Improving the benefits of multicast prioritization algorithms

The final publication is available at Springer via http://dx.doi.org/10.1007/s11227-014-1087-zPrioritized atomic multicast consists in delivering messages in total order while ensuring that the priorities of the messages are considered; i.e., messages with higher priorities are delivered first. That service can be used in multiple applications. An example is the usage of prioritization algorithms for reducing the transaction abort rates in applications that use a replicated database system. To this end, transaction messages get priorities according to their probability of violating the existing integrity constraints. This paper evaluates how that abort reduction may be improved varying the message sending rate and the bounds set on the length of the priority reordering queue being used by those multicast algorithms.This work has been partially supported by EU FEDER and Spanish MICINN under research Grants TIN2009-14460-C03-01 and TIN2010-17193.Miedes De Elías, EP.; Muñoz Escoí, FD. (2014). Improving the benefits of multicast prioritization algorithms. Journal of Supercomputing. 68(3):1280-1301. doi:10.1007/s11227-014-1087-zS12801301683Amir Y, Danilov C, Stanton JR (2000) A low latency, loss tolerant architecture and protocol for wide area group communication. In: International Conference on Dependable Systems and Networks (DSN), IEEE-CS, Washington, DC, USA, pp 327–336Chockler G, Keidar I, Vitenberg R (2001) Group communication specifications: a comprehensive study. ACM Comput Surv 33(4):427–469CiA (2001) About CAN in Automation (CiA). http://www.can-cia.org/index.php?id=aboutciaDéfago X, Schiper A, Urbán P (2004) Total order broadcast and multicast algorithms: taxonomy and survey. ACM Comput Surv 36(4):372–421Dolev D, Dwork C, Stockmeyer L (1987) On the minimal synchronism needed for distributed consensus. J ACM 34(1):77–97International Organization for Standardization (ISO) (1993) Road vehicles—interchange of digital information—controller area network (CAN) for high-speed communication. Revised by ISO 11898-1:2003JBoss (2011) The Netty project 3.2 user guide. http://docs.jboss.org/netty/3.2/guide/html/Kaashoek MF, Tanenbaum AS (1996) An evaluation of the Amoeba group communication system. In: International conference on distributed computing system (ICDCS), IEEE-CS, Washington, DC, USA, pp 436–448Miedes E, Muñoz-Escoí FD (2008) Managing priorities in atomic multicast protocols. In: International conference on availability, reliability and security (ARES), Barcelona, Spain, pp 514–519Miedes E, Muñoz-Escoí FD (2010) Dynamic switching of total-order broadcast protocols. In: International conference on parallel and distributed processing techniques and applications (PDPTA), CSREA Press, Las Vegas, Nevada, USA, pp 457–463Miedes E, Muñoz-Escoí FD, Decker H (2008) Reducing transaction abort rates with prioritized atomic multicast protocols. In: International European conference on parallel and distributed computing (Euro-Par), Springer, Las Palmas de Gran Canaria, Spain, Lecture notes in computer science, vol 5168, pp 394–403Mocito J, Rodrigues L (2006) Run-time switching between total order algorithms. In: International European conference on parallel and distributed computing (Euro-Par), Springer, Dresden, Germany, Lecture Notes in Computer Science, vol 4128, pp 582–591Moser LE, Melliar-Smith PM, Agarwal DA, Budhia R, Lingley-Papadopoulos C (1996) Totem: a fault-tolerant multicast group communication system. Commun ACM 39(4):54–63Nakamura A, Takizawa M (1992) Priority-based total and semi-total ordering broadcast protocols. In: International conference on distributed computing systems (ICDCS), Yokohama, Japan, pp 178–185Nakamura A, Takizawa M (1993) Starvation-prevented priority based total ordering broadcast protocol on high-speed single channel network. In: 2nd International symposium on high performance distributed computing (HPDC), pp 281–288Rodrigues L, Veríssimo P, Casimiro A (1995) Priority-based totally ordered multicast. In: Workshop on algorithms and architectures for real-time control (AARTC), Ostend, BelgiumRütti O, Wojciechowski