Review and Analysis of Cloud Computing Quality of Experience

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1 Rviw and Analysis of Cloud Computing Quality of Exprinc Fash Safdari and Victor Chang School of Computing, Crativ Tchnologis and Enginring, Lds Mtropolitan Univrsity, Hadinly, Lds LS6 3QR, U.K. {F.Safdari, Abstract. Cloud computing is gaining growing intrst from industry, organizations and th rsarch community. Th tchnology promiss many advantags in trms of cost saving by allowing organizations and usrs to rmotly gain accss to a hug pool of data storag and procssing powr. Howvr, migrating srvics to cloud computing introduc nw challngs in prformanc and srvic quality. Quality of srvic (QoS) is and has bn usd as a mans of monitoring cloud srvic prformanc. Howvr, QoS is basd on ntwork paramtrs which do not ncssarily rflct usrs quality of xprinc. This papr discusss cloud computing quality of xprinc as prcivd by th prson using th cloud-basd applications and srvics. W also rviw a slctd currnt contribution to th quality of xprinc (QoE). W hav xprt rviws undrtakn to idntify six ky prformanc indicators (PKIs). Basd on ths six KPIs, w snt our survys, collctd fdback and analyz data to confirm that QoE masurmnt can mt organizational goals, usr satisfaction and stakholdrs rquirmnts. 1 Introduction Cloud computing is a nw phnomnon in information systm and communication tchnology and has gaind a lot of intrsts from industry, organizations and th rsarch community. Moving srvics to cloud man that applications that usd to run on usrs PCs will rsid in data cntrs distributd across diffrnt continnts. Cloud computing promiss conomic bnfits. Howvr, migrating srvics to cloud computing introduc nw challngs in srvic prformanc, quality and rliability; traditionally srvic lvl agrmnts ar usd as a mans of guaranting th dlivry of accptabl srvic prformanc lvl. Existing QoS mtrics and srvic lvl agrmnts (SLAs) offrd by cloud srvic providrs do not includ anything about th prformanc of srvic from usr point of viw and ar not adquat to mt usrs' quality of srvic xpctations [16]. Organizations ar unlikly to adopt cloud computing if usrs xprinc is not as good as it was bfor moving to cloud or prformanc dos not mt usrs' prcivd quality of xprinc and prformanc xpctations [8]. Howvr, du to subjctiv natur of usrs quality of xprinc, masuring, capturing and quantifying QoE rmains an opn rsarch qustion. In this papr, w rviw slctd currnt contributions to QoE. Th rmaindr of this papr is struc-

