Streamlining Python Development: A Guide to a Modern Project Setup
Methodology of virtual machines sizing
1. Leonid Grinshpan, Ph.D. Consulting Technical Director, Oracle Corporation Methodology of virtual machines sizing Share to Facebook Share to LinkedIn Share toTwitter Share to SlideShare
17. Comparison between non-virtualized and virtualized deployments when a number of CPUs on each VM is equal to the one utilized by application in non-virtualized servers
18. Queuing theory explains it all Formal explanation based on queuing theory can be found in a book: Leonid Grinshpan. Solving Enterprise Application Performance Puzzles: Queuing Models to the Rescue, Willey-IEEE Press; available in bookstores and from Web booksellers from January 2012
19. Queuing theory results are in line with our experience Toll plaza Toll plaza with booths equally accessible by any cars has lower congestion than the same plaza with booths divided by two categories: ones serving only sedans and the others serving only trucks. An intuitive explanation is: in a non-divided plaza in an absence of the trucks a sedan can be processed by any booth and vice versa; in a divided plaza the booths dedicated to trucks will stay idle even if there is a queue of sedans. Movie theater box office If any wicket serves any theatergoers, then waiting queue is not as long as in a case when some wickets provide only to particular customer categories.
20. VM sizing after lesson learned Increasing a number of CPUs per virtual machine
21. VM sizing after lesson learned (continued 2) Deployment of Application A on non-virtual platform delivers the shortest transaction times. One-CPU virtual machines provide terrible performance. Set up with two CPUs per virtual machine increases transaction time 15% - 22%.
22. VM sizing after lesson learned (continued 3) Deployment of Application B on non-virtual platform delivers the shortest transaction times. Four-CPU virtual machines significantly degrades performance. Six-CPU virtual machines features only slight increase in transaction time.