Asian Journal of Information Technology

Year: 2016
Volume: 15
Issue: 11
Page No. 1691 - 1705

An Enhanced Query Optimization Approach for Cloud Data Management

Authors : Eman A. Maghawry, Rasha M. Ismail, Nagwa L. Badr and M.F. Tolba

References

Ahmad, M., A. Aboulnaga, S. Babu and K. Munagala, 2011. Interaction-aware scheduling of report-generation workloads. VLDB J. Int. J. Very Large Data Bases, 20: 589-615.
CrossRef  |  Direct Link  |  

Ahmad, M., S. Duan, A. Aboulnaga and S. Babu, 2011. Predicting completion times of batch query workloads using interaction-aware models and simulation. Proceedings of the 14th International Conference on Extending Database Technology, March 21-24, 2011, ACM, Uppsala, Sweden, ISBN: 978-1-4503-0528-0, pp: 449-460.

Akdere, M., U. Cetintemel, M. Riondato, E. Upfal and S.B. Zdonik, 2012. Learning-based query performance modeling and prediction. Proceedings of the 2012 IEEE 28th International Conference on Data Engineering (ICDE), April 1-5, 2012, IEEE, Washington, DC, USA., ISBN: 978-1-4673-0042-1, pp: 390-401.

Avnur, R. and J.M. Hellerstein, 2000. Eddies: Continuously adaptive query processing. Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, May 15-18, 2000, ACM, Dallas, Taxas, ISBN: 1-58113-217-4, pp: 261-272.

Brandic, I. and S. Dustdar, 2011. Grid vs Cloud-A technology comparison. Inf. Technol. Methods Appl. Comput. Sci. Inf. Technol., 53: 173-179.
CrossRef  |  Direct Link  |  

Chen, G., Y.G. Wu, J. Liu and G.W.M. Yang, 2011. Optimization of sub-query processing in distributed data integration systems. J. Network Comput. Appl., 34: 1035-1042.
CrossRef  |  

Chi, Y., H. Hacigumuş, W.P. Hsiung and J.F. Naughton, 2013. Distribution-based query scheduling. Proc. VLDB. Endowment, 6: 673-684.
CrossRef  |  Direct Link  |  

Chi, Y., H.J. Moon, H. Hacigumuş and J. Tatemura, 2011. SLA-tree: A framework for efficiently supporting SLA-based decisions in cloud computing. Proceedings of the 14th International Conference on Extending Database Technology, March 21-24, 2011, ACM, Uppsala, Sweden, ISBN: 978-1-4503-0528-0, pp: 129-140.

Duggan, J., O. Papaemmanouil, U. Cetintemel and E. Upfal, 2014. Contender: A resource modeling approach for concurrent query performance prediction. oceedings of 17th International Conference on Extending Database Technology, March 24-28, 2014, Athens, Greece, ISBN: 978-3-89318065-3, pp: 109-120.

Duggan, J., U. Cetintemel, O. Papaemmanouil and E. Upfal, 2011. Performance prediction for concurrent database workloads. Proceedings of the 2011 ACM SIGMOD International Conference on Management of Data, June 12-16, 2011, ACM, Athens, Greece, ISBN: 978-1-4503-0661-4, pp: 337-348.

Ganapathi, A., H. Kuno, U. Dayal, J.L. Wiener and A. Fox et al., 2009. Predicting multiple metrics for queries: Better decisions enabled by machine learning. Proceedings of the IEEE 25th International Conference on Data Engineering ICDE'09, March 29-April 2, 2009, IEEE, Shanghai, China, ISBN: 978-1-4244-3422-0, pp: 592-603.

Gupta, C., A. Mehta and U. Dayal, 2008. PQR: Predicting query execution times for autonomous workload management. Proceedings of the International Conference on Autonomic Computing ICAC'08, June 2-6, 2008, IEEE, Chicago, Illinois, USA., ISBN: 978-0-7695-3175-5, pp: 13-22.

Jarke, M., M. Jeusfeld and C. Quix, 2014. Data-centric intelligent information integration from concepts to automation. J. Intell. Inf. Syst., 43: 437-462.
CrossRef  |  Direct Link  |  

Kossmann, D. and T. Kraska, 2010. Data management in the cloud: Promises, state-of-the-art and open questions. Database Spectr., 10: 121-129.
CrossRef  |  Direct Link  |  

Krompass, S., D. Gmach, A. Scholz, S. Seltzsam and A. Kemper, 2006. Quality of Service Enabled Database Applications. In: Service-Oriented Computing-ICSOC 2006, Asit, D. and W. Lamersdorf (Eds.). Springer Berlin Heidelberg, Berlin, Germany, ISBN: 978-3-540-68147-2, pp: 215-226.

Krompass, S., H. Kuno, U. Dayal and A. Kemper, 2007. Dynamic workload management for very large data warehouses: Juggling feathers and bowling balls. Proceedings of the 33rd International Conference on Very Large Data Bases, September 23-28, 2007, VLDB Endowment, University of Vienna, Austria, ISBN: 978-1-59593-649-3, pp: 1105-1115.

Lee, R., M. Zhou and H. Liao, 2007. Request window: An approach to improve throughput of RDBMS-based data integration system by utilizing data sharing across concurrent distributed queries. Proceedings of the 33rd International Conference on Very Large Data Bases, September 23-28, 2007, VLDB Endowment, University of Vienna, Austria, ISBN: 978-1-59593-649-3, pp: 1219-1230.

