devopsy.kinsta.cloud

Tag: cloud deployments

  • AWS Looks to Accelerate Windows Migrations to the Cloud

    AWS Looks to Accelerate Windows Migrations to the Cloud

    Amazon Web Services (AWS) is ratcheting up pressure on Microsoft by devoting more resources to enable IT organizations to migrate Windows workloads to the cloud.

    Fred Wurden, general manager for enterprise engineering at AWS, said a Migration Acceleration Program (MAP) for Windows, now generally available, provides IT teams with prescriptive guidance, consulting support, tools, training and service credits to help reduce the risk and cost of migrating to the cloud.

    The service is being delivered in collaboration with partners including Rackspace, 2nd Watch, Accenture, Cloudreach, Enimbos Global Services, Onica and Slalom. Wurden said AWS has been migrating Windows workloads to the cloud for more than a decade now and claims to have more than half of all Windows workloads deployed in the cloud.

    Microsoft has clearly been making strides in recent years to close the gap between it and AWS by running both Windows and Linux workloads on its cloud alongside instances of Microsoft Office 365. Microsoft most recently reported a 39% year-over-year growth in commercial cloud revenue, reaching $13.3 billion for its fiscal third quarter. However, Microsoft does not break out Azure revenue from Microsoft Office 365 revenue or disclose what percentage of the workloads running on Azure is based on Windows versus Linux.

    At the same time, Microsoft now charges more for instances of SQL Server that run on other clouds. AWS contends that decision will only accelerate the rate at which organizations are transitioning to open source databases deployed on Windows or Linux.

    Competition for Windows workloads is growing in ferocity in the wake of the COVID-19 pandemic because more workloads are being lifted and shifted into the cloud as organizations realize that on-premises IT environments can be rendered inaccessible. IT staffs in many geographies will be working from home at least through the end of the year. Many organizations are also moving workloads to the cloud in anticipation of a second potential outbreak of the pandemic.

    At the same time, the rate at which new workloads are being deployed in the cloud is also accelerating. Many of those cloud-native workloads are based on containers running on Kubernetes. Support for containers on Windows platforms is still relatively nascent when compared to Linux platforms.

    AWS and Microsoft, along with a host of rival cloud service providers, are focusing on legacy workloads because the expectation is that monolithic workloads running on Windows will be re-engineered to run as a set of microservices that can be updated more flexibly within the context of a larger digital business transformation initiative. It may be a while before that transformation fully manifests; however, it’s clear the battle for control over millions of workloads in anticipation of that transition is reaching a higher level of intensity.

    There’s no doubt many of those workloads will run on the Microsoft Azure cloud. However, the degree to which Microsoft might be winning in the cloud era may not be as great when everything that is being lost to AWS and other cloud service providers is taken into consideration.

  • AWS Applies Machine Learning to Optimize Cloud Deployments

    AWS Applies Machine Learning to Optimize Cloud Deployments

    Amazon Web Services (AWS) this week at its re:Invent 2019 conference unfurled two tools based on machine learning algorithms to optimize cloud application deployments.

    Amazon CodeGuru is a service available as a preview, through which the cloud service provider will inspect application code using machine learning algorithms to profile the code and then identify bottlenecks and what part of that code is the most expensive to run on the AWS cloud.

    AWS Compute Optimizer, meanwhile, identifies optimal Amazon EC2 instance types, including those that are a part of Auto Scaling groups, for specific types of workloads. It analyzes the configuration and resource utilization of a workload, including historical metrics, to identify dozens of characteristics to recommend optimal AWS compute resources. AWS Compute Optimizer is accessed via the AWS Management Console. Rather than rely on humans to optimize cloud platforms, AWS is making a case for reducing the time and effort required to determine which of dozens of instance types will deliver the highest performance at the lowest cost possible.

    Amazon CloudGuru can pull code from either GitHub or CodeCommit repositories, with support for other repositories planned. It requires developers to insert agent software developed by AWS into their code. Once a pull request is made, Amazon CodeGuru will automatically start evaluating the code using trained artificial intelligence (AI) models that AWS developed using data gathered from thousands of different open source software projects by AWS and its parent company.

    Once the analysis is completed, Amazon CodeGuru will generate a “flame graph” showing, for example, latency issues and CPU utilization rates alongside human-readable recommendations to surface specific issues and recommended remediations that include example code and links to relevant documentation for any line of code. Amazon CodeGuru can observe application runtimes and profile application code every five minutes.

    AWS CEO Andy Jassy told conference attendees that Amazon is already employing Amazon CodeGuru across 80,000 applications, which has resulted in increases in infrastructure utilization rates that have reached 325% in some cases.

    While machine learning algorithms have a clear role to play in terms of enabling DevOps teams to build and deploy more efficient code, it’s not clear to what degree DevOps teams will want to give AWS that level of access to what are often highly proprietary applications. Many DevOps teams may prefer to employ machine learning algorithms within the context of a continuous integration/continuous deployment (CI/CD) environment to drive code to multiple cloud computing platforms. Whatever the approach pursued, it’s clear machine learning algorithms are about to play a much larger role in DevOps. In fact, Jassy this week made it clear AWS will be applying machine learning algorithms broadly to enhance everything from enterprise search to identifying potential fraud.

    What is less clear is how best DevOps practices will need to evolve to account for machine learning algorithms. Many of the processes that make up a DevOps toolchain are increasingly being automated. That doesn’t eliminate the need for the toolchain, but it will sharply reduce the amount of time and effort required to build and optimally deploy applications.

    — Mike Vizard