Strategies and systems towards grids and clouds integration:A DBMS-based solution

Future Generation Computer Systems

Volumn 88, Issue , 2018, Pages 718-729

  Mariotti, Mirko ^a   Gervasi, Osvaldo ^a   Vella, Flavio ^b   Cuzzocrea, Alfredo ^c   Costantini, Alessandro ^d  

^a UNIVERSITY OF PERUGIA   (Italy)

^b SAPIENZA UNIVERSITY OF ROME   (Italy)

^c UNIVERSITY OF TRIESTE   (Italy)

^d INFN SEZIONE DI BOLOGNA   (Italy)

Author keywords

Cloud computing; Distributed environments; GPGPU computing; Grid computing; Heterogeneous environments; Multi many core computing; Resource integration

Indexed keywords

CLOUD COMPUTING; DATABASE SYSTEMS; INTEROPERABILITY; PROGRAM PROCESSORS;

DISTRIBUTED ENVIRONMENTS; GPGPU COMPUTING; HETEROGENEOUS ENVIRONMENTS; MULTI/MANY CORE COMPUTING; RESOURCE INTEGRATION;

GRID COMPUTING;

EID: 85048737476     PISSN: 0167739X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.future.2017.02.047     Document Type: Article

References (40)

1. Bernaschi, M., Carbone, G., Mastrostefano, E., Vella, F., Solutions to the st-connectivity problem using a GPU-based distributed {BFS}. J. Parallel Distrib. Comput. 76 (2015), 145–153 Special Issue on Architecture and Algorithms for Irregular Applications.
2. Bressan, Stéphane, Cuzzocrea, Alfredo, Karras, Panagiotis, Lu, Xuesong, Heyrani-Nobari, Sadegh, An effective and efficient parallel approach for random graph generation over gpus. J. Parallel Distrib. Comput. 73:3 (2013), 303–316.
3. Gervasi, O., Russo, D., Vella, F., The aes implantation based on opencl for multi/many core architecture. Computational Science and Its Applications, ICCSA, 2010 International Conference on, 2010, 129–134.
4. Vella, F., Cefala, R.M., Costantini, A., Gervasi, O., Tanci, C., GPU computing in egi environment using a cloud approach. Computational Science and Its Applications, ICCSA, 2011 International Conference on, 2011, IEEE, 150–155.
5. Gervasi, O., Vella, F., Neri, I., Tasso, S., A simulation framework for scheduling performance evaluation on cpu-gpu heterogeneous system. Proceedings of the 12th International Conference on Computational Science and Its Applications - Volume Part IV ICCSA 2012, 2012, Springer-Verlag, Berlin, Heidelberg, 457–469.
6. Dovier, A., Pontelli, E., Formisano, A., Vella, Flavio, Parallel execution of the ASP computation - an investigation on GPUs. Proceedings of the Technical Communications of the 31st International Conference on Logic Programming (ICLP 2015), Cork, Ireland, August 31–September 4, 2015, 2015.
7. Parallella, Parallella 1-x Reference manual, 2014. (Accessed 11 July 2015).
8. Klauer, B., The convey hybrid-core architecture. Vanderbauwhede, Wim, Benkrid, Khaled, (eds.) High-Performance Computing Using FPGAs, 2013, Springer New York, 431–451.
9. Castillo, J., Bosque, A., Pedraza, C., Castillo, E., Huerta, P., Martinez, J.I., Low cost high performance reconfigurable computing. Vanderbauwhede, W., Benkrid, K., (eds.) High-Performance Computing Using FPGAs, 2013, Springer, New York, 453–479.
10. Foster, I., Zhao, Y., Raicu, I., Lu, S., Cloud computing and grid computing 360-degree compared. Grid Computing Environments Workshop, 2008, GCE’08, 2008, IEEE, 1–10.
11. Foster, I., Kesselman, C., Tuecke, S., The anatomy of the grid: enabling scalable virtual organizations. Int. J. High Perform. Comput. Appl. 15:3 (2001), 200–222.
12. Cuzzocrea, Alfredo, Furfaro, Filippo, Mazzeo, Giuseppe M., Saccà Domenico, A grid framework for approximate aggregate query answering on summarized sensor network readings. On the Move To Meaningful Internet Systems 2004: OTM 2004 Workshops: OTM Confederated International Workshops and Posters, GADA, JTRES, MIOS, WORM, WOSE, PhDS, and INTEROP 2004, Agia Napa, Cyprus, October 25–29, 2004. Proceedings, 2004, 144–153.
13. Hu, Junjie, Saleem, Arshad, You, Shi, Nordström, Lars, Lind, Morten, Østergaard, Jacob, A multi-agent system for distribution grid congestion management with electric vehicles. Eng. Appl. Artif. Intell. 38 (2015), 45–58.
14. Merzky, A., Jha, S., Fox, G., Using clouds to provide grids with higher levels of abstraction and explicit support for usage modes. Concurr. Comput.: Pract. Exp. 21:8 (2009), 1087–1108.
15. Buyya, R., Abramson, D., Giddy, J., Stockinger, H., Economic models for resource management and scheduling in grid computing. Concurr. Comput.: Pract. Exp. 14:13–15 (2002), 1507–1542.
