skip to main content
10.1145/2695664.2695890acmconferencesArticle/Chapter ViewAbstractPublication PagessacConference Proceedingsconference-collections
research-article

Evaluation of an adaptive framework for resilient Monte Carlo executions

Published: 13 April 2015 Publication History

Abstract

Solving certain calculations in time is crucial for some industrial, medical or research areas. However, problems with high computational requirements are specially constrained to the dependability of the computational resources. Distributed Computing Infrastructures have consolidated as the platform that can solve the issue in last decade. Grid and cloud infrastructures can currently supply users with thousands of resources of different types. Nevertheless, despite the advances achieved, the nature of these platforms finally makes them unpredictable, especially grid. Users continuously experience failures and poor performance, and consequently, infrastructures are unfeasible for some calculations. An instrument to deal with this lack of dependability is to build adaptive algorithms specifically designed for increasing the reliability of certain types of applications on these heterogeneous and dynamic infrastructures. In this work, the suitability of the Montera2 framework is evaluated for Monte Carlo calculations. For this purpose, the proposed approach is compared with the basic tools offered by current middleware, testing the execution of a set of different applications on production infrastructures.

References

[1]
D. W. O. Rogers, B. A. Faddegon, G. X. Ding, et al. BEAM: a Monte Carlo code to simulate radiotherapy treatment units. Medical Physics, 22(5):503--524, 1995.
[2]
E. García, A. Saracibar, S. Gómez-Carrasco, and A. Laganà. Modeling the global potential energy surface of the N + N2 reaction from ab initio data. Phys. Chem. Chem. Phys., 10(18):2552--2558, 2008.
[3]
M. A. Rodríguez-Pascual, J. Guasp, F. Castejón, et al. Improvements on the Fusion Code FAFNER2. IEEE Trans. Plasma Sci., 38:2102--2110, 2010.
[4]
C. Inzinger, B. Satzger, W. Hummer, et al. Non-Intrusive Policy Optimization for Dependable and Adaptive Service-Oriented Systems. In Proc. of 27th Annu. ACM Symp. on Appl. Comput. (SAC'12), pages 504--510, Riva del Garda, Italy, March 2012. ACM NY, USA.
[5]
Ian Foster. What is the Grid? A Three Point Checklist. Grid Today, 1, 2002.
[6]
E. Huedo, R. S. Montero, and I. M. Llorente. Evaluating the reliability of computational grids from the end user's point of view. J. Systems Architecture, 52(12):727--736, 2006.
[7]
M. Rodríguez-Pascual, I. M Llorente, and R. Mayo-García. Montera: a framework for the efficient executions of Monte Carlo codes on the Grid. Computing and Informatics, 32(1):113--144, 2013.
[8]
M. Rodríguez-Pascual, A. J. Rubio-Montero, A. Bustos, et al. More Efficient Executions of Monte Carlo Fusion Codes by Means of Montera: The ISDEP Use Case. In Proc. of 19th Int. Euromicro Conf. on Parallel, Distrib. and Network-Based Process. (PDP'11), pages 380--384, Ayia Napa, Cyprus, Feb. 2011. IEEE C. S.
[9]
P. Andreetto, S. Andreozzi, G. Avellino, et al. The gLite Workload Management System. J. Physics: Conf. Series, 119(6):62007, 2008.
[10]
J. Frey, T. Tannenbaum, M. Livny, et al. Condor-G: A Computation Management Agent for Multi-Institutional Grids. Cluster Comput., 5(3):237--246, 2002.
[11]
E. Huedo, R. S. Montero, and I. M. Llorente. The GridWay framework for adaptive scheduling and execution on grids. Scalable Comput.- Pract. Exper., 6(8):1--8, 2005.
[12]
A. Kretsis, P. Kokkinos, and E. A. Varvarigos. Implementing and evaluating scheduling policies in gLite middleware. Concurrency Computat. Pract. Exper., 25(3):349--366, 2013.
[13]
J. M. Ram, A. Tchernykh, R. Yahyapour, et al. Job Allocation Strategies with User Run Time Estimates for Online Scheduling in Hierarchical Grids. J. Grid Comput., 9(1):95--116, 2011.
[14]
J. Simão, T. Garrochinho, and L. Veiga. A checkpointing-enabled and resource-aware Java Virtual Machine for efficient and robust e-Science applications in grid environments. Concurrency Computat. Pract. Exper., 24(13):1421--1442, 2012.
[15]
C-C. Hsu, K-C. Huang, and F-J. Wang. Online scheduling of workflow applications in grid environments. Future Gener. Comput. Syst., 27(6):860--870, 2011.
[16]
M. Allani, B. Garbinato, and P. Pietzuch. Hyphen: A Hybrid Protocol for Generic Overlay Construction in P2P Environments. In Proc. of 28th Annu. ACM Symp. on Appl. Comput. (SAC'13), pages 423--430, Coimbra, Portugal, March 2012. ACM NY, USA.
[17]
M. Matos, A. Sousa, J. Pereira, and R. Oliveira. CLON: Overlay Network for Clouds. In Proc. of 3rd Workshop on Dependable Distributed Data Management (WDDDM'09), pages 14--17, Nuremberg, Germany, March 2009. ACM NY, USA.
[18]
A. J. Rubio-Montero, E. Huedo, F. Castejón, and R. Mayo-García. GWpilot: Enabling multi-level scheduling in distributed infrastructures with GridWay and pilot jobs. Future Gener. Comput. Syst. 45:25--52, 2015.
[19]
S. Camarasu-Pop, T. Glatard, R. F. da Silva, et al. Monte Carlo simulation on heterogeneous distributed systems: A computing framework with parallel merging and checkpointing strategies. Future Gener. Comput. Syst., 29(3):728--738, 2013.
[20]
S. Camarasu-Pop, T. Glatard, et al. Dynamic Partitioning of GATE Monte-Carlo Simulations on EGEE. J. Grid Comput., 8(2):241--259, 2010.
[21]
J. Díaz, S. Reyes, A. Niño, and C. Muñoz-Caro. Derivation of self-scheduling algorithms for heterogeneous distributed computer systems: Application to internet-based grids of computers. Future Gener. Comput. Syst., 25(6):617--626, 2009.
[22]
Y. Li and M. Mascagni. Grid-based Monte Carlo Application. In Grid Computing, volume 2536 of Lect. Notes in Comput. Sci., pages 13--24. Springer, 2002.
[23]
Y. Tao, H. Jin, S. Wu, et al. Dependable Grid Workflow Scheduling Based on Resource Availability. J. Grid Comput., 11(1):47--61, 2012.
[24]
J. K. Nilsen, X. Cai, B. Høyland, and H. P. Langtangen. Simplifying the parallelization of scientific codes by a function-centric approach in Python. Comput. Sci. & Discovery, 3(1):15003, 2010.
[25]
R. S. Montero, E. Huedo, and I. M Llorente. Benchmarking of high throughput computing applications on grids. Parallel Comput., 32(4):267--279, 2006.
[26]
A. Ferrari, P. R. Sala, A. Fasso, and J. Ranft. FLUKA: A Multi-Particle Transport Code. Number October. CERN, Geneva, 2005.
[27]
Jaime Rosado Vélez. Analysis of the air fluorescence induced by electrons for application to cosmicray detection. PhD thesis, Universidad Complutense de Madrid, 2011.
[28]
M. Rodríguez-Pascual, A. Bustos, et al. Simulations of fast ions distribution in stellarators based on coupled Monte Carlo fuelling and orbit codes. Plasma Phys. and Controlled Fusion, 55(8):85014, 2013.
[29]
D. Lingrand, J. Montagnat, J. Martyniak, and D. Colling. Optimization of Jobs Submission on the EGEE Production Grid: Modeling Faults Using Workload J. Grid Comput. 8(2):305--321, 2010.