2 84 turd as follows: Sction 2 dscribs QoE. Sction 3 provids an ovrviw of th rlatd work on QoE. Sction 4 prsnts th rsults and discussions from th xprt rviw and th survy. Sction 5 concluds this papr. 2 Quality of Exprinc (QoE) Many dfinitions for QoE hav bn proposd in litratur. In [15] QoE is rfrrd to as th basic charactr or natur of dirct prsonal participation or obsrvation. Dscriptions in [6] stats QoE as a concpt comprising all lmnts of a subscribr's prcption of th ntwork and prformanc rlativ to xpctations. Synthsis in [17] givs th following dscription for QoE: QoE is a multi-dimnsional construct of prcptions and bhaviors of a usr, which rprsnts his/hr motional, cognitiv, and bhavioural rsponss, both subjctiv and objctiv, whil using a systm.. ITU-T [9] dfins QoE as ovrall accptability of an application or srvic, as prcivd subjctivly by th nd usr. Thr is no a univrsally accptabl dfinition of QoE [15], QoE is outlind in various litraturs as a subjctiv masur. To modl and masur QoE appropriat QoE mtrics nd to b idntifid. Whil ntwork prformanc paramtrs and QoS mtrics ar wll-dfind, in contrast QoE mtrics ar not wll undrstood and rmain an opn rsarch qustion. A larg volum of publications has bn ddicatd to th study of QoE. In sction 3, w rviw a slctd list and summariz thir findings and proposals. 3 Summary of Existing QoE Studis Casa t al., [2] studid QoE in rmot virtual dsktop srvics. Authors outlind a thr layr QoE valuation framwork, consisting of usr layr, application layr, and ntwork layr. Ntwork layr dals with QoS paramtrs such as bandwidth, application layr includs rspons tim, scrn comprssion rat, and colour paramtrs. Usr layr is basd on usr fdback using Man Opinion scor (MOS). Thy prformd subjctiv xprimnt using 52 participants. Thir xprimnt was focusd on usrs ovrall xprinc and round trip tim. Thir main finding is th corrlation btwn QoE and application QoS rquirmnt. Intractiv applications ar mor dlay snsitiv, high RTT dgrads usr quality of xprinc. This indicats a dirct corrlation btwn ntwork bandwidth and QoE, low bandwidth has ngativ impact on usr xprinc. Chn t al., [5] argu that obtaining usr fdback aftr thy usd an application is not th most appropriat mchanism. Thy proposd a QoE captur framwork calld OnClick whr usrs click a particular button indicating thy ar not satisfid with th quality of an application. Thy assrt that dissatisfaction rfrs to all QoE dimnsions and QoE is application spcific as applications hav diffrnt QoS rquirmnts. Authors in [5] invstigatd QoE a group of 52 usrs using Th Box, a Dropbox-lik application in a controlld lab nvironmnt. Thy obsrvd a corrlation btwn poor usr QoE and low ntwork bandwidth. [8] stat that QoE has th potntial to bcom th nw guiding standard for cloud computing quality managmnt.

3 85 Authors argud that undrstanding and managing QoE of cloud srvics involvs a multidisciplinary viw including ntwork, usr, and businss aspcts. Tchnical factors that influnc cloud computing QoE vary from application to application. QoEbasd classification of application basd on lvl of intractivity, usag domain, and srvic complxity is prsntd. A QoE managmnt framwork was proposd [12], whr fiv stps ar outlind: Srvic classification provids srvic contnts; Main quality paramtrs xtraction slcts ky quality mtrics; usr fdback collction, QoE analysis prdicts rasons affcting srvic quality and Prformanc upgrad. Th framwork uss simpl raltim onclick [5] fdback mchanism. Thy prformd an xprimnts using a 100- scond vido straming, only two ntwork paramtrs, dlay pr packt, and packt loss wr masurd. Thir findings indicat incras in packt dlay and packt loss rsult in poor QoE. On th othr hand, it is not asy to masur QoE in a unifid form bcaus many factors affcting QoE [14]. Authors affirmd that taking into account all factors could rsult in a complx QoE mtrics; simpl QoE mtrics ar mor practical. An in-srvic fdback QoE framwork is introducd, which runs ovr a multiagnt nvironmnt, and abstract functions of diffrnt agnts wr discussd. Kist and Brodi [15] did a cas study using two groups of svn and lvn studnts to idntifying th ffct of QoS on QoE. Thir findings indicat that flow (lvl of intrruption to concntrat on a task), achivability (sns of complting th task), and xtra tim ndd to complt th task wr dirctly affctd by QoS. Thir findings ar rathr gnric as thr is no mapping of QoE and diffrnt QoS mtrics. Statmnts in [16] assrt that QoE will play a major rol in th succss or failur of cloud srvic. Th authors argu that undrstanding QoE rquirs a multi-disciplinary viw of ntwork, usr and businss aspcts. A concptual viw of cloud srvics QoE landscap is proposd, which consists of thr main branchs: usr profil, application, and ntwork QoS. In tabl 1 w summarizd a list of factors idntifid by diffrnt authors that could hav an impact on QoE. R f r n c Usr dmographics Tabl 1. Summary of authors viws on factors affcting QoE. Usr prsonality Usr intrfac Dvic usability Application charactristics Volum of data Rspons tim Dlay Jittr Bandwidth Packt loss CPU Consumptions [2] [3] [6] [8] [9] [13] [15] [16] [17] Srvic rliability