P, Schiper A (2006) Structural and algorithmic issues of dynamic protocol update. In: 20th International parallel and distributed processing symposium (IPDPS), IEEE-CS Press, Rhodes Island, GreeceTindell K, Clark J (1994) Holistic schedulability analysis for distributed hard real-time systems. Microprocess Microprogr 40(2–3):117–134Tully A, Shrivastava SK (1990) Preventing state divergence in replicated distributed programs. In: International symposium on reliable distributed systems (SRDS), Huntsville, Alabama, USA, pp 104–113Wiesmann M, Schiper A (2005) Comparison of database replication techniques based on total order broadcast. IEEE Trans Knowl Data Eng 17(4):551–56

A platform to deploy customized scientific virtual infrastructures on the cloud

This paper presents a software platform to dynamically deploy complex scientific virtual computing infrastructures, on top of Infrastructure as a Service (IaaS) Clouds. The platform orchestrates different services to provision the virtual computing resources. It dynamically installs the appropriate software to satisfy the requirements of a researcher, both on public and on-premise Clouds. The platform provides a web interface to enable the users to easily management of the lifecycle of virtual infrastructures. It enables users to define infrastructures, share them with other users, deploy and relinquish them, add or remove resources dynamically, create and share application recipes, etc. The paper also describes three case studies to deploy complex infrastructures, namely a Hadoop cluster, a single-node to perform NGS sequencing and a gateway for users to access the European Grid Infrastructure (EGI). This platform promotes a better use of on-premise hardware resources of a research center by allocating the computing resources just-in-time to the specific life time of the virtual infrastructures as well as the deployment of the very same infrastructures on a public Cloud.The authors would to thank the Spanish "Ministerio de Economia y Competitividad" for the project "Clusters Virtuales Elasticos y Migrables sobre Infraestructuras Cloud Hibridas" with reference TIN2013-44390-R.Caballer Fernández, M.; Segrelles Quilis, JD.; Moltó, G.; Blanquer Espert, I. (2015). A platform to deploy customized scientific virtual infrastructures on the cloud. Concurrency and Computation: Practice and Experience. 27(16):4318-4329. https://doi.org/10.1002/cpe.3518S431843292716Mell P Grance T The NIST definition of Cloud computing. NIST Special Publication 800-145 (Final) Technical Report 2011 http://csrc.nist.gov/publications/nistpubs/800-145/SP800-145.pdfBuyya, R., Broberg, J., & Goscinski, A. (Eds.). (2011). Cloud Computing. doi:10.1002/9780470940105Sahoo J Mohapatra S Lath R Virtualization: a survey on concepts, taxonomy and associated security issues 2010 Second International Conference on Computer and Network Technology Bangkok, Thailand 2010 222 226OpenStack OpenStack 2013 http://openstack.orgNurmi D Wolski R Grzegorczyk C Obertelli G Soman S Youseff L Zagorodnov D The Eucalyptus open-source Cloud-computing system Proceedings of 9th IEEE International Symposium on Cluster Computing and the Grid Shanghai, China 2009 124 131Amazon Web Services AWS CloudFormation http://aws.amazon.com/cloudformation/Amazon Web Services AWS OpsWorks http://aws.amazon.com/opsworks/Keahey K Freeman T Contextualization: providing one-click virtual clusters Fourth IEEE International Conference on eScience Indianapolis, Indiana, USA 2008 301 308Keahey K Freeman T Architecting a large-scale elastic environment: recontextualization and adaptive Cloud services for scientific computing 2012Marshall P Keahey K Freeman T Elastic site: using Clouds to elastically extend site resources Proceedings of the 2010 IEEE/ACM 10th International Conference on Cluster, Cloud and Grid Computing CCGRID '10 IEEE Computer Society, Washington, DC, USA 2010 43 52Bresnahan J Freeman T LaBissoniere D Keahey K Managing appliance launches in infrastructure Clouds Proceedings of the 2011 TeraGrid Conference: Extreme Digital Discovery TG '11 ACM, New York, NY, USA 2011 12:1 12:7Apache Whirr 2013 from:http://whirr.apache.org/Juve