16. Sotomayor, B., Keahey, K., Foster, I., Combining batch execution and leasing using virtual machines. Proceedings of the 17th International Symposium on High Performance Distributed Computing, 2008, ACM, 87–96.
17. Mariotti, M., Servoli, L., Cefalà R.M., A proposal to dynamically manage virtual environments in heterogeneous batch systems. IEEE Nuclear Science Symposium Conference Record, 2008. NSS08, 2008, 823–826.
18. Web site of eucalyptus, http://www.eucalyptus.com. (Accessed 9 September 2015).
19. Web site of nimbus project, http://www.nimbusproject.org/. (Accessed 9 September 2015).
20. Web site of the amazon elastic compute cloud (ec2), http://aws.amazon.com/ec2/. (Accessed 9 September 2015).
21. Web site of the open cloud computing interface (occi), http://occi-wg.org/. (Accessed 9 September 2015).
22. Web site of the fog library sdk, an interface to openstack cloud environment, https://github.com/fog/fog/blob/master/lib/fog/openstack/docs/getting_started.md. (Accessed 9 September 2015).
23. Web site of boto library, a python interface to amazon web services, https://github.com/boto/boto. (Accessed 9 September 2015).
24. Chellappa, R.K., Intermediaries in cloud–computing: A new computing paradigm. INFORMS Meeting, 1997.
25. Barone, G.B., Bifulco, R., Boccia, V., Bottalico, D., Canonico, R., Carracciuolo, L., Gaas: Customized grids in the clouds. Euro-Par 2012: Parallel Processing Workshops, 2013, Springer, 577–586.
26. Loomis, C., Airaj, M., Bégin, M.E., Floros, E., Kenny, S., O'Callaghan, D., Stratuslab cloud distribution. Eur. Res. Act. Cloud Comput., 2012, 271.
27. Ronchieri, E., Cesini, D., D'Agostino, D., Ciaschini, V., Torre, G. Dalla, Cozzi, P., Salomoni, D., Clematis, A., Milanesi, L., Merelli, I., The wnodes cloud virtualization framework: a macromolecular surface analysis application case study. Parallel, Distributed and Network-Based Processing, PDP, 2014 22nd Euromicro International Conference on, 2014, IEEE, 218–222.
28. Venugopal, S., Broberg, J., Buyya, R., Yeo, C.S., Brandic, I., Cloud computing and emerging it platforms: Vision, hype, and reality for delivering computing as the 5th utility. Future Gener. Comput. Syst. 25:6 (2009), 599–616.
29. Naskos, Athanasios, Gounaris, Anastasios, Sioutas, Spyros, Cloud elasticity: A survey. Algorithmic Aspects of Cloud Computing - First International Workshop, ALGOCLOUD 2015, Patras, Greece, September 14–15, 2015. Revised Selected Papers, 2015, 151–167.
30. Walters, J.P., Younge, A.J., Kang, D.I., Yao, K.T., Kang, M., Crago, S.P., Fox, G.C., GPU passthrough performance: A comparison of kvm, xen, vmware esxi, and lxc for cuda and opencl applications. Cloud Computing, CLOUD, 2014 IEEE 7th International Conference on, 2014, IEEE, 636–643.
31. Giunta, F., Montella, R., Laccetti, G., Isaila, F., Blas, F., A GPU accelerated high performance cloud computing infrastructure for grid computing based virtual environmental laboratory. Adv. Grid Comput., 2011, 121–146.
32. Duato, J.J., Igual, F.D., Mayo, R., Peña, A.J., Quintana-Ortí E.S., Silla, F., An efficient implementation of GPU virtualization in high performance clusters. Euro-Par 2009–Parallel Processing Workshops, 2010, Springer, 385–394.
33. Gupta, V., Gavrilovska, A., Schwan, K., Kharche, H., Tolia, N., Talwar, V., Ranganathan, P., GViM: GPU-accelerated virtual machines. Proceedings of the 3rd ACM Workshop on System-Level Virtualization for High Performance Computing HPCVirt’09, 2009, ACM, New York, NY, USA, 17–24.
34. Shi, L., Chen, H., Sun, J., Li, K., vCUDA: GPU-accelerated high-performance computing in virtual machines. IEEE Trans. Comput. 61:6 (2012), 804–816.
35. European grid infrastructure, http://www.egi.eu/. (Accessed 9 September 2015).
36. Thompson, K., Trickey, H., Pike, R., Presotto, D., et al. Plan 9 from bell labs. Proceedings of the Summer 1990 UKUUG Conference, 1990, London, UK, 1–9.
37. Cuzzocrea, Alfredo, Darmont, Jérôme, Mahboubi, Hadj, Fragmenting very large xml data warehouses via K-means clustering algorithm. IJBIDM 4:3/4 (2009), 301–328.
38. Leung, Carson Kai-Sang, Cuzzocrea, Alfredo, Jiang, Fan, Discovering frequent patterns from uncertain data streams with time-fading and landmark models. T. Large-Scale Data- and Knowledge-Centered Syst. 8 (2013), 174–196.
39. Cuzzocrea, Alfredo, Saccà Domenico, Ullman, Jeffrey D., Big data: a research agenda. 17th International Database Engineering & Applications Symposium, IDEAS’13, Barcelona, Spain - October 09–11, 2013, 2013, 198–203.