wireless sensor network

5IEEE Network • July/August 2014 0890-8044/14/$25.00 © 2014 IEEE

ur world is generating data at a speed fasterthan ever before. In 2010, 5 exabytes (1018bytes, or 1 billion gigabytes) of data were cre-ated every two days, exceeding the total

amount of information that was created by human beingsfrom the dawn of civilization to 2003.1 Till 2020, over 40Zettabytes (1021 bytes) of data would be created, replicated,and consumed.2 With the overwhelming amount of data pour-ing into our lives, from anywhere, anytime, and any device, weare undoubtedly entering the era of big data.

Big data brings big value. With advanced big data analyzingtechnologies, insights can be acquired to enable better deci-sion making for critical development areas such as healthcare, economic productivity, energy, and natural disaster pre-diction, to name but a few. For example, by collecting andanalyzing flu related keyword searches, Google has developedthe Flu Trends service to detect regional flu outbreaks in nearreal time. Specifically, Google Flu Trends collected historicalsearch frequency data of 50 million common keywords each

week from 2003 to 2008. Then a linear model was used tocompute the correlation coefficient between each keywordsearch history data and the actual influenza-like illness historydata obtained from the Centers for Disease Control and Pre-vention (CDC) in the United States. After that, the keywordswith the highest correlation coefficients were picked out, andtheir instant search frequencies were aggregated to predictfuture flu outbreaks in the United States. With big data inkeyword searches, Google Flu Trends is able to detect flu out-breaks over a week earlier than CDC, which can significantlyreduce the loss caused by the flu and even save lives. Anotherexample comes from the United Parcel Service (UPS), whoequips its vehicles with sensors to track their speed and loca-tion. With the sensed data, UPS has optimized its deliveryroutes and cut its fuel consumption by 8.4 million gallons in2011.3 It has been reported that big data analytics is amongthe top five catalysts that help increase U.S. productivity andraise the gross domestic product (GDP) in the coming years.4

In general, these big data are stored in data warehousesand processed in powerful data centers with massive intercon-nected server nodes. There have been significant studies onknowledge discovery and mining over the data and high-per-formance parallel computing tools for big data (e.g., MapRe-duce). But big data are not just born and reside there — theyare transmitted from a variety of sources and are to be uti-lized at a variety of destinations for broad purposes. The lifecycle of big data consists of multiple stages ranging from gen-

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Abstract

Big data, with their promise to discover valuable insights for better decision mak-ing, have recently attracted significant interest from both academia and industry.Voluminous data are generated from a variety of users and devices, and are to bestored and processed in powerful data centers. As such, there is a strong demandfor building an unimpeded network infrastructure to gather geologically distributedand rapidly generated data, and move them to data centers for effective knowl-edge discovery. The express network should also be seamlessly extended to inter-connect multiple data centers as well as interconnect the server nodes within adata center. In this article, we take a close look at the unique challenges in build-ing such a network infrastructure for big data. Our study covers each and everysegment in this network highway: the access networks that connect data sources,the Internet backbone that bridges them to remote data centers, as well as the ded-icated network among data centers and within a data center. We also present twocase studies of real-world big data applications that are empowered by network-ing, highlighting interesting and promising future research directions.

Building a Network Highway for Big Data: Architecture and Challenges

Xiaomeng Yi, Fangming Liu, Jiangchuan Liu, and Hai Jin

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Xiaomeng Yi, Fangming Liu, and Hai Jin are with the Services ComputingTechnology and System Lab, Cluster and Grid Computing Lab in theSchool of Computer Science and Technology, Huazhong University of Sci-ence and Technology.

Jiangchuan Liu is with Simon Fraser University.

1 “Gold Rush: The Scramble to Claim and Protect Value in the DigitalWorld,” Deloitte Review, 2011

2 “The Digital Universe in 2020: Big Data, Bigger Digital Shadows, andBiggest Growth in the Far East,” IDC: Analyze the Future, 2012.

3 “Big Data in Big Companies,” SAS Research Report, 2013.

4 “Game Changers: Five Opportunities for US Growth and Renewal,”McKinsey Global Institute Report, 2013.

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IEEE Network • July/August 20146

eration, collection, aggregation, processing, and applicationdelivery. While big data aggregation and processing occurmostly in data centers, the data are generated by and collect-ed from geographically distributed devices, and the dataknowledge/service are then distributed to interested users; thelatter heavily depends on inter-data-center networks, accessnetworks, and the Internet backbone, as depicted in Fig. 1. Assuch, networking plays a critical role as the digital highwaythat bridges data sources, data processing platforms, and datadestinations. There is a strong demand to build an unimpedednetwork infrastructure to gather geologically distributed andrapidly generated data, and move them to data centers foreffective knowledge discovery. The express network shouldalso be extended to interconnect the server nodes within adata center and interconnect multiple data centers, thus col-lectively expanding their storage and computing capabilities.

