Name
Abstract | ||
|---|---|---|
Monitoring the status of running applications is a real life requirement and important research area. In particular log analysis is often required to understand how the system is behaving during execution. For example it is common for system administrators to collect and view logs from different hardware and software components to gain an understanding into system behavior, especially during activities such as problem determination. A recent research project in this area, the Run Time Correlation Engine (RTCE), provides a framework for run-time correlation of distributed log files in a scalable manner for enterprise applications. The framework has been designed for enterprise applications consisting of distributed software components and is in use in real industry environments. The purpose of this paper is to explore how the RTCE can scale for cloud computing environments where providers of cloud services will require large architectures (e.g. data centers) to deploy such services. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/ECBS.2010.11 | ECBS |
Keywords | Field | DocType |
scalable run-time correlation engine,software component,real industry environment,system behavior,run time correlation engine,event,cloud service,important research area,log file,correlation,ubiquitous computing,system administrator,internet,particular log analysis,distributed log file,scalable,rtce,cloud computing,enterprise application,distributed processing,cloud computing environment,meteorology,data center,generators,application software,sun,hardware,testing,computer architecture,search engines,stress | Search engine,Computer science,Real-time computing,Correlation,Ubiquitous computing,Component-based software engineering,Application software,Operating system,Cloud computing,The Internet,Scalability | Conference |
ISBN | Citations | PageRank |
978-1-4244-6538-5 | 9 | 0.63 |
References | Authors | |
7 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Miao Wang | 1 | 107 | 18.49 |
| Viliam Holub | 2 | 49 | 5.38 |
| Trevor Parsons | 3 | 121 | 8.79 |
| John Murphy | 4 | 75 | 10.07 |
| Patrick O'Sullivan | 5 | 9 | 0.63 |