Skip to main content

Upcoming presentation on web application performance: why is this web app running slowly?

The venue is the next Seattle Code Camp meeting on September 13 at Seattle University. See https://seattle.codecamp.us/Schedule for details.

The presentation slides are available on SlideShare here.

I am also scheduled to give a similar presentation at the October meeting of the local .NET Developer's Association, to be held an the main Microsoft Redmond campus after hours on October 6. See http://www.meetup.com/NET-Developers-Association/ for details.

I have written a little something about this topic in the past, for example, here: http://performancebydesign.blogspot.com/2013/10/page-load-time-and-yslow-scalability.html. The latest presentation expands upon the ideas presented there, but also introduces a new ETW-based tool for monitoring web application response times for Windows apps, which I will be demo-ing.

Of course, I will have a lot more to say on this topic in future blog posts on the subject.

Comments

Popular posts from this blog

Monitoring SQL Server: the OS Wait stats DMV

This is the 2nd post in a series on SQL Server performance monitoring, emphasizing the use of key Dynamic Management View. The series starts here : OS Waits  The consensus among SQL Server performance experts is that the best place to start looking for performance problems is the OS Wait stats from the sys.dm_os_wait_stats DMV. Whenever it is running, the SQL Server database Engine dispatches worker threads from a queue of ready tasks that it services in a round-robin fashion. (There is evidently some ordering of the queue based on priority –background tasks with lower priority that defer to foreground tasks with higher priority.) The engine records the specific wait reason for each task waiting for service in the queue and also accumulates the Wait Time (in milliseconds) for each Wait reason. These Waits and Wait Time statistics accumulate at the database level and reported via the sys.dm_os_wait_stats DMV. Issuing a Query like the following on one of my SQL Server test mac

Memory Ballooning in Hyper-V

The previous post in this series discussed the various Hyper-V Dynamic Memory configuration options. Ballooning Removing memory from a guest machine while it is running is a bit more complicated than adding memory to it, which makes use of a hardware interface that the Windows OS supports. One factor that makes removing memory from a guest machine difficult is that the Hyper-V hypervisor does not gather the kind of memory usage data that would enable it to select guest machine pages that are good candidates for removal. The hypervisor’s virtual memory capabilities are limited to maintaining the second level page tables needed to translate Guest Virtual addresses to valid machine memory addresses. Because the hypervisor does not maintain any memory usage information that could be used, for example, to identify which of a guest machine’s physical memory pages have been accessed recently, when Guest Physical memory needs to be removed from a partition, it uses ballooning, which transfe

Hyper-V Architecture: Intercepts, interrupts and Hypercalls

Intercepts, interrupts and Hypercalls Three interfaces exist that allow for interaction and communication between the hypervisor, the Root partition and the guest partitions: intercepts, interrupts, and the direct Hypercall interface. These interfaces are necessary for the virtualization scheme to function properly, and their usage accounts for much of the overhead virtualization adds to the system. Hyper-V measures and reports on the rate these different interfaces are used, which is, of course, workload dependent. Frankly, the measurements that show the rate that the hypervisor processes interrupts and Hypercalls is seldom of interest outside the Microsoft developers working on Hyper-V performance itself. But these measurements do provide insight into the Hyper-V architecture and can help us understand how the performance of the applications running on guest machines is impacted due to virtualization. Figure 3 is a graph showing these three major sources of virtualization overhead