Data explosion, which has been a continuous trend sincethe 1970s, is not news. The Internet has long grown togetherwith the explosion, and has indeed greatly contributed to it.The three Vs (volume, variety, and velocity) from today’s bigdata, however, are unprecedented. It remains largely unknownwhether the Internet and related networks can keep up withthe rapid growth of big data.

In this article, we examine the unique challenges when bigdata meet networks, and when networks meet big data. Wecheck the state of the art for a series of critical questions:What do big data ask for from networks? Is today’s networkinfrastructure ready to embrace the big data era? If not,where are the bottlenecks? And how could the bottlenecks belifted to better serve big data applications? We take a closelook at building an express network infrastructure for bigdata. Our study covers each and every segment in this net-work highway: the access networks that connect data sources,the Internet backbone that bridges them to remote data cen-ters, and the dedicated network among data centers and with-in a data center. We also present two case studies ofreal-world big data applications that are empowered by net-working, highlighting interesting and promising futureresearch directions.

A Network Highway for Big Data: Why andWhere?

With so much value hiding inside, big data have been regard-ed as the digital oil, and a number of government funded pro-jects have been launched recently to build up big dataanalyzing systems, involving a number of critical areas rangingfrom healthcare to climate change and homeland security. Aprominent example is the $200 million National Big DataResearch & Development Initiative of the United States,announced by the White House in March 2012.5 This initia-tive involves six departments and agencies in the UnitedStates, and aims to improve the tools and techniques neededto access, organize, and glean discoveries from big data. A listof representative projects worldwide is presented in Table 1.

In industry, there is also strong interest in exploiting bigdata to gain business profits. Advances in sensor networking,cyber-physical systems, and Internet of things have enabledfinancial service companies, retailers, and manufacturers tocollect their own big data in their business processes. On theother hand, through such utility computing services as cloudcomputing, high-performance IT infrastructures and platformsthat were previously unaffordable are now available to abroader market of medium and even small companies. As aresult, more and more enterprises are seeking solutions toanalyze the big data they generate. Gartner’s survey on 720companies in June 2013 shows that 64 percent of companiesare investing or planning to invest in big data technologies.6 Anumber of big data analytic platforms have already emergedin this competitive market. For example, Google launched

5 “Obama Administration Unveils ‘Big Data’ Initiative: Announces $200Million in New R&D Investments,” Office of Science and Technology Pol-icy, The White House, 2012.

6 “Survey Analysis: Big Data Adoption in 2013 Shows Substance Behindthe Hype,” Gartner, 2013.

Figure 1. Three-layered network architecture from the perspective of big data applications.

Cellularantenna

Sensor

PDA

Mobilephone

Laptop

PC

Gateway ISP 1

CDN

Access network

Internet backbone

Inter- and intra-data center network

...

ISP 2WiFiAP

Replicationbig data

Contentbig data

DC networkAnalyticalbig data

...

Sensorbig data

Mobilebig data


IEEE Network • July/August 2014 7

BigQuery, a big data service platform that enables customersto analyze their data by exploiting the elastic computingresources in Google’s cloud; SAP also released its in-memorybig data platform HANA, which is capable of processing largevolumes of structured and unstructured data in real time.

In both government-funded research projects and business-oriented services, the life cycle of big data consists of multiplestages, as illustrated in Fig. 1. At first, the user data are gener-ated from a variety of devices and locations, which are collect-ed by wired and wireless networks. The data are thenaggregated and delivered to data centers via the global Inter-net. In data centers, the big data are processed and analyzed.Finally, the results are delivered back to users or devices ofinterest and utilized.

Obviously, networks play a critical role in bridging the dif-ferent stages, and there is a strong demand to create a fastand reliable interconnected network for the big data to flowfreely on this digital highway. This network highway concernsnot only just one segment of data delivery, but rather thewhole series of segments for the life cycle of big data, fromaccess networks to the Internet backbone, and to intra- andinter-data-center networks. For each layer of the network, thespecific requirements big data transmission poses should besatisfied.

Access networks, which are directly connected to enddevices such as personal computers, mobile devices, sensors,and radio frequency identification (RFID) devices, lie in theouter layer. On one hand, such raw data from fixed or mobiledevices are transmitted into the network system; on the otherhand, processed big data and analytics results are sent back to

devices and their users. With the rapid development of wire-less networks, more data are now collected from mobiledevices that have limited battery capacity. As such, energy-efficient networking is expected to make batteries in mobiledevices more durable. Wireless links also suffer from interfer-ence, which results in unstable bandwidth provisioning. Bigdata applications such as cinematic-quality video streamingrequire sustained performance over a long duration to guar-antee the quality of user experience, which has become a criti-cal challenge for wireless networking.

The Internet backbone serves as the intermediate layerthat connects access networks and data center networks. Pop-ular big data applications like photo sharing and video shar-ing allow users to upload multimedia contents to data centersand share them with their friends in real time. To enablegood user experience, the Internet backbone needs to for-ward massive geologically distributed data to data centerswith high throughput, and deliver processed data to usersfrom data centers with low latency. As such, high-perfor-mance end-to-end links are required for uploading, and effi-cient content distribution networks (CDNs) are required fordownloading.