G Deelman E Automating application deployment in infrastructure clouds Proceedings of the 2011 IEEE Third International Conference on Cloud Computing Technology and Science CLOUDCOM '11 IEEE Computer Society, Washington, DC, USA 2011 658 665OASIS Topology and orchestration specification for cloud applications version 1.0 2013 http://docs.oasis-open.org/tosca/TOSCA/v1.0/TOSCA-v1.0.htmlBinz T Breitenbcher U Haupt F Kopp O Leymann F Nowak A Wagner S OpenTOSCA - a runtime for TOSCA-based cloud applications ICSOC, Lecture Notes in Computer Science 8274 Springer 2013 692 695Puppet Labs IT automation software for system administrators 2013 http://www.puppetlabs.com/Opscode Chef 2013 http://www.opscode.com/chef/DeHaan M Ansible 2013 http://ansible.cc/Vogels, W. (2008). Beyond server consolidation. Queue, 6(1), 20. doi:10.1145/1348583.1348590Carrión JV Moltó G De Alfonso C Caballer M Hernández V A generic catalog and repository service for virtual machine images 2nd International ICST Conference on Cloud Computing (CloudComp 2010) Barcelona, Spain 2010 1 15de Alfonso C Caballer M Alvarruiz F Molto G Hernández V Infrastructure deployment over the Cloud 2011 IEEE Third International Conference on Cloud Computing Technology and Science Athens, Greece 2011 517 521Caballer, M., Blanquer, I., Moltó, G., & de Alfonso, C. (2014). Dynamic Management of Virtual Infrastructures. Journal of Grid Computing, 13(1), 53-70. doi:10.1007/s10723-014-9296-5Dean, J., & Ghemawat, S. (2008). MapReduce. Communications of the ACM, 51(1), 107. doi:10.1145/1327452.1327492Shvachko K Kuang H Radia S Chansler R The Hadoop distributed file system 2010 IEEE 26th Symposium on Mass Storage Systems and Technologies (MSST) Incline Village, NV, USA 2010 1 10Altschul, S. F., Gish, W., Miller, W., Myers, E. W., & Lipman, D. J. (1990). Basic local alignment search tool. Journal of Molecular Biology, 215(3), 403-410. doi:10.1016/s0022-2836(05)80360-

Multimodal Sentiment Analysis of Instagram Using Cross-media Bag-of-words Model

Instagram, one of social media sharing services has increasing growth of use and popularity during recent years. Photos or videos shared by Instagram users are challenging to be mined and analyzed for some purposes. One type of studies can be applied to Instagram data is sentiment analysis, a field of study that learn and analyze people opinion, sentiment, and (or) evaluation about something. Sentiment analysis applied to Instagram can be used as analytics tool for some business purposes such as user behavior, market intelligence and user evaluation. This research aimed to analyze sentiment contained on Instagrams post by considering two modalities: images and English text on its caption. The Cross-media Bag-of-Words Model (CBM) was applied for analyzing the sentiment contained on Instagrams post. CBM treated text and image features as a unit of vector representation. These cross-media features then classified using logistic regression to predict sentiment values which categorized into three classes: positive, negative and neutral. Simulation results showed that the combination of unigram text features and 56-length images features achieves the highest accuracy. The accuracy achieved is 87.2%. Keywords : Instagram, sentiment analysis, Cross-media Bag-of-Words Model (CBM), logistic regression, classification Bibliography [1] D. Borth, R. Ji, T. Chen, T. Breuel, and S.-F. Chang, “Large-scale visual sentiment ontology and detectors using adjective noun pairs,” in Proceedings of the 21st ACM International Conference on Multimedia, ser. MM '13. New York, NY, USA: ACM, 2013, pp. 223–232. [2] R.-E. Fan, K.-W. Chang, C.-J. Hsieh, X.-R. Wang, and C.-J. Lin, “Liblinear: A library for large linear classification,” J. Mach. Learn. Res., vol. 9, pp. 1871– 1874, Jun. 2008. [3] E. Ferrara, R. Interdonato, and A. Tagarelli, “Online popularity and topical interests through the lens of instagram,” in Proceedings of the 25th ACM Conference on Hypertext and Social Media, ser. HT '14. New York, NY, USA: ACM, 2014, pp. 24–34. [4] N. Gunawardena, J. Plumb, N. Xiao, and H. Zhang, “Instagram hashtag sentiment analysis,” in University of Utah CS530/CS630 Conference of Machine Learning 2013. Universiti of Utah CS530/CS630 Conference of Machine Learning 2013, 2013. [5] J. Han, Data Mining: Concepts and Techniques. