Deployment environment

Abstract: Continuous deployment is the software engineering practice of deploying many small incremental software updates into production, leading to a continuous stream of 10s, 100s, or even 1,000s of deployments per day. High-profile Internet firms such as Amazon, Etsy, Facebook, Flickr, Google, and Netflix have embraced continuous deployment. However, the practice has not been covered in textbooks and no scientific publication has presented an analysis of continuous deployment. In this paper, we describe the continuous deployment practices at two very different firms: Facebook and OANDA. We show that continuous deployment does not inhibit productivity or quality even in the face of substantial engineering team and code size growth. To the best of our knowledge, this is the first study to show it is possible to scale the size of an engineering team by 20X and the size of the code base by 50X without negatively impacting developer productivity or software quality. Our experience suggests that top-level management support of continuous deployment is necessary, and that given a choice, developers prefer faster deployment. We identify elements we feel make continuous deployment viable and present observations from operating in a continuous deployment environment.

Abstract: Performance predictions of component assemblies and the ability of obtaining system-level performance properties from these predictions are a crucial success factor when building trustworthy component-based systems. In order to achieve this goal, a collection of methods and tools to capture and analyze the performance of software systems has been developed. These methods and tools aim at helping software engineers by providing them with the capability to understand design trade-offs, optimize their design by identifying performance inhibitors, or predict a system's performance within a specified deployment environment. In this paper, we analyze the applicability of various performance prediction methods for the development of component-based systems and contrast their inherent strengths and weaknesses in different engineering problem scenarios. In so doing, we establish a basis to select an appropriate prediction method and to provide recommendations for future research activities, which could significantly improve the performance prediction of component-based systems.

Abstract: Methods and apparatus are disclosed to scale application deployments in cloud computing environments using virtual machine pools. An example method disclosed herein includes displaying a user-selectable control to specify whether the application is to be scaled in accordance with a scaling policy, based on selection of the user-selectable control, storing, in a blueprint of the application, an indication of whether the application is to be scaled in accordance with the scaling policy, based on the indication in the blueprint, preparing a virtual machine pool in the computing environment, the virtual machine pool including a virtual machine provisioned for use in a scaling operation, in response to a request to scale the application deployed in a deployment environment, determining whether configuration information satisfies a scaling requirement, and based on the determination, performing the scaling operation in accordance with the request to scale by transferring the virtual machine to the deployment environment.