Data Storage for Social Networks: A Socially Aware Approach

52 Pages·2012·0.696 MB·English

by  Duc A. Tran (auth.)

SpringerBriefs in Optimization
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Duc A. Tran
Data Storage for Social
Networks
A Socially Aware Approach
123
DucA.Tran
DepartmentofComputerScience
UniversityofMassachusetts
100MorrisseyBlvd
Boston,MA
USA
ISSN2190-8354 ISSN2191-575X(electronic)
ISBN978-1-4614-4635-4 ISBN978-1-4614-4636-1(eBook)
DOI10.1007/978-1-4614-4636-1
SpringerNewYorkHeidelbergDordrechtLondon
LibraryofCongressControlNumber:2012943345
MathematicsSubjectClassification(2010):91D30,00-02,97A40
©DucA.Tran2012
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Preface
Online social networkinghas become one of the most importantformsof today’s
communication.Whilesociallyinterestingfeaturescanmakeasystemattractive,its
competitive edge will diminish if it is not able to keep pace with increasing user
activities.Deployingmoreserversisanintuitivewaytomakethesystemscale,but
forthebestperformanceoneneedstodeterminewherebestacrosstheserverstoput
thedata,whetherreplicationisneeded,and,ifso,how.
The purpose of this brief book is to introduce a novel approach to social data
storage, which optimizes the distribution and replication of the data based on not
only how often they are accessed but also their social relationships. The book is
structuredasfollows.Chapter1providesareviewofselectedkeytechnologiesthat
form the data storage infrastructure for many of today’s online social networks.
Chapter2introducesoptimizationproblemsforsociallyawaredatapartitioningand
replication,twoofthemostimportantdesignissuesofdatastorage.Chapters3and4
presentrecentsolutionstotheseproblems,respectively.Thebookisconcludedwith
anepilogue.
Although related literature is reviewed, this book is not intended to be a
comprehensive reference, but more as a resource advocating an unexplored area
ofresearchanddevelopment.Advancedstudents,softwareengineers,andcomputer
scientistswhoworkintheareasofnetworkinganddistributeddatasystemsarethe
bestaudienceforthebook.
The author would like to thank his students, Khanh Nguyen and Ting Zhang,
for help with the review of the existing technologies in Chap.1 (Khanh) and the
numericalresultsdiscussedinChap.3(Ting)andChap.4(Khanh)ofthisbook.
Boston,Massachusetts,USA DucA.Tran
v