Within a data center, big data are processed and analyzedwith distributed computing tools such as MapReduce andDryad, which involve intensive data shuffle among servers. Ascalable, ultra-fast, and blocking-free network is thus neededto interconnect the server nodes. Multiple geologically dis-tributed data centers can be exploited for load balancing andlow-latency service provisioning, which calls for fast network-ing for data exchange, replication, and synchronization amongthe data centers. Moreover, inter-data-center links are leasedfrom Internet service providers (ISPs) or dedicatedly deployedby cloud providers with nontrivial costs. Effective data trans-mission and traffic engineering schemes (e.g., using software

Table 1. Representative government-funded big data projects.

Project Begintime Department Goal

1000 GenomesProject 1/2008 National Institutes of

HealthTo produce an extensive public catalog of human genetic variation,including SNPs and structural variants, and their haplotype contexts.

ARM Project 3/2012 Department of EnergyTo collect and process climate data from all over the world to under-stand Earth’s climate and come up with answers to climate changeissues.

XDATA 3/2012Defense AdvancedResearch Projects Agency(DARPA)

To develop new computational techniques and software programsthat can analyze structured and unstructured big data sets faster andmore efficiently.

BioSense 2.0 3/2012 Center for Disease Controland Prevention

To track public health problems and make data instantly accessible toend users across government departments.

The Open ScienceGrid 3/2012

National ScienceFoundation (NSF) andDepartment of Energy

To provide advanced fabric of services for data transfer and analysis toscientists worldwide for collaboration in science discovery.

Big Data for EarthSystem Science 3/2012 U.S. Geological Survey

To provide scientists with state-of-the-art computing capabilities andcollaborative tools to make sense of huge data sets and better under-stand the earth.

Human Brain Project 2/2013 European Commission To simulate the human brain and model everything that scientistsknow about the human mind using a supercomputer.

Unique IdentificationAuthority 2/2009 The Indian Planning

Commission

To create a biometric database of fingerprints, photographs, and irisscan images of all 1.2 billion people for efficient resident identifica-tion in welfare service delivery.

7 “The Zettabyte Era—Trends and Analysis,” Cisco Visual NetworkingIndex white paper, 2013.



defined networking, SDN) that fully utilize the capacity ofsuch links are therefore expected. For instance, Google andMicrosoft both attempt to deploy SDN-based global data cen-ter WANs to achieve fault tolerance, utilization improvement,and policy control that can hardly be realized by traditionalWAN architectures.

In summary, to attain full speed for big data transmissionand processing, every segment of the network highway shouldbe optimized and seamlessly concatenated. We proceed toinvestigate each part of the network system by identifying theunique challenges that big data applications pose and analyz-ing the state-of-the-art works that endeavor to build the net-work highway for big data.

Access Network: Linking SourcesWith the fast progress of digitalization and the developmentof sensor networks, huge amounts of data are collected by allkinds of end devices like PCs, smartphones, sensors, and GPSdevices. Meanwhile, applications like online social networksand video streaming push rich content big data into userdevices. The access network plays a critical role here in gath-ering such distributed data and forwarding them through theInternet to data centers. While the last mile problem has beenaddressed well with today’s high-speed home and office net-work connections, the wireless connection remains a severebottleneck. Cisco predicts that traffic from wireless andmobile devices will exceed traffic from wired devices by 2016,which makes wireless network performance optimization ofparamount importance.

Wireless broadband technologies have evolved significantlyin recent years; but when facing data-intensive applications,they are still insufficient to satisfy the bandwidth require-ments. Yoon et al. exploit the wireless broadcast advantageand use it to bridge the gap between wireless networkingcapacity and the bandwidth demands of video applications.MuVi [1], a multicast delivery scheme, is proposed to opti-mize video distribution. By prioritizing video frames accordingto their importance in video reconstruction and exploiting aresource allocation mechanism that maximizes the system util-ity, MuVi improves the overall video quality across all users ina multicast group.

Multiple-input multiple-output orthogonal frequency-divi-sion multiplexig (MIMO-OFDM) technologies, which can sig-nificantly increase wireless capacity, have become the defaultbuilding blocks in the next generation of wireless networks.Liu et al. [2] observe that in wireless video streaming applica-tions, both the video coding scheme and the MIMO-OFDMchannel present non-uniform energy distribution among theircorresponding components. Such non-uniform energy distri-bution in both the source and the channel can be exploitedfor fine-grained unequal error protection for video delivery inerror-prone wireless networks. To this end, ParCast, an opti-mized scheme for video delivery in MIMO-OFDM channels,is proposed. It separates the video coding and wireless chan-nel into independent components and allocates more impor-tant video components with higher-gain channel components.This leads to significantly improved quality for video overwireless.

