This site uses cookies to improve your experience. To help us insure we adhere to various privacy regulations, please select your country/region of residence. If you do not select a country, we will assume you are from the United States. Select your Cookie Settings or view our Privacy Policy and Terms of Use.
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Used for the proper function of the website
Used for monitoring website traffic and interactions
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Strictly Necessary: Used for the proper function of the website
Performance/Analytics: Used for monitoring website traffic and interactions
The image below shows a significant drop in latency once we've launched the new point of presence in Israel. In fact, latency has been reduced by almost 50%! With a total of 5 POPs in Oceania, this continent benefits from lower latency with every POP added. So far, traffic from Nigeria has been routed to Europe.
This enables customers to serve content to their end users with low latency, giving them the best application experience. In 2008, AWS opened a point of presence (PoP) in Hong Kong to enable customers to serve content to their end users with low latency. Since then, AWS has added two more PoPs in Hong Kong, the latest in 2016.
They can run applications in Sweden, serve end users across the Nordics with lower latency, and leverage advanced technologies such as containers, serverless computing, and more. Vivino also uses Auto Scaling to deal with the large seasonal fluctuations in traffic. Telenor Connexion.
Back in 2016, I gave a talk outlining the causes and effects of the terrible performance of web apps built using popular tools on the fastest-growing device segment: low-end to mid-range Android phones. In 2016, Jio swept over the subcontinent like a monsoon dropping a torrent of 4G infrastructure and free data rather than rain.
There is no way to model how much more traffic you can send to that system before it exceeds it’s SLA. I presented this analysis of response time distributions talk in 2016 — at Microxchg in Berlin ( video ). Mu is the mean of each component, the latency. I’ve been thinking about this for a long time.
This is a complex topic, but to borrow from a recent post , web performance expands access to information and services by reducing latency and variance across interactions in a session, with a particular focus on the tail of the distribution (P75+). Consistent performance matters just as much as low average latency.
This was a keynote presentation at the “2nd International Workshop on Performance Modeling: Methods and Applications” (PMMA16), June 23, 2016, Frankfurt, Germany (in conjunction with ISC16 ). Here I assumed a particular analytical function for the amount of memory traffic as a function of cache size to scale the bandwidth time.
This is similar to the type of redirect used if you’ve registered multiple domains and you want to direct all of your traffic to your primary URL. In all of these instances, you should identify which URL garners the most traffic and then configure an HTTP 301 -type redirect for all of the lesser-used URLs to the most-trafficked.
LTS (April 2016). I wrote about it in a previous post, [DTrace for Linux 2016]. Here's some output from my zfsdist tool, in bcc/BPF, which measures ZFS latency as a histogram on Linux: # zfsdist. Tracing ZFS operation latency. The hardest part on Linux is now done: kernel support. Hit Ctrl-C to end. ^C
Contended, over-subscribed cells can make “fast” networks brutally slow, transport variance can make TCP much less efficient , and the bursty nature of web traffic works against us. It simulates a link with a 400ms RTT and 400-600Kbps of throughput (plus latency variability and simulated packet loss).
This was a keynote presentation at the “2nd International Workshop on Performance Modeling: Methods and Applications” (PMMA16), June 23, 2016, Frankfurt, Germany (in conjunction with ISC16 ). Here I assumed a particular analytical function for the amount of memory traffic as a function of cache size to scale the bandwidth time.
I started writing “ Serverless Architectures ” in May 2016. I also rewrote the section on Startup Latency since Cold Starts are one of the big “FUD” areas of Serverless. I was glad to be able to talk about Amazon’s automated traffic shifting / canary releases. I thought a few folks might be interested.
We organize all of the trending information in your field so you don't have to. Join 5,000+ users and stay up to date on the latest articles your peers are reading.
You know about us, now we want to get to know you!
Let's personalize your content
Let's get even more personalized
We recognize your account from another site in our network, please click 'Send Email' below to continue with verifying your account and setting a password.
Let's personalize your content