Adaps - A three-phase adaptive prediction system for the run-time of jobs based on user behaviour
Journal of Computer and System Sciences
Minimizing technical complexities in emerging cloud computing platforms
Euro-Par 2010 Proceedings of the 2010 conference on Parallel processing
International Journal of Distributed Systems and Technologies
Future Generation Computer Systems
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Computers perform different applications in different ways. To characterize an application performance into a machine, the usual method is a throughout execution of it. This work is a step into a synthetic probe able to characterize a master-worker application's performance in a fraction of the time required to run it entirely. This is specially important for CPU-intensive scientific applications, who runs for very long, as it makes sense that it runs as efficiently (and fast) as possible. To know how, and for how long a master-worker application is going to run can guide the decision to use this machine or not. Our software probe takes into account only the performance-relevant parts of the application, discovering a program's relevant phases. Running solely these significant phases is a powerful way to quickly characterize the application's performance on a machine. It can help to select the best computing nodes in a grid or in a multi-cluster to run this application, and even quickly predict the total execution time for this application/data set in the machine analyzed. We also present ongoing work on a fully synthetic probe generated from programs' phases.