Google is harnessing machine learning to cut data center energy


Leave it to Google (s GOOG) to have an engineer so brainy he hacks out machine learning models in his 20 percent time. Google says that recently it’s been using machine learning — developed by data center engineer Jim Gao (his Googler nickname is “Boy Genius”) — to predict the energy efficiency of their global data centers down to 99.6 percent accuracy, and then to optimize the data centers in incremental ways if they become less efficient for whatever reason.

Part of Gao’s day-to-day job at Google is to track its data centers’ power usage efficiency, or PUE, which demonstrates how efficiently data center computing equipment is using energy. Traditionally many data center operators were seeing about half of their energy consumed by cooling equipment, but in recent years data center leaders like Google, Facebook(s fb) and others have focused on tools like using the outside air for cooling, or running the server…

View original post 450 more words

4 mistakes engineers make when building a SaaS company


Before I joined Loggly as CTO and VP of Engineering, I built seven cloud-based products. From my perspective, four mistakes separated the SaaS companies that stumble from the best.

1. “Adoption for our offering will take time, so we can build fast now and build right later.”
Look at how steep the technology adoption curve has become. Every SaaS product needs to be built for scalability and robustness from the start.


Image courtesy Udayan Banerjee

2. “Our customers have predictable behavior.”
Be ready for something unexpected that will threaten to break your service, and have processes for managing out-of-policy activities. For example, Loggly must deal with customers that send a huge burst of log events, inadvertently or during a fire, 24-7.

3. “We don’t need operations automation.”
Operations is at the heart of every SaaS business, and they shouldn’t be treated like sysadmins. By automating defrag of ElasticSearch, Loggly devops saves about 15…

View original post 117 more words