nach oben

Knowledge and Information Systems

Erschienen in:

01.05.2015 | Regular Paper

CloudRec: a framework for personalized service Recommendation in the Cloud

verfasst von: Qi Yu

Erschienen in: Knowledge and Information Systems | Ausgabe 2/2015

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

The elastic computing power and the pay-as-you-go model of the cloud offer an attractive platform to deploy software as a service applications. The large number of applications expected to heavily take advantage of the cloud will result in an explosive growth of various cloud services. As many cloud services may compete to offer similar functionalities, it is desirable to consider user preferences on the nonfunctional service properties (aka, quality of service, or QoS) when delivering cloud services to the end users. Unfortunately, current approaches primarily rely on the descriptions from the cloud service providers or expert-provided rankings, which are completely orthogonal to the open and distributed nature of the cloud. We present a novel framework (referred to as CloudRec) that exploits a user-centric strategy to achieve personalized QoS assessment of cloud services. CloudRec integrates a novel community-based QoS assessment model with an iterative algorithm to accurately discover a set of homogenous user and service communities from scarce and large-scale QoS data. The communities can serve as a bridge to relate users and services and hence provide an effective means to estimate the QoS of unknown cloud services. The effectiveness of the proposed framework is demonstrated through a rigorous theoretical analysis and an extensive empirical study on real QoS data.

Vorheriger Artikel Learning to suggest questions in social media

Nächster Artikel Efficient clustering-based source code plagiarism detection using PIY

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

We use community and cluster in an exchangeable manner in the rest of the paper.

Strictly speaking, we ignore \(p(u_i)\) here as \(p(z_p^u|u_i)=U_{ip}a_p/p(u_i)\). For a given \(u_i, p(u_i)\) is a constant for all clusters. Thus, we can just choose another diagonal matrix \(A'_U=\text{ diag }(a_1/p(u_i),\ldots ,a_k/p(u_i))\) to absorb the constant.

http://www.planet-lab.org/.

The \(P\) values for the RMSE show a similar result so we skip them to avoid redundancy.

(2012) http://www.cloudcomputingmarket.com/

Abadi DJ (2009) Data management in the cloud: limitations and opportunities. IEEE Data Eng Bull 32(1):3–12

Armbrust M, Fox A, Griffith R, Joseph A, Katz R, Konwinski A, Lee G, Patterson D, Rabkin A, Stoica I, Zaharia M (2009) Above the clouds: a berkeley view of cloud computing. Technical report, 2009. University of California at Berkeley Technical, Report No. UCB/EECS-209-28

Belkin M, Niyogi P (2001) Laplacian eigenmaps and spectral techniques for embedding and clustering. In NIPS ’01, pp 585–591

Breese JS, Heckerman D, Kadie C (1998) Empirical analysis of predictive algorithms for collaborative filtering. In UAI ’98, pp 43–52

Cai D, Wang X, He X (2009) Probabilistic dyadic data analysis with local and global consistency. In: ICML ’09: Proceedings of the 26th annual international conference on machine learning, pp 105–112, New York, NY, USA, ACM

Canny J (2002) Collaborative filtering with privacy via factor analysis. In: SIGIR ’02, pp 238–245

Cao Y, Chen C, Guo F, Jiang D, Lin Y, Ooi BC, Vo HT, Wu S, Xu Q (2011) Es\(^2\): a cloud data storage system for supporting both oltp and olap. In: ICDE

Chen C, Chen G, Jiang D, Ooi BC, Vo HT, Wu S, Xu Q (2010) Providing scalable database services on the cloud. In: WISE, pp 1–19

10.

Chen X, Zheng Z, Liu X, Huang Z, Sun H (2011) Personalized QoS-aware Web service recommendation and visualization. IEEE Trans Serv Comput, (PrePrints)

11.

Chen Y, Wang L, Dong M (2010) Non-negative matrix factorization for semisupervised heterogeneous data coclustering. IEEE Trans Knowl Data Eng 22(10):1459–1474CrossRef

12.

Choudhury P, Sharma M, Vikas K, Pranshu T, Satyanarayana V (2012) Service ranking systems for cloud vendors. Adv Mater Res 433:3949–3953CrossRef

13.

Dean J, Ghemawat S (2004) Mapreduce: simplified data processing on large clusters. In: OSDI’04, pp 10–10

14.

Deerwester SC, Dumais ST, Landauer TK, Furnas GW, Harshman RA (1990) Indexing by latent semantic analysis. J Am Soc Inf Sci 41(6):391–407CrossRef

15.

Dhillon IS (2001) Co-clustering documents and words using bipartite spectral graph partitioning. In: KDD ’01, pp 269–274

16.

Ding C, Li T, Luo D, Peng W (2008) Posterior probabilistic clustering using nmf. In: SIGIR ’08, pp 831–832

17.

Ding C, Li T, Peng W, Park H (2006) Orthogonal nonnegative matrix t-factorizations for clustering. In: KDD ’06, pp 126–135

18.

Ding CHQ, He X (2005) On the equivalence of nonnegative matrix factorization and spectral clustering. In: SDM

19.

Ding CHQ, He X, Zha H, Gu M, Simon HD (2001) A min-max cut algorithm for graph partitioning and data clustering. In: ICDM ’01: Proceedings of the 2001 IEEE international conference on data mining, pp 107–114, Washington, DC, USA. IEEE Computer Society

20.

Dong X, Halevy AY, Madhavan J, Nemes E, Zhang J (2004) Similarity search for web services. In: VLDB conference

21.