In addition to the aforementioned works, there are severalother promising approaches to improve the quality of wire-less video streaming services. On the user’s side, an applica-tion-aware MIMO video rate adaptation mechanism can bedeployed. It detects changes in a MIMO channel and adap-tively selects an appropriate transmission profile, therebyimproving the quality of the delivered video. On the server’sside, standard base station schedulers work on a fine-grained

per-packet basis to decrease the delivery delay of singlepackets. However, it is insufficient to guarantee video watch-ing experience at a coarse granularity, such as a fixed videobit rate over several seconds, which would typically consist ofvideo content in thousands of packets. In response, a videomanagement system, which schedules wireless video deliveryat a granularity of seconds with knowledge of long-termchannel states, has the potential to further improve userexperience. Moreover, distortion and delay, which are twoimportant user experience metrics, conflict with each otherin wireless networks. The optimal trade-off between distor-tion and delay in wireless video delivery systems largelydepends on the specific features of video flows. As such, apolicy that smartly balances distortion and delay accordingto the features of video flows can also improve user experi-ence.

Internet Backbone: From Local to RemoteBeyond the access network, the user or device generated datawill be forwarded through the Internet backbone to data cen-ters. For example, in mobile cloud computing services [3],where powerful cloud resources are exploited to enhance theperformance of resource-constrained mobile devices, datafrom geologically distributed mobile devices are transmittedto the cloud for processing. Given that the local data comeworldwide, the aggregated data toward a data center can beenormous, which creates significant challenges to the Internetbackbone. Table 2 summarizes the Internet backbone solu-tions for big data.

End-to-End TransmissionWith the growing capacity of access links, network bottlenecksare observed to be shifting from the network edges in accessnetworks to the core links in the Internet backbone. Toimprove the throughput of end-to-end data transmission, pathdiversity should be explored, which utilizes multiple pathsconcurrently to avoid individual bottlenecks. A representativeis mPath [4], which uses a large set of geographically distribut-ed proxies to construct detour paths between end hosts. Anadditive increase and multiplicative decrease (AIMD) algo-rithm similar to TCP is used to deal with congested proxypaths to adaptively regulate the traffic over them, or evencompletely avoid them.

Besides uploading, the big data, after being processedby the data centers, also need to be downloaded by usersto appreciate the inside value. Downloading, however,poses different demands. For applications like onlinesocial networks, it is critical to deliver user required con-tents with low latency while providing a consistent serviceto all users. Wittie et al. [5] reverse engineered Facebook,investigating the root causes of its poor performance whenserving users outside of the United States. They suggestthat this can be improved by exploring the locality of inter-est, which, with proxy and caching, can dramatically reducethe backbone traffic of such online social networks as wellas its access delay.

Content Delivery NetworkFor geologically distributed data consumers, CDNs can beexplored to serve them with higher throughput. High through-put is typically achieved in two ways: optimizing path selec-tion to avoid network bottlenecks, and increasing the numberof peering points. Yu et al. [6] introduce a simple model toillustrate and quantify the benefit of them. Using both syn-thetic and Internet network topologies, they show thatincreasing the number of peering points improves the



throughput the most, while optimal path selection has onlylimited contribution. Liu et al. [7] further find that videodelivery optimized for low latency or high average throughputmay not work well for high-quality video delivery thatrequires sustained performance over a long duration. Thisleads to an adaptive design with global knowledge of networkand distribution of clients.

To further reduce the operational cost of big data trafficover CDN, Jiang et al. [8] suggest that the CDN infrastruc-tures can be extended to the edges of networks, leveragingsuch devices such as set-top boxes or broadband gateways.Their resources can be utilized through peer-to-peer commu-nications with smart content placement and routing to miti-gate the cross-traffic among ISPs.

Table 2. A taxonomy of Internet backbone, intra- and inter-data-center solutions for big data.

Internet backbone

Approaches Networkinfrastructure

Big dataapplication Goal Technique Evaluation method Overhead

mPath [4] Internetbackbone

End-to-endtransmission

Avoidbottleneck

AIMD algo-rithm

Implementation onPlanetLab Proxy node deployment

Wittie et al. [5] Internetbackbone

Socialnetworks

Reduceservicelatency

TCP proxy,caching

Trace-drivensimulation

Cache and proxydeployment

Liu et al. [7] CDN Videostreaming

ImproveQoS

Bit rateadaption

Trace-drivensimulation Low scalability

Jiang et al. [8] CDN Videostreaming

Reduceoperationalcost

CDN extensionSynthetic andtrace-drivensimulation

Trackerdeployment

Intra- and inter-data-center networks

Approaches Networkinfrastructure

Big dataapplication Goal Technique Evaluation method Overhead

Hedera [9] Data centernetworks

Dataprocessing

Optimizenetworkutilization

Flowscheduling

Simulation,implementation onPortland testbed

Centralized control, lowscalability

FlowComb [10] Data centernetworks

MapReduce/Dryad

Optimizenetworkutilization

Flowscheduling

Implementation onHadoop testbed

Monitor and transferdemand information

Orchestra [11] Data centernetworks

MapReduce/Dryad

Reduce jobduration

Transferscheduling

Implementation onAmazon EC2 andDETERlab

Modify the distributedframework

RoPE [12] Data centernetworks

MapReduce/Dryad

Reduce jobduration

Execution planoptimization

Implementation onBing’s productioncluster

Pre-run jobs to acquirejob property

Camdoop [13]Data centernetworktopology

MapReduce/Dryad

Decreasenetworktraffic

Dataaggregation

Implementation onCamCube

Special networktopology deployment

Mordia [14]OCSdata centernetwork

Dataprocessing

Reduceswitchingdelay

OCS, trafficmatrixscheduling

Prototypeimplementation

Hardware/topologydeployment

3D Beamform-ing [15]