Li, J., A.C. Konig, V. Narasayya and S. Chaudhuri, 2012. Robust estimation of resource consumption for sql queries using statistical techniques. Proc. VLDB. Endowment, 5: 1555-1566.
CrossRef  |  Direct Link  |  

Luo, G., J.F. Naughton and S.Y. Philip, 2006. Multi-Query SQL Progress Indicators. In: Advances in Database Technology-EDBT 2006. Yannis, I., H.S. Marc, J.W. Schmidt, F. Matthes and M. Hatzopoulos et al., (Eds.). Springer Berlin Heidelberg, Berlin, Germany, ISBN: 978-3-540-32960-2, pp: 921.

Maghawry, E.A., R.M. Ismail, N.L. Badr and M.F. Tolba, 2012. An Enhanced Resource Allocation Approach for Optimizing Sub Query on Cloud. In: Advanced Machine Learning Technologies and Applications. Hassanien, A.E., A. Badeeh, M. Salem, R. Ramadan and T.H. Kim (Eds.). Springer Berlin Heidelberg, Berlin, Germany, ISBN: 978-3-642-35325-3, pp: 413-422.

Maghawry, E.A., R.M. Ismail, N.L. Badr and M.F. Tolba, 2014. An enhanced queries scheduler for query processing over a cloud environment. Proceedings of the 2014 9th International Conference on Computer Engineering & Systems (ICCES), December 22-23, 2014, IEEE, Cairo, Egypt, ISBN: 978-1-4799-6593-9, pp: 409-414.

Maghawry, E.A., R.M. Ismail, N.L. Badr and M.F. Tolba, 2014. Queries Based Workload Management System for the Cloud Environment. In: Advanced Machine Learning Technologies and Applications. Hassanien, A.E., A.B.M. Salem, R. Ramadan and T.H. Kim (Eds.). Springer International Publishing, Cham, Germany, ISBN: 978-3-319-13460-4, pp: 77-86.

Maghawry, E.A., R.M. Ismail, N.L. Badr and M.F. Tolba, 2016. Enhancing query optimization technique by conditional merging over cloud computing. Proceedings of the 1st International Conference on Advanced Intelligent System and Informatics (AISI2015), November 28-30, 2015, Springer International Publishing, Beni Suef, Egypt, ISBN: 978-3-319-26688-6, pp: 347-356.

Paton, N., M.A.D. Aragao, K. Lee, A.A. Fernandes and R. Sakellariou, 2009. Optimizing utility in cloud computing through autonomic workload execution. Bull. Tech. Committee Data Eng., 32: 51-58.
Direct Link  |  

Paton, N.W., C.J. Buenabad, M. Chen, V. Raman and G. Swart et al., 2009. Autonomic query parallelization using non-dedicated computers: An evaluation of adaptivity options. VLDB. J., 18: 119-140.
CrossRef  |  Direct Link  |  

Paton, N.W., D.M.A. Aragao and A.A. Fernandes, 2012. Utility-driven adaptive query workload execution. Future Gener. Comput. Syst., 28: 1070-1079.
CrossRef  |  Direct Link  |  

Schroeder, B., H.M. Balter, A. Iyengar and E. Nahum, 2006. Achieving class-based QoS for transactional workloads. Proceedings of the 22nd International Conference on Data Engineering ICDE'06, April 3-7, 2006, IEEE, Carnegie Mellon University, Pittsburgh, Pennsylvania, ISBN: 0-7695-2570-9, pp: 153-153.

Sheikh, M.B., U.F. Minhas, O.Z. Khan, A. Aboulnaga and P. Poupart et al., 2011. A bayesian approach to online performance modeling for database appliances using gaussian models. Proceedings of the 8th ACM International Conference on Autonomic Computing, June 14-18. 2011, ACM, Karlsruhe, Germany, ISBN: 978-1-4503-0607-2, pp: 121-130.

Subramanian, I., C. McCarthy and M. Murphy, 2000. Meeting performance goals with the HP-UX workload manager. Proceedings of the 1st Workshop on Industrial Experiences with Systems Software WIESS, October 79-80, 2000, Ibrarian, San Diego, California, pp: 79-80.

Tian, F. and D.J. DeWitt, 2003. Tuple routing strategies for distributed eddies. Proceedings of the 29th International Conference on Very Large Data Bases, September 9-12, 2003, VLDB Endowment, Berlin, Germany, ISBN: 0-12-722442-4, pp: 333-344.

Tozer, S., T. Brecht and A. Aboulnaga, 2010. Q-Cop: Avoiding bad query mixes to minimize client timeouts under heavy loads. Proceedings of the 2010 IEEE 26th International Conference on Data Engineering (ICDE), March 1-6, 2010, IEEE, Long Beach, California, ISBN: 978-1-4244-5445-7, pp: 397-408.

Zhao, J., X. Hu and X. Meng, 2010. ESQP: An efficient SQL query processing for cloud data management. Proceedings of the second international Workshop on Cloud Data Management, October 26-30, 2010, ACM, Toronto, Ontario, Canada, ISBN: 978-1-4503-0380-4, pp: 1-8.

Design and power by Medwell Web Development Team. © Medwell Publishing 2024 All Rights Reserved