4 86 4 Rsults from th Exprt Rviw and Survy QoE is an ara that rquirs th support of rsults from survys and quantitativ analysis dmonstratd by Chang [3], who idntifis six ky prformanc indicators (KPI) for IT succss and xplains how ths six KPIs can b usd in masuring th succss of IT projct dlivry. Th dmonstratd xampls by [3] ar rlvant to QoE, in which xprt rviws hav bn involvd. Basd on th xtnsion of th work, w hav prsntd th summary of th importanc of six KPIs as follows. Rlvanc for usability for Cloud QoE adoption is important with th support of ral us cass. Achiving good prformanc is an ssntial for Cloud QoE adoption. Scurity concrn is a main rason for som organizations not to adopt Cloud Computing which rsarchrs dscrib challngs and issus to b improvd. Data accuracy is important to comput accurat rsults so that organizations hav a highr trust and confidnc in Cloud QoE adoption. Srvic and data portability ar highly rlvant to Cloud QoE adoption that rsarchrs dmonstrat thir usfulnss for QoE. Scalability is a cor charactristic for Cloud and th ability to scal up and down rsourcs promptly for diffrnt dmands is ssntial for Cloud QoE adoption. Ths six KPIs ar ssntial for th dvlopmnt of QoE adoption and masurmnt. Whil fdback from xprts ar important, th fdback from usrs (in this cas, studnts) ar ssntial for th dvlopmnt of QoE adoption and masurmnt. Qustions ar basd on ths six PKIs. Each participant was askd thir opinions and ratd th importanc of ths six KPIs. In this cas, w idntifid that studnts ar likly to us or gt involvd in QoE at som stag, which xplains why studnts tak part in this study. As a rsult, w snt out survys and collctd fdback from studnts about thir fdback about Cloud QoE adoption. Analysis of varianc (ANOVA) provids statistical tst of whthr mans of svral groups ar qual and can gnraliz t-tst or F-tst for two or mor groups [4]. ANOVA can b usd whn ths two cohorts hav clos mans and ach group has two sts of data. Tabl 6 shows th ANOVA analysis with F-tst for cohort group on, whr grp_with_cloud is th shortr vrsion of group_with_cloud. Cohort group on has F-valu = 2.78 and Prob > F is (th smallr, th bttr). Th R- squard valu is (th highr, th bttr) and th root man squar rror (MSE) is (th lowr, th bttr). Ths ky rsults confirm that th statistical analysis agrs that th us of Cloud QoE adoption hlp studnts in thir motivation for larning. Statistical rsults confirm a good xtnt of accuracy in analysis by having high R-squard valus; low root man squar valus and high F-tst valus. Ths rsults confirm that w can achiv th stakholdrs rquirmnts in th first stag of QoE invstigation. Th p-valu is blow 0.1 and is considrd a good rsult. To nsur smooth dlivry, p-valu should b as small as possibl. In this cas, variations in p-valu hav bn xplaind by th variations bfor and aftr using Cloud QoE adoption. Basd on dirct fdback from som participants, Cloud QoE adoption can nhanc larning and undrstanding of th subjct involvd.