Contents
1 Introduction ................................................................... 1
1.1 Amazon’sDynamo....................................................... 3
1.1.1 Partitioning....................................................... 3
1.1.2 Replication....................................................... 4
1.1.3 Versioning........................................................ 4
1.1.4 ReadandWrite .................................................. 5
1.1.5 HandlingDynamics.............................................. 5
1.2 Google’sBigTable ....................................................... 6
1.2.1 TableFormat..................................................... 6
1.2.2 DistributedStorage .............................................. 7
1.3 ApacheCassandra........................................................ 8
1.3.1 DataModel....................................................... 8
1.3.2 Partitioning....................................................... 11
1.3.3 Replication....................................................... 11
1.3.4 HandlingDynamics.............................................. 12
1.3.5 Querying ......................................................... 12
2 SocialLocalityinDataStorage.............................................. 13
2.1 Perfectvs.ImperfectSocialLocality ................................... 14
2.2 AssumptionsandNotations ............................................. 15
2.3 OptimizationObjectives................................................. 17
2.3.1 ReadLoad........................................................ 17
2.3.2 WriteLoad....................................................... 19
2.3.3 StorageLoad..................................................... 19
2.3.4 LoadBalancing.................................................. 20
2.4 Multi-ObjectiveOptimization........................................... 21
2.4.1 ThePartitioningProblem........................................ 21
2.4.2 TheReplicationProblem........................................ 23
2.4.3 OtherProblems .................................................. 25
vii
viii Contents
3 S-PUT .......................................................................... 27
3.1 Approach................................................................. 28
3.2 Algorithm................................................................. 29
3.2.1 InitialPartitioning ............................................... 29
3.2.2 FinalPartitioning ................................................ 30
3.3 NumericalResults........................................................ 31
3.4 Notes...................................................................... 32
4 S-CLONE...................................................................... 35
4.1 Approach................................................................. 36
4.2 Algorithm................................................................. 37
4.3 NumericalResults........................................................ 38
4.4 Notes...................................................................... 40
5 Epilogue........................................................................ 43
References.......................................................................... 45
Chapter 1
Introduction
Evidenced by the success of Facebook with more than 800 million users, Twitter
with more than 200 million users, and other sites alike, online social networks
(OSNs) have become ubiquitous, offering novel ways for people to access infor-
mation and communicate with each other. Nielsen published stats in June 2010
[28] showing that three of the world’s top ten popular brands online are social-
mediarelatedand,forthefirsttimeever,socialnetworkorblogsitesarevisitedby
75%of globalconsumerswho goonline.Amongmobile users, socialnetworking
wouldsurpassvoiceas themostpopularformofmobilecommunicationby2015,
accordingtoAirwideSolutions[2].
Theincreasingpopularityofsocialnetworkingisundeniable,andsoscalability
is an important issue for any OSN that wants to serve a large number of users.
There are two main ways to address scalability: vertical scaling and horizontal
scaling. While vertical scaling scales “up” the system by adding more hardware
resourcestotheexistingservers,horizontalscalingscales“out”thesysteminstead,
by adding commodity servers and partitioning the workload across these servers.
Vertical scaling is simple to manage, but horizontalscaling is more cost-effective
andavoidsthesingle-point-of-failureandbottleneckproblems.Thelatterhasbeen
a de facto standard when it comes to managing data at massive scale for many
OSNstoday.
Inadistributedstoragesystem,wherethedataispartitionedacrossanumberof
servers, the data can also be replicated to provide a high degree of availability in
caseoffailures.Whiledatapartitioningandreplicationisawell-knownproblemin
theliteratureofdistributeddatabasesystems[1,14,20,33,35–37],OSNsrepresent
a novel class of data systems. In OSNs, a data read for a user often requires
fetchingthedataofitsneighborsinthesocialgraph(e.g.,friends’statusmessagesin
Facebookorconnections’updatesinLinkedIn).Werefertothispropertyassocial
locality. Recent works [9,32] have shown that distributed queries of small data
records reduce performance compared to local queries. Therefore, social locality
should be taken into account when designing a distributed storage for OSNs.
D.A.Tran,DataStorageforSocialNetworks:ASociallyAwareApproach,SpringerBriefs 1
inOptimization,DOI10.1007/978-1-4614-4636-1 1,©DucA.Tran2012
2 1 Introduction
More specifically, data of the users who are socially connected should be stored
on serverswithin shortreachfrom each other. An idealstorage scheme shouldbe
sociallyaware.
However, the most prominentdistributed storage scheme for OSNs, Cassandra
[21], isnotsociallyaware.OriginallydeployedforFacebooktoenhanceitsInbox
Search feature and now an Apache project, Cassandra has been used by most
popular OSNs including Facebook, Twitter, Digg, and Reddit. While there exist
well-knowndistributedfileandrelationaldatabasesystemssuchasFicus[33],Coda
[36], GFS [14], Farsite [1], and Bayou [37], these systems do notscale with high
read/write rates which is the case for OSNs. Cassandra’s purpose is to be able to
run on top of an infrastructureof manycommoditystorage hosts, possibly spread
across different data centers, with high write throughput without sacrificing read
efficiency.Cassandra isa key-valuestoreresemblinga combinationofa BigTable
datamodel[8]runningonanAmazon’sDynamo-likeinfrastructure[11].Thedata
partitioningschemeunderlyingbothCassandraandDynamoisbasedonconsistent
hashing [17], using an order-preserving DHT. Data is hashed to random servers,
thusbreakingtheirsociallocality.
Aimed to improve system performance and scalability by enforcing social
localityinthedatastorage,sociallyawaredatapartitioningandreplicationschemes
have been proposed. SPAR [32] is one such scheme that preserves social locality
perfectly by requiring every two neighbor users to have their data colocated on
the same servers. Since this is impossible if each user has only one copy of its
data, replicas are introducedand placed appropriately in the servers such that the
number of replicas needed to ensure perfect social locality is minimum. Another
scheme is SCHISM [9], which can partition and replicate user data of a social
graphefficientlybytakingintoaccounttransactionworkloadsuchashowoftentwo
usersareinvolvedinthesametransaction.Inthisbook,weintroducetwosocially
awaretechniquesfordatapartitioningandreplication,S-PUTandS-CLONE,which
our research group has recently devised. S-PUT is specifically designed for data
partitioning, whereas S-CLONE is for data replication that assumes an existing
datapartitionacrosstheservers;thisunderlyingdatapartitioncanbeanyarbitrary
partition,e.g.,aresultofrunningCassandraorS-PUT.UnlikeSPARandSCHISM,
S-CLONE attempts to maximize social locality under a fixed space budget for
replication.S-PUTandS-CLONEcanbedeployedseparatelyorworktogetherina
distributedstoragesystem.
While later chapters of the book are focused on the issue of social locality,
formulation of socially aware data partitioning and replication as optimization
problems, and discussion of S-PUT and S-CLONE, this chapter is devoted to a
briefreviewof Dynamo,BigTable,and Cassandra,three keytechniquesthatform
thedatastorageinfrastructureformostOSNstoday.Thereviewisasummaryofthe
detailsaboutthesetechniquesasdescribedintheirsourcepublications.