A comparison of the influence of different multi-core processors on the runtime overhead for application-level monitoring

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7303 LNCS, Issue , 2012, Pages 42-53

  Waller, Jan ^a   Hasselbring, Wilhelm ^a,b  

^a UNIVERSITY OF KIEL   (Germany)

^b Steering Committee   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CONTROLLED LABORATORIES; HARDWARE PLATFORM; LOW OVERHEAD; MONITORED SYSTEMS; MULTI-CORE PROCESSOR; OPEN SOURCE SOFTWARE; OPERATING SOFTWARE; PERFORMANCE MONITORING; RUNTIME OVERHEADS; RUNTIMES; SOFTWARE SYSTEMS; STORING TIME;

COMPUTER SOFTWARE; EXPERIMENTS; LEVEL MEASUREMENT;

MONITORING;

EID: 84862166517     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-31202-1_5     Document Type: Conference Paper

References (25)

1. Chawla, A., Orso, A.: A generic instrumentation framework for collecting dynamic information. ACM SIGSOFT Softw. Eng. Notes 29, 1-4 (2004)
2. Chen, S., Gibbons, P.B., Kozuch, M., Mowry, T.C.: Log-based architectures: Using multicore to help software behave correctly. ACM SIGOPS Operating Systems Review 45, 84-91 (2011)
3. Ehlers, J., Hasselbring, W.: A Self-adaptive Monitoring Framework for Component-Based Software Systems. In: Crnkovic, I., Gruhn, V., Book, M. (eds.) ECSA 2011. LNCS, vol. 6903, pp. 278-286. Springer, Heidelberg (2011)
4. Ehlers, J., van Hoorn, A., Waller, J., Hasselbring, W.: Self-adaptive software system monitoring for performance anomaly localization. In: Proc. of the 8th IEEE/ACM Int. Conf. on Autonomic Computing (ICAC 2011), pp. 197-200. ACM (2011)
5. Gao, J., Zhu, E., Shim, S., Chang, L.: Monitoring software components and component-based software. In: The 24th Annual International Computer Software and Applications Conference (COMPSAC 2000), pp. 403-412. IEEE Computer Society (2000)
6. Georges, A., Buytaert, D., Eeckhout, L.: Statistically rigorous Java performance evaluation. ACM SIGPLAN Notices 42, 57-76 (2007)
7. Ha, J., Arnold, M., Blackburn, S., McKinley, K.: A concurrent dynamic analysis framework for multicore hardware. ACM SIGP. 44, 155-174 (2009)
8. van Hoorn, A., Rohr, M., Hasselbring, W., Waller, J., Ehlers, J., Frey, S., Kiesel-horst, D.: Continuous monitoring of software services: Design and application of the Kieker framework. Tech. Rep. TR-0921, Department of Computer Science, University of Kiel, Germany (2009)
9. van Hoorn, A., Waller, J., Hasselbring, W.: Kieker: A framework for application performance monitoring and dynamic software analysis. In: Proc. of 3rd ACM/SPEC Int. Conf. on Performance Eng. (ICPE 2012). ACM (2012)
10. Huang, X., Seyster, J., Callanan, S., Dixit, K., Grosu, R., Smolka, S., Stoller, S., Zadok, E.: Software monitoring with controllable overhead. Int. Journal on Software Tools for Technology Transfer (STTT), 1-21 (2010)
11. Maebe, J., Buytaert, D., Eeckhout, L., De Bosschere, K.: Javana: A system for building customized Java program analysis tools. ACM SIGPLAN Notices 41, 153-168 (2006)
12. Marowka, A.: Parallel computing on any desktop. Communications of the ACM 50, 74-78 (2007)
13. Moon, S., Chang, B.M.: A thread monitoring system for multithreaded Java programs. ACM SIGPLAN Notices 41, 21-29 (2006)
14. Mos, A.: A Framework for Adaptive Monitoring and Performance Management of Component-Based Enterprise Applications. Ph.D. thesis, Dublin City University (2004)
15. Moseley, T., Shye, A., Reddi, V.J., Grunwald, D., Peri, R.: Shadow profiling: Hiding instrumentation costs with parallelism. In: Int. Symp. on Code Generation and Optimization (CGO 2007), pp. 198-208 (2007)
16. Nair, A., Shankar, K., Lysecky, R.: Efficient hardware-based nonintrusive dynamic application profiling. ACM Transactions on Embedded Computing Systems 10, 32:1-32:22 (2011)
17. Okanović, D., van Hoorn, A., Konjović, Z., Vidaković, M.: Towards adaptive monitoring of Java EE applications. In: Proc. of the 5th Int. Conf. on Information Technology (ICIT 2011). IEEE Computer Society (2011)
18. Parsons, T., Mos, A., Murphy, J.: Non-intrusive end-to-end runtime path tracing for J2EE systems. IEEE Software 153, 149-161 (2006)
19. Patil, H., Fischer, C.N.: Efficient run-time monitoring using shadow processing. In: AADEBUG, pp. 119-132 (1995)
20. Sim, S.E., Easterbrook, S., Holt, R.C.: Using benchmarking to advance research: A challenge to software engineering. In: Proc. of the 25th Int. Conf. on Software Engineering (ICSE 2003), pp. 74-83. IEEE (2003)
21. Tichy, W.: Should computer scientists experiment more? IEEE Computer 31, 32-40 (1998)
22. Vlachos, E., Goodstein, M.L., Kozuch, M., Chen, S., Falsafi, B., Gibbons, P., Mowry, T.: ParaLog: Enabling and accelerating online parallel monitoring of multithreaded applications. ACM SIGPLAN Notices 45, 271-284 (2010)
23. Wallace, S., Hazelwood, K.: SuperPin: Parallelizing dynamic instrumentation for real-time performance. In: Int. Symp. on Code Generation and Optimization (CGO 2007), pp. 209-220 (2007)
24. Zhao, Q., Cutcutache, I., Wong, W.F.: Pipa: Pipelined profiling and analysis on multi-core systems. In: Proc. of the 6th Annual IEEE/ACM Int. Symp. on Code Generation and Optimization (CGO 2008), pp. 185-194. ACM (2008)
25. Zheng, Q., Ou, Z., Liu, L., Liu, T.: A novel method on software structure evaluation. In: Proc. of the 2nd IEEE Int. Conf. on Software Engineering and Service (ICSESS 2011). IEEE Computer Society (2011)