and Management

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Datacenters are the core of cloud computing, and their network is an essential component to allow distributed applications to run efficiently and predictably [17]. However, not all datacenters provide cloud computing. In fact, there are two main types of datacenters: production and cloud. Production datacenters are often shared by one tenant or among multiple (possibly competing) groups, services and applications, but with low rate of arrival and departure. They run data-analytics jobs with relatively little variation in demands, and their size varies from hundreds of servers to tens of thousands of servers. Cloud datacenters, in contrast, have high rate of tenant arrival and departure (churn) [75], run both user-facing applications and inward computation, require elasticity (since application demands are highly variable), and consist of tens to hundreds of thousands of physical servers [12]. Moreover, clouds can be composed of several datacenters spread around the world. As an example, Google, Microsoft and Amazon (three of the biggest players in the market) have datacenters in four continents; and each company has over 900, 000 servers.This chapter presents a in-depth study of datacenter networks (DCNs), relevant standards and operation. Our goal here is three-fold: i) provide a detailed view of the net-Cloud Services, Networking and Management, First Edition.

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Network traffic characteristics of data centers in the wild

Cited by 1,765 publications

References 26 publications

A Dynamic Timeout Control Algorithm in Software Defined Networks

A Dynamic Timeout Control Algorithm in Software Defined Networks

Dahu: Commodity switches for direct connect data center networks

Datacenter Networks and Relevant Standards

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