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc., 2005. [6] M. Hu and B. Liu, “Mining and summarizing customer reviews,” inProceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, USA: ACM, 2004, pp. 168–177.[7] Y. Hu, L. Manikonda, and S. Kambhampati, “What we instagram: A first analysis of instagram photo content and user types,” International AAAI Conference on Weblogs and Social Media, 2014. [8] L. S. Huey and R. Yazdanifard, “How instagram can be used as a tool in social networking marketing,” Help College of Art and Technology Malaysia, Tech. Rep., 2014. [9] D. Jurafsky and J. H. Martin, Speech and Language Processing: An introduction to natural language processing, computational linguistics, and speech recognition(2nd Edition). Upper Saddle River, NJ, USA: Prentice-Hall, Inc., 2009. [10] S. G. K and S. Joseph, “Text classification by augmenting bag of words ( bow ) representation with co-occurrence feature,” IOSR Journal of Computer Engineering (IOSR-JCE), vol. 16, pp. 34–38, 1 2014. [11] B. B. Kachru, The Alchemy of English: The Spread, Functions and Models of Non-native Englishes. Champaign: University of Illinois Press, 1990. [12] S. S. Keerthi and C.-J. Lin, “Asymptotic behaviors of support vector machines with gaussian kernel,” Neural Comput., vol. 15, no. 7, pp. 1667–1689, Jul. 2003. [13] M. Koppel and J. Schler, “The importance of neutral examples for learning sentiment,” in In Workshop on the Analysis of Informal and Formal Information Exchange during Negotiations, 2005.[14] A. Kowcika, A. Gupta, K. Sondhi, N. Shivhre, and R. Kumar, “Sentiment analysis for social media,” International Journal of Advanced Research in Computer Science and Software Engineering, vol. 3, no. 7, 7 2013. [15] F.-F. Li and P. Perona, “A bayesian hierarchical model for learning natural scene categories,” in Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02, ser. CVPR '05. Washington, DC, USA: IEEE Computer Society, 2005, pp. 524–531. [16] B. Liu, Sentiment Analysis and Opinion Mining. Morgan and Claypool Publisher, 2012. [17] W.-Y. Ma and K.-J. Chen, “A bottom-up merging algorithm for chinese unknown word extraction,” in Proceedings of the Second SIGHAN Workshop on Chinese Language Processing - Volume 17, ser. SIGHAN '03. Stroudsburg, PA, USA: Association for Computational Linguistics, 2003, pp. 31–38. [18] Z. McCune, “Consumer production in social media networks: A case study of the instagram iphone app,” Ph.D. dissertation, Dr. John Thompson, 2011. [19] W. Medhata, A. Hassanb, and H. Korashyb, “Sentiment analysis algorithms and applications: A survey,” Ain Shams Engineering Journal, 2014. [20] L.-P. Morency, R. Mihalcea, and P. Doshi, “Toward multimodal sentiment analysis: Harvesting opinion from the web,” in International Conference on Multimodal Interface, 2011. [21] A. Pak and P. Paroubek, “Twitter as a corpus for sentiment analysis and opinion mining,” in Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10), N. C. C. Chair), K. Choukri, B. Maegaard, J. Mariani, J. Odijk, S. Piperidis, M. Rosner, and D. Tapias, Eds. Valletta, Malta: European Language Resources Association (ELRA), may 2010.[22] B. Pang and L. Lee, “Opinion mining and sentiment analysis,” Foundation and Trends in Information Retrieval, vol. 2, no. 1-2, p. 4, 2008. [23] B. Pang, L. Lee, and S. Vaithyanathan, “Thumbs up?: Sentiment classification using machine learning techniques,” in Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing - Volume 10, ser. EMNLP '02. Stroudsburg, PA, USA: Association for Computational Linguistics, 2002, pp. 79–86. [24] C.-Y. J. Peng, K. L. Lee, and G. M. Ingersol, “An introduction to logistic regression analysis and reporting,” The Journal of Educational Research, vol. 96, no. 1, September/October 2002. [25] V. V. Piyush Bansal, Romil Bansal, “Towards deep semantic analysis of hashtags,” 37th European Conference on Information Retrieval, 2015. [26] R. Plutchik, Emotion: A Psycho-evolutionary Synthesis. Harper and Row, 1980. [27] S. Poria, A. Hussain, and E. Cambria, “Beyond text based sentiment analysis: Towards multi-modal systems,” University of Stirling, Stirling FK9 4LA, UK, Tech. Rep., 2013. [Online]. Available: http://www.cs.stir.ac.uk/~spo/publication/resources/cogcomp.pdf [28] E. Praseyto, Data Mining Konsep dan Aplikasi Menggunakan Matlab. Yogyakarta: Andi, 2012.