Wirelessdata centernetwork

Dataprocessing

Flexiblebandwidthprovisioning

60 GHzwireless links

Local testbed,simulation

Physical antenna/reflector deployment

NetStitcher [16] Inter-data-center links

Data backupandmigration

Improvenetworkutilization

Store-and-forwardalgorithm

Emulation, livedeployment

Periodical schedulerecomputation

Jetway [17] Inter-data-center links

Videodelivery

Minimizelink cost

Flowassignmentalgorithm

Simulation,implementation onAmazon EC2

Centralized controller,video flow tracking



Data Center Networks: Where BigData Are Stored and ProcessedBig data collected from end devices are storedand processed in data centers. Big data applica-tions such as data analysis and deep learning usu-ally exploit distributed frameworks likeMapReduce and Dryad to achieve inflexibilityand scalability. Data processing in those distribut-ed frameworks consists of multiple computationalstages (e.g., map and reduce in the MapReduceframework). Between the stages, massive amountsof data need to be shuffled and transferred amongservers. The servers usually communicate in anall-to-all manner, which requires high bisection bandwidth indata center networks. As such, data center networks oftenbecame a bottleneck for those applications, with data trans-fers accounting for more than 33 percent of the running timein typical workloads. In Table 2, we summarize the state-of-the-art solutions toward engineering better intra- and inter-data-center networks.

Dynamic Flow SchedulingAs shown in Fig. 2, there are multiple equal-cost pathsbetween any pair of servers in a typical multi-rooted treetopology of a data center network. To better utilize the paths,Hedera [9] is designed to dynamically forward flows alongthese paths. It collects flow information from switches, com-putes non-conflicting paths for flows, and instructs switches toreroute traffic accordingly. With a global view of routing andtraffic demands, Hedera is able to maximize the overall net-work utilization with only small impact on data flows. Throughmonitoring the flow information in switches, it schedules dataflows only after they cause congestion at certain location. Toactually avoid congestion, however, the data flows should bedetected even before they occur. This is addressed in Flow-Comb [10], which predicts data flows effectively by monitoringMapReduce applications on servers through software agents.A centralized decision engine is designed to collect data fromthe agents and record the network information.

Transfer Optimization in MapReduce-Like FrameworksIn addition to scheduling individual flows, optimizing theentire data transfer has the potential to further reduce jobcompletion time. To this end, Orchestra [11] optimizes com-mon communication patterns like shuffle and broadcast bycoordinating data flows. When there are concurrent transfers,Orchestra enforces simple yet effective transfer schedulingpolicies such as first-in first-out (FIFO) to reduce the averagetransfer time. This is further enhanced in RoPE [12] by opti-mizing job execution plans. An execution plan specifies theexecution order of operations in a job, as well as the degree ofparallelism in each operation. RoPE employs a composablestatistics collection mechanism to acquire code and data prop-erties in a distributed system, and then automatically gener-ates optimized executions plans for jobs, which reduces thevolume of data to be transferred.

Novel Topology and HardwareNovel network topologies have also been proposed to improvethe network performance in big data processing. Costa et al.[13] observe that in common workloads, data volumes aregreatly reduced after the progress of data processing. With thisinsight, a data processing framework, Camdoop, is proposed. Itis built on a novel network topology where the servers aredirectly connected, through which partial data can be aggregat-ed on servers along the path to minimize data transmission.

Recently, optical circuit switching (OCS) has been suggest-ed to accommodate the fast growing bandwidth demands indata center networks. The long circuit reconfiguration delayof OCSs, however, hinders their deployment in modern datacenters. To address this issue, Porter et al. [14] propose noveltraffic matrix scheduling (TMS), which leverages applicationinformation and short-term demand estimates to computeshort-term circuit schedules, and proactively communicatescircuit assignments to communicating entities. As such, it cansupport flow control in microseconds, pushing down thereconfiguration time by two to three orders of magnitude. Onthe other hand, wireless links in the 60 GHz band are attrac-tive to relieve hotspots in oversubscribed data center net-works. The 60 GHz wireless links require direct line of sightbetween sender and receiver, which limits the effective rangeof wireless links. Moreover, they can suffer from interferencewhen nearby wireless links are working. To deal with these,Zhou et al. [15] propose a new wireless primitive, 3D beam-forming, for data centers. By bouncing wireless signals off theceiling, 3D beamforming avoids blocking obstacles, thusextending the range of each wireless link. Moreover, the sig-nal interference range is significantly reduced, allowing nearbylinks to work concurrently.

