The AppScale team is excited to announce the availability of AppScale 3.4! This release contains new features, additions to the API, performance improvements, upgrades for dependencies, and bug fixes.
A key new feature is the ability to deploy and manage multiple services
In AppScale, we call each App Engine application instance an AppServer. It’s the unit we use to scale the application. Applications need at least one AppServer running to serve requests. Adding more AppServers increases application performance and redundancy.
High latency can kill a user’s experience and hurt your bottom line. An Amazon study found that every tenth of a second added to how long it took to render a web page had adverse effects on revenue. Google found that a drop in half a second in responsiveness resulted in a 20% drop in users. 1
When Google made App Engine available to the public in 2008, it was an immediate success. Developers and innovators loved the ability to quickly create web and mobile applications. This ease of use made Google App Engine (GAE) popular with start-up innovators and agile enterprises. There are many benefits to using GAE including the auto-scaling of applications and services, and the no-ops experience it provides.
AppScale 3.1 saw the introduction of the Azure agent in a beta capability. AppScale 3.2 brings a new and improved Azure agent which graduates to production ready our support of Azure. One of the main changes, was the introduction of Scale Sets in the agent code.
We recently have received growing interest from our customers in moving their Google App Engine workload to Microsoft Azure, and to emerging markets around the globe. This is no surprise given Microsoft’s wide range of global deployment options, including multiple alternatives in China.
AppScale’s strong suit has been to support a wide range of resources, from clouds (public and private) to virtualized environments (virtualbox, kvm, docker, and more) to bare-metal (clusters and any resources the user has access to). Whenever there is a provisioning service, AppScale can take advantage of it to simplify resource management: for example, AWS, GCE, Azure, Euca, OpenStack all provide provisioning systems that AppScale can leverage to manage resources -- instances, network, disks.
We are thrilled to announce the release of AppScale 3.2. The main theme for the next installment in the AppScale 3 series, has been the tuning and improvement of the Big Data capabilities of AppScale. So in this release we finely tuned the performance of Datastore, and TaskQueue APIs in order to support massive load with the MapReduce and Pipeline libraries.