[29] A. Qazi, R. G. Raj, M. Tahir, E. Cambria, and K. B. S. Syed, “Enhancing business intelligence by means of suggestive reviews,” The Scientific World Journal, vol. 2014, June 2014. [30] R. Schapire, “Machine learning algorithms for classification,” Princeton University, Tech. Rep. [31] S. Siersdorfer, E. Minack, F. Deng, and J. Hare, “Analyzing and predicting sentiment of images on the social web,” in Proceedings of the International Conference on Multimedia, ser. MM '10. New York, NY, USA: ACM, 2010, pp. 715–718. [32] T. H. Silva, P. O. S. V. de Melo, J. M. Almeida, J. Salles, and A. A. F. Loureiro, “A comparison of foursquare and instagram to the study of city dynamics and urban social behavior,” in Proceedings of the 2Nd ACM SIGKDD International Workshop on Urban Computing, ser. UrbComp '13. New York, NY, USA: ACM, 2013, pp. 4:1–4:8. [33] P. N. Stuart Russell, Artificial Intelligence A Modern Approach, M. Hirsch, Ed. New Jersey: Pearson Education, 2010. [34] M. Taboada, J. Brooke, M. Tofiloski, K. Voll, and M. Stede, “Lexicon-based methods for sentiment analysis,” Comput. Linguist., vol. 37, no. 2, pp. 267– 307, Jun. 2011. [35] C.-F. Tsai, “Bag-of-words representation in image annotation: A review,” ISRN Artificial Intelligence, p. 19, 2012. [36] A. J. Viera and J. M. Garrett, “Understanding interobserver agreement: The kappa statistic,” Family Medicine, vol. 37, no. 5, pp. 360–363, May 2005. [37] M. Wang, D. Cao, L. Li, S. Li, and R. Ji, “Microblog sentiment analysis based on cross-media bag-of-words model,” in Proceedings of International Conference on Internet Multimedia Computing and Service, ser. ICIMCS '14. New York, NY, USA: ACM, 2014, pp. 76:76–76:80. [38] A. Westerski, “Sentiment analysis: Introduction and the state of the art overview,” Universidad Politecnica de Madrid, Spain, Tech. Rep., 2009. [39] F. Yu, L. Cao, R. Feris, J. Smith, and S.-F. Chang, “Designing category-level attributes for discriminative visual recognition,” in IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), Portland, OR, June 2013. [40] L. Yu and H. Liu, “Efficiently handling feature redundancy in high-dimensional data,” in Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ser. KDD '03. New York, NY, USA: ACM, 2003, pp. 685–690. [41] J. Yuan, S. Mcdonough, Q. You, and J. Luo, “Sentribute: Image sentiment analysis from a mid-level perspective,” in Proceedings of the Second International Workshop on Issues of Sentiment Discovery and Opinion Mining, ser. WISDOM '13. New York, NY, USA: ACM, 2013, pp. 10:1– 10:8

4.45 Pflops Astrophysical N-Body Simulation on K computer -- The Gravitational Trillion-Body Problem

As an entry for the 2012 Gordon-Bell performance prize, we report performance results of astrophysical N-body simulations of one trillion particles performed on the full system of K computer. This is the first gravitational trillion-body simulation in the world. We describe the scientific motivation, the numerical algorithm, the parallelization strategy, and the performance analysis. Unlike many previous Gordon-Bell prize winners that used the tree algorithm for astrophysical N-body simulations, we used the hybrid TreePM method, for similar level of accuracy in which the short-range force is calculated by the tree algorithm, and the long-range force is solved by the particle-mesh algorithm. We developed a highly-tuned gravity kernel for short-range forces, and a novel communication algorithm for long-range forces. The average performance on 24576 and 82944 nodes of K computer are 1.53 and 4.45 Pflops, which correspond to 49% and 42% of the peak speed.Comment: 10 pages, 6 figures, Proceedings of Supercomputing 2012 (http://sc12.supercomputing.org/), Gordon Bell Prize Winner. Additional information is http://www.ccs.tsukuba.ac.jp/CCS/eng/gbp201