Inter-Data-Center LinksBig-data-based services such as social networks usually exploitseveral geographically distributed data centers for replicationand low-latency service provision. Those data centers areinterconnected by high-capacity links leased from ISPs. Oper-ations like data replication and synchronization require highbandwidth transformation between data centers. It is thus crit-ical to improve the utilization or reduce the cost for suchinter-data-center links.

Laoutaris et al. [16] observe a diurnal pattern of userdemand on inter-data-center bandwidth, which results in lowbandwidth utilization in off-peak hours. They propose Net-Stitcher to utilize the leftover bandwidth in off-peak hours forsuch non-real-time applications as backups and data migra-tions. As illustrated in Fig. 3, NetStitcher exploits a store-and-forward algorithm to transfer big data among data centers.The data are split into pieces and transferred to their destina-tion along multiple paths, each of which consists of a series ofintermediate data centers. A scheduling module decides whenand where the data pieces should travel according to theavailable bandwidth.

For real-time big data applications like video streaming,Feng et al. [17] propose Jetway, which, based on a widely usedpercentile charging model, uses the qth largest traffic volume ofall time intervals during the charging period as the chargingvolume. If the current time interval’s traffic volume exceedsthat of the qth percentile of previous time intervals, it willincur additional cost. Otherwise, the already paid for band-width will not be fully used. Jointly considering the link capac-

Figure 2. Data flow scheduling and data shuffling techniques in intra-data-center networks.

Individualdata flow

Data shufflingin MapReduce

Data flowprediction

Multi-rooted tree topology

Equal-costPath

......



ity, bandwidth availability, delay-tolerant degree of flows, andprevious traffic volume of links, Jetway can minimize the costof inter-data-center links.

In SummaryThe aforementioned research efforts propose solutions toserve big data applications at different levels ranging from themacroscopic multi-data-center level to the microscopic individ-ual flows. Specifically, from the macroscopic level, inter-data-center links enable multiple geologically distributed datacenters to provide such consistent reliable big data services asvideo streaming and social networking with low latency. Atthe single-data-center level, novel network topologies andhardware devices provide cost-effective solutions toward ahigh-performance network that interconnects all servers. Atthe microscopic level, coordinated all-to-all communicationsamong a subset of servers in a data center mitigate networkcongestion and reduce the completion times of data process-ing jobs. In particular, for individual data flows between a spe-cific pair of servers, dynamic flow scheduling techniqueschoose proper paths for data flows to avoid network resourcecompetition. Nevertheless, there remain significant open ques-tions to be addressed in the literature, such as how to sched-ule data flows in order to meet their specific completiondeadlines, and how to provide guaranteed network perfor-mance to multiple concurrent data processing jobs in a datacenter. To achieve a comprehensive understanding of theseproblems, Xu et al. [18] have investigated the state-of-the-artresearch in the literature on providing guaranteed networkperformance for tenants in Internet as a service (IaaS) clouds.

Big-Data-Based Networking ApplicationsWe generally classify big data applications into two categories,Internet applications and mobile wireless network applications,with regard to the networking infrastructure on which theywork. For each category, we discuss the benefit and opportu-nity that big data brings, by analyzing representative applica-tions depicted in Fig. 4.

Internet ApplicationsOne of the prominent big data applications closely related toour daily lives is Netflix, which offers streaming video-on-demand services and now takes up a third of U.S. download

Internet traffic during peak traffic hours. To support the com-bination of huge traffic and unpredictable demand bursts,Netflix has developed a global video distribution system usingAmazon’s cloud. Specifically, depending on customer demand,Netflix’s front-end services are running on 500 to 1000 Linux-based Tomcat JavaServer and NGINX web servers. These areempowered by hundreds of other Amazon S3 and NoSQLCassandra database servers using the Memcached high-perfor-mance distributed memory object caching system.

Netflix purchases master copies of digital films from moviestudios and, using the powerful Amazon EC2 cloud machines,converts them to over 50 different versions with differentvideo resolutions and audio quality, targeting a diverse arrayof client video players running on desktop computers, smart-phones, and even DVD players or game consoles connectedto television. The master copies and the many convertedcopies are stored in Amazon S3. In total, Netflix has over 1petabyte of data stored on Amazon.

Thanks to insights from such big video data and the associ-ated client behavior data as the programs they are watching,their demographics and preferences, Netflix made a big deci-sion of bidding over $100 million for two seasons of the U.S.version of “House of Cards” in 2011, which turned out to be agreat success. The “House of Cards” series, directed by DavidFincher, starring Kevin Spacey, is based on a popular Britishseries. Big data in Netflix show that its British version hasbeen well watched, and the same subscribers who loved theoriginal BBC production also like movies starring KevinSpacey or directed by David Fincher. The analytical results ofbig data indicate that “House of Cards” would bring signifi-cant business value to Netflix. In fact, the series has broughtNetflix over two million new subscribers in the United Statesand one million outside the United States. Netflix’s purchaseof “House of Cards” is largely regarded as a great success ofbig data.