Goldberg D, Nichols D, Oki BM, Terry D (1992) Using collaborative filtering to weave an information tapestry. Commun ACM 35(12):61–70CrossRef

22.

Gu Q, Zhou J (2009) Co-clustering on manifolds. In: KDD ’09, pp 359–368

23.

Gu Q, Zhou J, Ding C (2010) Collaborative filtering: weighted nonnegative matrix factorization incorporating user and item graphs. In: SDM, pp 199–210

24.

Han S-M, Hassan MM, Yoon C-W, Huh E-N (2009) Efficient service recommendation system for cloud computing market. In: Proceedings of the 2nd international conference on interaction sciences: information technology, culture and human, ICIS ’09, pp 839–845, New York, NY, USA. ACM

25.

Herlocker JL, Konstan JA, Borchers A, Riedl J (1999) An algorithmic framework for performing collaborative filtering. In: SIGIR ’99, pp 230–237

26.

Hofmann T (2004) Latent semantic models for collaborative filtering. ACM Trans Inf Syst 22(1):89–115CrossRef

27.

Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791CrossRef

28.

Lee DD, Seung HS (2000) Algorithms for non-negative matrix factorization. In: NIPS, pp 556–562

29.

Li T, Ding CHQ (2006) The relationships among various nonnegative matrix factorization methods for clustering. In: ICDM, pp 362–371

30.

Liu C, Yang H-C, Fan J, He L-W, Wang Y-M (2010) Distributed nonnegative matrix factorization for web-scale dyadic data analysis on mapreduce. In: Proceedings of the 19th international conference on world wide web, WWW ’10, pp 681–690, New York, NY, USA. ACM

31.

Long B, Zhang ZM, Yu PS (2005) Co-clustering by block value decomposition. In: KDD ’05: Proceedings of the 11th ACM SIGKDD international conference on Knowledge discovery in data mining, pp 635–640, New York, NY, USA. ACM

32.

OWL-S (2004) http://www.daml.org/services/owl-s/

33.

Rehman ZU, Hussain OK, Hussain FK (2012) Iaas cloud selection using MCDM methods. In: Proceedings of the 2012 IEEE 9th international conference on e-Business engineering, ICEBE ’12, pp 246–251, Washington, DC, USA. IEEE Computer Society

34.

Roweis ST, Saul LK (2000) Nonlinear dimensionality reduction by locally linear embedding. Science 290:2323–2326CrossRef

35.

Shao L, Zhang J, Wei Y, Zhao J, Xie B, Mei H (2007) Personalized QoS prediction for web services via collaborative filtering. Web Services, IEEE International Conference on 439–446

36.

Shi J, Malik J (2000) Normalized cuts and image segmentation. IEEE Trans Pattern Anal Mach Intell 22(8):888–905CrossRef

37.

ur Rehman Z, Hussain O, Hussain F (2013) Multi-criteria IaaS service selection based on QoS history. In: Advanced information networking and applications (AINA), 2013 IEEE 27th international conference on, pp 1129–1135

38.

Vo HT, Chen C, Ooi BC (2010) Towards elastic transactional cloud storage with range query support. PVLDB 3(1):506–517

39.

Yu T, Zhang Y, Lin K-J (2007) Efficient algorithms for web services selection with end-to-end QoS constraints. ACM Trans Web 1(1):6CrossRef

40.

Zeng L, Benatallah B, Dumas M, Kalagnanam J, Sheng Q (2003) Quality-driven web service composition, In: WWW

41.

Zhang S, Wang W, Ford J, Makedon F (2006) Learning from incomplete ratings using non-negative matrix factorization. In: SDM’06: Proceedings of the 6th SIAM conference on data mining (SDM), pp 549–553

42.

Zhang Y, Koren J (2007) Efficient bayesian hierarchical user modeling for recommendation system. In: SIGIR ’07: Proceedings of the 30th annual international ACM SIGIR conference on research and development in information retrieval, pp 47–54, New York, NY, USA. ACM

43.

Zhang Y, Zheng Z, Lyu MR (2011) Exploring latent features for memory-based QoS prediction in cloud computing. In: SRDS, pp 1–10

44.

Zheng Z, Ma H, Lyu MR, King I (2009) Wsrec: A collaborative filtering based web service recommender system. In: ICWS, pp 437–444

45.

Zheng Z, Zhang Y, Lyu MR (2010) Cloudrank: a QoS-driven component ranking framework for cloud computing. In: SRDS, pp 184–193

46.

Zheng Z, Zhang Y, Lyu MR (2010) Distributed QoS evaluation for real-world Web services. In: Proceedings of 8th international conference on web services (ICWS’10), pp 83–90

Titel: CloudRec: a framework for personalized service Recommendation in the Cloud
verfasst von: Qi Yu
Publikationsdatum: 01.05.2015
Verlag: Springer London
Erschienen in: Knowledge and Information Systems / Ausgabe 2/2015
Print ISSN: 0219-1377
Elektronische ISSN: 0219-3116
DOI: https://doi.org/10.1007/s10115-013-0723-x

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Weitere Artikel der Ausgabe 2/2015

STenSr: Spatio-temporal tensor streams for anomaly detection and pattern discovery

A model for revocation forecasting in public-key infrastructures

Learning to suggest questions in social media

Finding top--cliques for keyword search from graphs in polynomial delay

Fast and scalable support vector clustering for large-scale data analysis

Answering ordered tree pattern queries over fuzzy XML data

Premium Partner