5 87 Tabl 2. ANOVA Analysis for th Studnt Cohort Group. Numbr of obs = 30 R-squard = Root MSE = Adj R-squard = Sourc Partial SS df MS F Prob > F Modl grp_with_cloud Rsidual Total Conclusion and Futur Work This rsarch has focusd on th litratur on th currnt stat of QoE of cloud computing. Th assssmnt of cloud computing QoE cannot b basd only on datacntrs prformanc fficincy, SLA, and ntwork QoS paramtrs du to th fact many diffrnt intrmdiat ntworks and providrs ar involvd in providing usrs accss to cloud srvics. Many factors affct QoE, having a framwork that captur all individual factors could rsult in a complx QoE managmnt modl. It is mor practical and fficint to captur and modl usrs ovrall accptability of quality of srvic in ral-tim. Individual applications and srvics hav diffrnt QoS rquirmnts. It is important to monitor ntwork paramtrs and QoE for individual application and idntify paramtrs and factors that influnc QoE for a givn application. This provids a fundamntal start towards dvloping a modl undrstanding corrlation btwn QoE and ntwork prformanc mtrics. Apart from th consolidation of litratur, w prsnt that th us of xprt rviws and th survy analysis hlp shak up th rsarch for QoE adoption. W confirm that th six KPIs ssntial and statistical rsults for th proposd QoE framwork to nsur that stakholdrs, usrs and studnts ar wll ngagd in th dvlopmnt work. Thir fdback will b usful for a succssful dlivry of QoE implmntation to nsur that adoption and masurmnt can mt th organizational goals, usr satisfaction and stakholdrs rquirmnts. W also idntify mor usr groups that rquir QoE, collct thir fdback and analyz our collctd data. Rfrncs 1. Armbrust, M. Fox, A. Griffith, R. Josph, A. D. Katz, R. H. Konwinski, A. L, G. Pattrson, D. Rabkin, A. Stoica, I. Zaharia, M. Abov th Clouds: A Brkly Viw of Cloud Computing, Tchnical Rport, No. UCB/EECS , UC Brkly, (2009). 2. Casas, P. Sufrt, M. Eggr, S and Schatz, R. Quality of Exprinc in Rmot Virtual Dsktop Srvics, IFIP/IEEE IM2013 Workshop: 1st Intrnational Workshop on Quality of Exprinc Cntric Managmnt (2013).

6 88 3. Chang, V., Businss Intllignc in th Cloud, Futur Gnration Computr Systms, DOI: (2014). 4. Chang, V. and Wills, G., A Univrsity of Grnwich Cas Study of Cloud Computing Education as a Srvic, E-Logistics and E-Supply Chain Managmnt: Applications for Evolving Businss, IGI Global, April, (2013). 5. Chn, K. Tu, C, and Xiao, W. OnClick: A Framwork for Masuring Ntwork Quality of Exprinc, IEEE, (2009). 6. Empirix. Assuring QoE on Nxt Gnration Ntworks, Empirix whit papr, (2001). 7. Fischr, H. Casas, P. Sutt, S and Schatz, R. First Look at Quality of Exprinc in Prsonal Cloud Storag Srvics, IEEE Intrnational Confrnc on 1stt Intrnational Workshop on Mobil Cloud ntworking and Srvics, (2013). 8. Hobfld, T. Schatz, R. Varla, M. and Timmrr, C. Challngs of QoE Managmnt for Cloud Applications, IEEE Communications Magazin, (2012). 9. ITU-T. Vocabulary and ffcts of transmission paramtr on customr opinion of transmission quality, (2006), 10. Laghari, K. Crspi, N. Molina, B. QoE awar Srvic Dlivry in Distributd Environmnt, IEE Computr socity (2011). 11. Jarschl, M. Schlossr, D. Schuring, S and Hoßfld, T. An Evaluation of QoE in Cloud Gaming Basd on Subjctiv Tsts', IEEE computr socity., (2011). 12. Ji, Z and Kim, H, 'Usr Fdback Orintd Quality of Exprinc Managmnt Framwork', China communications., (2013). 13. Khalil, R. Crspi, N. Molina, B. and Palau,C. QoE awar Srvic Dlivry in Distributd Environmnt, 2011 workshop of Intrnational Confrnc on Advancd Infromation Ntworking and Applications, (2011). 14. Kim. H. L, K. and Zhang, J. 'In-Srvic Fdback QoE Framwork', 2010 Third Intrnational Confrnc on Communication Thory, Rliability, and Quality of Srvic, (2010). 15. Kilkki, K. Quality of Exprinc in Communications Ecosystm, Journal of Univrsal Computr Scinc, vol. 14, no. 5 (2008). 16. Varla, M. and Sankala, J, Undrstandin Quality Issus in th Cloud, IEEE compting, (2010). 17. Wu, w. Arfin, A. Rivas, R. Nahrstdt, K. Shppard, Z. Yang, Z.: Quality of Exprinc in Distributd Intractiv Multimdia Environmnts: Toward a Thortical Framwork, Procding of ACM intrnamtional confrnc on Multimdia, (2009).

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