664 research outputs found
Datacenter Traffic Control: Understanding Techniques and Trade-offs
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.Comment: Accepted for Publication in IEEE Communications Surveys and Tutorial
SDN-BASED MECHANISMS FOR PROVISIONING QUALITY OF SERVICE TO SELECTED NETWORK FLOWS
Despite the huge success and adoption of computer networks in the recent decades, traditional network architecture falls short of some requirements by many applications. One particular shortcoming is the lack of convenient methods for providing quality of service (QoS) guarantee to various network applications. In this dissertation, we explore new Software-Defined Networking (SDN) mechanisms to provision QoS to targeted network flows. Our study contributes to providing QoS support to applications in three aspects. First, we explore using alternative routing paths for selected flows that have QoS requirements. Instead of using the default shortest path used by the current network routing protocols, we investigate using the SDN controller to install forwarding rules in switches that can achieve higher bandwidth. Second, we develop new mechanisms for guaranteeing the latency requirement by those applications depending on timely delivery of sensor data and control signals. The new mechanism pre-allocates higher priority queues in routers/switches and reserves these queues for control/sensor traffic. Third, we explore how to make the applications take advantage of the opportunity provided by SDN. In particular, we study new transmission mechanisms for big data transfer in the cloud computing environment. Instead of using a single TCP path to transfer data, we investigate how to let the application set up multiple TCP paths for the same application to achieve higher throughput. We evaluate these new mechanisms with experiments and compare them with existing approaches
Endpoint-transparent Multipath Transport with Software-defined Networks
Multipath forwarding consists of using multiple paths simultaneously to
transport data over the network. While most such techniques require endpoint
modifications, we investigate how multipath forwarding can be done inside the
network, transparently to endpoint hosts. With such a network-centric approach,
packet reordering becomes a critical issue as it may cause critical performance
degradation.
We present a Software Defined Network architecture which automatically sets
up multipath forwarding, including solutions for reordering and performance
improvement, both at the sending side through multipath scheduling algorithms,
and the receiver side, by resequencing out-of-order packets in a dedicated
in-network buffer.
We implemented a prototype with commonly available technology and evaluated
it in both emulated and real networks. Our results show consistent throughput
improvements, thanks to the use of aggregated path capacity. We give
comparisons to Multipath TCP, where we show our approach can achieve a similar
performance while offering the advantage of endpoint transparency
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
Reducing Internet Latency : A Survey of Techniques and their Merit
Bob Briscoe, Anna Brunstrom, Andreas Petlund, David Hayes, David Ros, Ing-Jyh Tsang, Stein Gjessing, Gorry Fairhurst, Carsten Griwodz, Michael WelzlPeer reviewedPreprin
Systems and Methods for Measuring and Improving End-User Application Performance on Mobile Devices
In today's rapidly growing smartphone society, the time users are spending on their smartphones is continuing to grow and mobile applications are becoming the primary medium for providing services and content to users. With such fast paced growth in smart-phone usage, cellular carriers and internet service providers continuously upgrade their infrastructure to the latest technologies and expand their capacities to improve the performance and reliability of their network and to satisfy exploding user demand for mobile data. On the other side of the spectrum, content providers and e-commerce companies adopt the latest protocols and techniques to provide smooth and feature-rich user experiences on their applications.
To ensure a good quality of experience, monitoring how applications perform on users' devices is necessary. Often, network and content providers lack such visibility into the end-user application performance. In this dissertation, we demonstrate that having visibility into the end-user perceived performance, through system design for efficient and coordinated active and passive measurements of end-user application and network performance, is crucial for detecting, diagnosing, and addressing performance problems on mobile devices. My dissertation consists of three projects to support this statement. First, to provide such continuous monitoring on smartphones with constrained resources that operate in such a highly dynamic mobile environment, we devise efficient, adaptive, and coordinated systems, as a platform, for active and passive measurements of end-user performance. Second, using this platform and other passive data collection techniques, we conduct an in-depth user trial of mobile multipath to understand how Multipath TCP (MPTCP) performs in practice. Our measurement study reveals several limitations of MPTCP. Based on the insights gained from our measurement study, we propose two different schemes to address the identified limitations of MPTCP. Last, we show how to provide visibility into the end- user application performance for internet providers and in particular home WiFi routers by passively monitoring users' traffic and utilizing per-app models mapping various network quality of service (QoS) metrics to the application performance.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/146014/1/ashnik_1.pd
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Understanding the characteristics of Internet traffic and designing an efficient RaptorQ-based data transport protocol for modern data centres
This thesis is the amalgamation of research on efficient data transport protocols for data centres and a comprehensive and systematic study of Internet traffic, which came as a result of the need to understand traffic patterns and workloads in modern computer networks.
The first part of the thesis is on the development of efficient data transport pro- tocols for data centres. We study modern data transport protocols for data centres through large scale simulations using the OMNeT++ simulator. We developed and experimented with an OMNeT++ model of NDP. This has led to the identification of limitations of the state of the art and the formulation of research questions with respect to data transport protocols for modern data centres. The developed model includes an implementation of a Fat-tree topology and per-packet ECMP load bal- ancing. We discuss how we integrated the model with the INET Framework and validated it by running various experiments that test different model parameters and components. This work revealed limitations of NDP with respect to efficient one-to-many and many-to-one communication in data centres, which led to the de- velopment of SCDP, a novel and general-purpose data transport protocol for data centres that, in contrast to all other protocols proposed to date, natively supports one-to-many and many-to-one data communication, which is extremely common in modern data centres. SCDP does so without compromising on efficiency for short and long unicast flows. SCDP achieves this by integrating RaptorQ codes with receiver-driven data transport, in-network packet trimming and Multi-Level Feed- back Queuing (MLFQ); (1) RaptorQ codes enable efficient one-to-many and many- to-one data transport; (2) on top of RaptorQ codes, receiver- driven flow control, in combination with in-network packet trimming, enable efficient usage of network re- sources as well as multi-path transport and packet spraying for all transport modes. Incast and Outcast are eliminated; (3) the systematic nature of RaptorQ codes, in combination with MLFQ, enable fast, decoding-free completion of short flows. We extensively evaluated SCDP in a wide range of simulated scenarios with realistic data centre workloads. For one-to-many and many-to-one transport sessions, SCDP performs significantly better than NDP. For short and long unicast flows, SCDP performs equally well or better compared to NDP.
In the second part of the thesis, we extensively study Internet traffic. Getting good statistical models of traffic on network links is a well-known, often-studied problem. A lot of attention has been given to correlation patterns and flow duration. The distribution of the amount of traffic per unit time is an equally important but less studied problem. We study a large number of traffic traces from many different networks including academic, commercial and residential networks using state-of-the-art statistical techniques. We show that the log-normal distribution is a better fit than the Gaussian distribution. We also investigate a second, heavy- tailed distribution and show that its performance is better than Gaussian but worse than log-normal. We examine anomalous traces which are a poor fit for all tested distributions and show that this is often due to traffic outages or links that hit maximum capacity. Stationarity tests showed that the traffic is stationary at some range of aggregation times. We demonstrate the utility of the log-normal distribution in two contexts: predicting the proportion of time traffic will exceed a given level (for link capacity estimation) and predicting 95th percentile pricing. We also show the log-normal distribution is a better predictor than Gaussian orWeibull distributions
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