(CNCF), which seeks to drive large-scale cloud computing with an emphasis on containers and microservices, has just added the Container Network Interface (CNI) project to its fold. The project joins others hosted by the nonprofit foundation, including the Kuberrnetes container orchestration platform and CoreDNS DNS server. CNI had been a GitHub open source project. It features… Continue reading Kubernetes foundation takes on container networking
Yaniv Romem is co-founder and CTO and Tom Leyden is VP of corporate marketing at Excelero Tech giants such as Amazon, Facebook, and Google have redefined IT for web-scale applications, leveraging standard servers and shared-nothing architectures to ensure maximum operational efficiency, flexibility, and reliability. As new application workloads – cloud, mobile, IoT, machine learning, and… Continue reading A cloud storage architecture for the enterprise
Microsoft is positioning its P language as a solution for asynchrony in a world where this capability is becoming increasingly vital for the cloud, artificial intelligence, and embedded systems. Geared to asynchronous event-driven programming, the open source P unifies modeling and programming into a single activity. “Today’s software uses cloud resources, is often embedded in… Continue reading Microsoft’s P language is aimed at where cloud, AI, and IoT meet
Microsoft apparently missed database godfather Michael Stonebraker’s memo. In 2005 Stonebraker declared the “one size fits all” mentality of the database market is an idea whose “time has come and gone.” Fast forward to 2017 and Microsoft launched Azure Cosmos DB, a new database that promises to do… everything. No, really. Everything. Relational data? Check.… Continue reading Does Microsoft’s Cosmos DB promise too much?
The internet of things is real, and it’s a real part of the cloud. A key challenge is how you can get data processed from so many devices. Cisco Systems predicts that cloud traffic is likely to rise nearly fourfold by 2020, increasing 3.9 zettabytes (ZB) per year in 2015 (the latest full year for… Continue reading Make sense of edge computing vs. cloud computing
When Google first told the world about its Tensor Processing Unit, the strategy behind it seemed clear enough: Speed machine learning at scale by throwing custom hardware at the problem. Use commodity GPUs to train machine-learning models; use custom TPUs to deploy those trained models. The new generation of Google’s TPUs is designed to handle both… Continue reading Google’s machine-learning cloud pipeline explained