Mobile Wireless Network ApplicationsPowered by advanced technologies in mobile networking, sen-sor networking, and the Internet of Things, mobile wirelessbig data applications are emerging in our lives. As a typicalexample of them, Nike+ provides improved service to Nikeusers with wireless connected sensing devices and smart-phones. In particular, Nike enhances its products, such asshoes and wristbands, with built-in sensors that continuously

Figure 3. Emerging network architectures of different data centers, and multipath store-and-forwarddata transfer among geo-distributed data centers.

Multi-rooted treetopology

Novel topologywith servers

directly connected

Wirelessdatacenternetwork

Source

Store and forward

Multipathdata transfer

Percentilecharging

Videodelivery

Destination

Datamigration

Data backup

Optical circuitswitching

datacenter network



track the users’ movement during their workouts. The userscan install Nike+ apps in their smartphones, which collectdata from the sensors through wireless connections. The col-lected data provide users with their instant exercise informa-tion such as their pace, GPS position, distance moved, andcalories burned. Moreover, with mobile networks intercon-necting users’ smartphones, the Nike+ apps enable socialinteractions among users. They can share their progress andexercising experience to cheer each other on or create groupsto go after a goal together. By now, Nike+ has become a bigdata platform that collects, stores, and processes data generat-ed from more than 18 million users.

A series of issues in wireless networking, however, shouldbe addressed when collecting and transferring user data inNike+. To provide users with real-time feedback, both senseddata in wireless sensor networks and social interaction data inmobile cellular networks should be synchronized to users’smartphones frequently. Unfortunately, high-frequency datatransmission in wireless networks would result in high energyconsumption. Both wireless sensors and smartphones havelimited battery capacity. Transferring data at too high a fre-quency would cause degradation in battery life. It is nontrivialfor wireless networks to provide an energy-efficient solutionfor frequent data transmissions. In its latest product, Nike+Fuelband, Nike+ adopts Bluetooth 4.0 wireless techniques fordata transmission. By exploiting the novel Bluetooth LE (forlow energy) protocol to synchronize sensed data to smart-phones, Nike+ Fuelband has been made more durable.

Conclusion and Future TrendsSo far we have reviewed the networking architecture and ser-vices for big data applications. We identify the major chal-lenges big data applications bring to networking systems anddiscuss the state-of-the-art research efforts to meet thedemand of big data over networking. With the rapid growth ofbig data applications, building the network system as a high-way for big data transmission will continue to be a hot topic inboth academia and industry. We now identify some notablefuture trends.

Standardization: Bridging the Fragmented WorldThe world of big data remains largely fragmented. Today’s bigdata are generally stored and analyzed within a particularbusiness entity or organization to obtain insights and guidedecision making. There is a great demand to exchange thesebig data for better insight discovery. For example, manufac-turing factories may require feedback data from retailers to

discover user demands and help design new products. Theretailers, in turn, would need product-related data to set prop-er prices for products and recommend them to target con-sumers. Standards are thus necessary to bridge the fragmentedworld. There has been active development on big data storageand exchange. For example, the National Institute of Stan-dards and Technology (NIST) set up a big data working groupon June 19, 2013, aimed at defining the requirements forinteroperability, reusability, and extendability of big data ana-lytic techniques and infrastructures.

From the perspective of networking, the standards arerequired to specify how to transfer big data between differentplatforms. The transferred big data may contain semi-struc-tured or unstructured data, such as pictures, audios, videos,click streams, log files, and the output of sensors that measuregeographic or environmental information. The standardsshould specify how these data should be encoded and trans-ferred in network systems in order to facilitate network quali-ty of service (QoS) management with low latency and highfidelity. Moreover, as novel technologies such as softwaredefined networking (SDN) keep emerging in networking sys-tems, corresponding standards are required to specify howthese technologies can be exploited for efficient big datatransmission. For example, the Open Network Foundation(ONF) has been releasing and managing the OpenFlow stan-dard since 2011, which defines the communications interfacebetween the control and forwarding layers in an SDN archi-tecture.

Privacy and SecurityWith a variety of personal data such as buying preferences,healthcare records, and location-based information being col-lected by big data applications and transferred over networks,the public’s concerns about data privacy and security naturallyarise. While there have been significant studies on protectingdata centers from being attacked, the privacy and securityloopholes when moving crowsourced data to data centersremain to be addressed. There is an urgent demand on tech-nologies that endeavor to enforce privacy and security in datatransmission. Given the huge data volume and number ofsources, this requires a new generation of encryption solutions(e.g., homomorphic encryption).

On the other hand, big data techniques can also be used toaddress the security challenges in networked systems. Networkattacks and intrusions usually generate data traffic of specificpatterns in networks. By analyzing the big data gathered by anetwork monitoring system, those misbehaviors can be identi-fied proactively, thus greatly reducing the potential loss.