Cited By

View all
  • (2017)A simple model to exploit reliable algorithms in cloud federationsSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-016-2143-921:16(4543-4555)Online publication date: 1-Aug-2017
  • (2016)Adapting Reproducible Research Capabilities to Resilient Distributed CalculationsInternational Journal of Grid and High Performance Computing10.4018/IJGHPC.20160101058:1(58-69)Online publication date: 1-Jan-2016
  • (2016)User-Guided Provisioning in Federated Clouds for Distributed CalculationsAdaptive Resource Management and Scheduling for Cloud Computing10.1007/978-3-319-28448-4_5(60-77)Online publication date: 8-Jan-2016

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
SAC '15: Proceedings of the 30th Annual ACM Symposium on Applied Computing
April 2015
2418 pages
ISBN:9781450331968
DOI:10.1145/2695664
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 April 2015

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. Monte Carlo
  2. dynamic
  3. efficiency
  4. fault tolerance
  5. grid computing
  6. scheduling

Qualifiers

  • Research-article

Conference

SAC 2015
Sponsor:
SAC 2015: Symposium on Applied Computing
April 13 - 17, 2015
Salamanca, Spain

Acceptance Rates

SAC '15 Paper Acceptance Rate 291 of 1,211 submissions, 24%;
Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

Upcoming Conference

SAC '25
The 40th ACM/SIGAPP Symposium on Applied Computing
March 31 - April 4, 2025
Catania , Italy

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)0
  • Downloads (Last 6 weeks)0
Reflects downloads up to 17 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (2017)A simple model to exploit reliable algorithms in cloud federationsSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-016-2143-921:16(4543-4555)Online publication date: 1-Aug-2017
  • (2016)Adapting Reproducible Research Capabilities to Resilient Distributed CalculationsInternational Journal of Grid and High Performance Computing10.4018/IJGHPC.20160101058:1(58-69)Online publication date: 1-Jan-2016
  • (2016)User-Guided Provisioning in Federated Clouds for Distributed CalculationsAdaptive Resource Management and Scheduling for Cloud Computing10.1007/978-3-319-28448-4_5(60-77)Online publication date: 8-Jan-2016

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media