A statistical approach for identifying memory leaks in cloud applications

CLOSER 2011 - Proceedings of the 1st International Conference on Cloud Computing and Services Science

Volumn , Issue , 2011, Pages 623-628

  Šor, Vladimir ^a   Srirama, Satish Narayana ^a  

^a UNIVERSITY OF TARTU   (Estonia)

Author keywords

Byte code instrumentation; Cloud computing; Java virtual machine; Tools; Troubleshooting

Indexed keywords

BYTECODE INSTRUMENTATION; DETECTION METHODS; MEMORY LEAKS; POSITION PAPERS; PRODUCTION ENVIRONMENTS; PRODUCTION SYSTEM; RUNTIMES; SOURCE CODES; STATISTICAL APPROACH; STATISTICAL MEMORY; VIRTUAL MACHINES;

COMPUTER SYSTEMS; JAVA PROGRAMMING LANGUAGE; LEAK DETECTION;

CLOUD COMPUTING;

EID: 80052583657     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (18)

1. AppDynamics (2010). Appdynamics home page. http://www.appdynamics.com/.
2. Armbrust, M., Fox, A., Griffith, R., Joseph, A. D., Katz, R. H., Konwinski, A., Lee, G., Patterson, D. A., Rabkin, A., Stoica, I., and Zaharia, M. (2009). Above the clouds, a berkeley view of cloud computing. Technical report UCB/EECS-2009-28, University of California.
3. CA Wily Introscope (2010). http://www.ca.com/us/application-management. aspx.
4. Chen, K. and Chen, J.-B. (2007). Aspect-based instrumentation for locating memory leaks in java programs. In Computer Software and Applications Conference, 2007. COMPSAC 2007. 31st Annual International, volume 2, pages 23-28. (Pubitemid 350301767)
5. Chilimbi, T. M. and Hauswirth, M. (2004). Low-overhead memory leak detection using adaptive statistical profiling. In In Proceedings of the 11th International Conference on Architectural Support for Programming Languages and Operating Systems, pages 156-164.
6. Dean, J. and Ghemawat, S. (2004). Mapreduce: Simplified data processing on large clusters. In OSDI'04: Sixth Symposium on Operating System Design and Implementation.
7. Dmitriev, M. (2003). Design of jfluid: A profiling technology and tool based on dynamic bytecode instrumentation. Technical report, Sun Microsystems Laboratories.
8. Formanek, I. and Sporar, G. (2006). Dynamic bytecode instrumentation. Dr. Dobbs Journal, Online.
9. Jump, M. and McKinley, K. S. (2007). Cork: dynamic memory leak detection for garbage-collected languages. In Proceedings of the 34th annual ACM SIGPLAN-SIGACT symposium on Principles of programming languages, POPL '07, pages 31-38, New York, NY, USA. ACM.
10. Maxwell, E. K. (2010). Graph mining algorithms for memory leak diagnosis and biological database clustering. Master's thesis, Virginia Polytechnic Institute and State University.
11. Sedlacek, J. (2010). Uncovering memory leaks using netbeans profiler. http://netbeans.org/kb/articles/nb-profiler-uncoveringleakspt1.html.
12. Srirama, S. N., Batrashev, O., and Vainikko, E. (2010). Sci-Cloud: Scientific Computing on the Cloud. In The 10th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing(CCGrid 2010), page 579.
13. Standard Performance Evaluation Corporation (2008). Specjvm2008. http://www.spec.org/jvm2008/. Verified in Nov. 2010.
14. Sun Microsystems Inc. (2003). Tuning Garbage Collection with the 5.0 Java™ Virtual Machine.
15. Sun Microsystems Inc. (2006). Jvm™ tool interface. Online.
16. The Eclipse Foundation (2010). Memory analyzer. Online.
17. The Jikes RVM Project (2010). The jikes rvm project. Online.
18. Xu, G. and Rountev, A. (2008). Precise memory leak detection for java software using container profiling. In ICSE '08. ACM/IEEE 30th International Conference on Software Engineering, 2008., pages 151-160.