Figure 4. Representative big data applications in the Internet and wireless networks: left: Netflix’s “House of Cards”; right: Nike+.

Video streaming

ServingBidding

User

feed

back

sUserpreference

In 2006, Nikereleased theNike+ platform

How can Nike+ use big data?Nike’s way to big data

EcosystemMaterial analysis

PrecisionmarketingProduct

improvement

In 2010, NikeDigital Sportdivision islaunched

In 2012, Nike+Acceleratorprogram islaunched



Empowered by real-time big data analyzing technologies, weexpect that a series of advanced tools for identifying deepsecurity loopholes in large-scale and complex system could bedeveloped in the near future.

AcknowledgmentThe corresponding author is Fangming Liu ([email protected]).The research was supported by a grant from the National BasicResearch Program (973 program) under grant No.2014CB347800.

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BiographiesXIAOMENG YI ([email protected]) is currently a Ph.D. student in theSchool of Computer Science and Technology, Huazhong University of Scienceand Technology, Wuhan, China. His current research interests focus on cloudcomputing, modeling and optimization.

FANGMING LIU [M] ([email protected]) is an associate professor in the Schoolof Computer Science and Technology, Huazhong University of Science andTechnology, and he is named the CHUTIAN Scholar of Hubei Province,China. He is the Youth Scientist of National 973 Basic Research Program Pro-ject on Software-Defined Networking (SDN)-Based Cloud Data Center Net-works, which is one of the largest SDN projects in China. Since 2012, he hasalso been invited as a StarTrack Visiting Young Faculty at Microsoft ResearchAsia (MSRA), Beijing. He received his B.Eng. degree in 2005 from theDepartment of Computer Science and Technology, Tsinghua University, Bei-jing, and his Ph.D. degree in computer science and engineering from theHong Kong University of Science and Technology in 2011. From 2009 to2010, he was a visiting scholar at the Department of Electrical and ComputerEngineering, University of Toronto, Canada. He was the recipient of BestPaper Awards from IEEE GLOBECOM 2011, IEEE IUCC 2012, and IEEECloudCom 2012, respectively. His research interests include cloud computingand data center networking, mobile cloud, green computing and communica-tions, software-defined networking and virtualization technology, large-scaleInternet content distribution, and video streaming systems. He is a member ofACM, as well as a member of the China Computer Federation (CCF) InternetTechnical Committee. He has been a Guest Editor of IEEE Network and IEEESystems Journal, an Associate Editor of Frontiers of Computer Science, andserved on the Technical Program Committees for IEEE INFOCOM 2013–2015,ICNP 2014, ACM Multimedia 2014, and IEEE GLOBECOM 2012–2014. Heserved as the IEEE LANMAN 2014 Publicity Chair and GPC 2014 ProgramChair.

JIANGCHUAN LIU [SM] ([email protected]) received his B.Eng. degree (cum laude)from Tsinghua University, Beijing, China, in 1999, and his Ph.D. degree fromthe Hong Kong University of Science and Technology in 2003, both in com-puter science. He is currently an associate professor in the School of Comput-ing Science, Simon Fraser University, British Columbia, Canada, and was anassistant professor in the Department of Computer Science and Engineering atthe Chinese University of Hong Kong from 2003 to 2004. He is a recipient ofa Microsoft Research Fellowship (2000), Hong Kong Young Scientist Award(2003), and Canada NSERC DAS Award (2009). He is a co-recipient of theACM Multimedia 2012 Best Paper Award, the IEEE GLOBECOM 2011 BestPaper Award, the IEEE Communications Society Best Paper Award on Multi-media Communications in 2009, as well as IEEE IWQoS ’08 and IEEE/ACMIWQoS ’12 Best Student Paper Awards. His research interests are in network-ing and multimedia, in particular, multimedia communications, peer-to-peernetworking, cloud computing, social networking, and wireless sensor/meshnetworking. He has served on the Editorial Boards of IEEE Transactions onMultimedia, IEEE Communications Tutorial and Surveys, IEEE Internet of ThingsJournal, Elsevier Computer Communications, and Wiley Wireless Communica-tions and Mobile Computing. He is a TPC Co-Chair for IEEE/ACM IWQoS’14 in Hong Kong.

HAI JIN [SM] ([email protected]) is a Cheung Kung Scholars Chair Professor ofcomputer science at the Huazhong University of Science and Technology,China. He is now dean of the School of Computer Science and Technology atthe university. He received his Ph.D. degree in computer engineering from theHuazhong University of Science and Technology in 1994. In 1996, he wasawarded a German Academic Exchange Service fellowship to visit the Techni-cal University of Chemnitz in Germany. He was awarded the Excellent YouthAward from the National Science Foundation of China in 2001. He is thechief scientist of ChinaGrid, the largest grid computing project in China, andchief scientist of the National 973 Basic Research Program Project of Virtual-ization Technology of Computing Systems. His research interests include com-puter architecture, virtualization technology, cloud computing and gridcomputing, peer-to-peer computing, network storage, and network security.


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