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  • Appian World CEO Keynote: Low Code for Sophisticated Applications

    Appian World CEO Keynote: Low Code for Sophisticated Applications

    Here I am at the Fontainebleau Miami Beach for Appian World 2018, where the theme is “Transform.” The event, which is the biggest one yet with 1,500 registered attendees, is all about low code, and in his keynote address, CEO Matt Calkins emphasized that low code app-building isn’t for unsophisticated, simple apps—we don’t need powerful tools such as Appian for those. Rather, low code is for sophisticated apps to set free the renaissance that will happen with easier app-building.

    This is the first Appian World since the company went public last year, and the event has a public-company feel. I didn’t see a lot of people in jeans and t-shirts—most attendees were fairly well-dressed in business-casual, the mark of enterprises.

    appian worldOne of the touch points in Calkins’ keynote was reliance on open standards. Another was to make use of artificial intelligence (AI). I know AI and machine learning (ML) seem to be everywhere. It was interesting the company announced AI integrations with Amazon, Microsoft and Google, but no Apple or IBM Watson. I will ask Calkins about this when I interview him for DevOps TV later today.

    Calkins inherently believes that low code will set off a revolution as software continues to disrupt and empower the digital transformation revolution. The logic behind it is simple: “Making applications easier to make because you don’t have to code as much.” But Calkins emphasizes that does not mean you are making unsophisticated applications. The world doesn’t need low-code tools to make unsophisticated applications. It is the higher calling for sophisticated apps that Appian is all about.

    Another mantra from the keynote was, “Host anywhere, run anywhere, data anywhere.” The idea here is a true hybrid reality where data can be on-prem or apps hosted on public cloud, on containers or not. In fact, Calkins was very bullish on containers (who isn’t?). Docker integration is already available and he hinted at announcements regarding Kubernetes as well.

    Appian seems to have succeeded by becoming the low-code platform. With lots of integration and focus on making application development, management and deployment easier, the company has risen to the top of its market.

    Stay tuned for more updates from Appian World today and tomorrow.

    — Alan Shimel

  • Reliance on Cloud Foundry PaaS Increases

    Reliance on Cloud Foundry PaaS Increases

    A survey of 383 IT professionals published April 18 suggests the open source Cloud Foundry platform-as-a-service (PaaS) environment is gaining traction across the enterprise.

    The survey conducted by the Cloud Foundry Foundation (CFF) finds one-third of companies now have at least 100 developers using Cloud Foundry. The number of companies with more than 50 developers using Cloud Foundry rose from 20 percent to 47 percent in the last year. More than half the respondents (53 percent) report they have been using Cloud Foundry for less than two years.

    Companies with more than 50 applications on Cloud Foundry rose from 24 percent to 40 percent in just over six months and nearly a third (30 percent) of respondents describe their Cloud Foundry usage as broad.

    More than a third of Cloud Foundry users report they now save a few months or more per application development cycle, and 10 percent report saving more than six months. In addition, nearly a quarter report saving $500,000 per application development cycle, with 17 percent saying they have saved more than $1 million.

    The survey also finds there has been a sharp rise in interest in containers. A total of 13 percent of respondents already are using both the Cloud Foundry Application Runtime (CFAR) and the Cloud Foundry Container Runtime (CFCR), which is based on an instance of Kubernetes. Another 45 percent are evaluating CFCR.

    CFF CTO Chip Childers said he expects the Cloud Foundry PaaS and Kubernetes to be employed side by side for years to come. The CFF will continue to develop technologies that unify the management of Cloud Foundry and Kubernetes before there is an inevitable convergence of the two platforms, he said, adding that for now, a PaaS and container-as-a-service (CaaS) environment are serving different classes of applications. In the case of Cloud Foundry, that historically has been complex 12-factor applications. But Childers noted that the types of applications being deployed on Cloud Foundry is also expanding.

    Overall, Cloud Foundry reports more than 3,200 contributors participating in the community, with more than 60,000 commits in the last 12 months alone. According to Gartner, the total market value Cloud Foundry is approximately $3.1 billion.

    As application development continues to evolve, it is clear many organizations are embracing multiple platforms. Different business units within organizations want to be able to develop applications in the cloud or on-premises as needed. Ninety percent of ten Cloud Foundry users surveyed cited cross-platform flexibility as important capability, with 67 percent calling it “very important.” One of the core value propositions of Cloud Foundry is that it provides a highly portable platform backed by a consortium that includes IBM, Pivotal, SAP, SUSE and, now, Alibaba.

    In general, DevOps teams are embracing PaaS and CaaS environments to take advantage of higher levels of abstraction that don’t require them to manage the underlying IT infrastructure. The theory is that the more automated the environment becomes, the more resources there are to allocate to developing applications versus managing the platform.

    The trade-off is that each PaaS and CaaS environment is usually highly opinionated in terms of how the application development process should be managed. Because of that issue, many organizations still prefer to build their own platforms from the ground up. But as PaaS and CaaS platforms continue to mature, it’s likely many more organizations may decide there isn’t enough differentiated value to be gained by building a platform themselves.

    — Mike Vizard

  • VMware Extends vSphere Hybrid Cloud Reach

    VMware Extends vSphere Hybrid Cloud Reach

    VMware this week extended its campaign to automate IT environments based on its hypervisors by adding a link capability that enables the VMware vCenter platform to manage instances of VMware vSphere running on-premises and in public clouds such as Amazon Web Services (AWS) and IBM Cloud from the same console.

    Mike Adams, senior director of cloud platform product marketing for VMware, said the linking capability added to VMware vSphere will enable DevOps teams to deploy the VMware hypervisor anywhere without having to invest in a separate management framework for each environment.

    At the same time, VMware is also updating VMware vSAN storage software that, among other capabilities, adds a self-healing feature designed to minimize disruptions in the event of underlying hardware failure.

    Version 6.7 of VMware vSphere and VMware vSAN also now support enhanced client software based on an HTML5 interface that can manage VMware NSX network virtualization software, vSAN and vSphere along with additional third-party products.

    Other new features in VMware vSphere include ESXi Single Reboot, which reduces patch and upgrade times by halving the number of reboots required to one, and vSphere Quick Boot, which will skip hardware initialization steps to gain further re-start efficiencies.

    VMware is also adding support for persistent memory to speed vSphere performance and adding support for graphics processor units (GPUs).

    There’s also now support for both Trusted Platform Module (TPM) 2.0 Support and Virtual TPM 2.0, which serves to protect virtual machines and hosts from being tampered or compromised by the loading of unauthorized components or allowing a guest operating system running on the hypervisor to circumvent existing security controls.

    Finally, VMware is making available an instance of VMware vCenter management software running on a dedicated appliance.

    Adams said VMware today has 500,000 customers running VMware vSphere, compared to 10,000 that have elected to also run VMware vSAN storage software. The goal for the company is to enable more IT organizations to embrace hyperconverged infrastructure by moving storage software on to the same server where VMware vSphere runs. That approach dramatically reduces the total cost of operations for most organizations, he said.

    As VMware makes an arguably belated push into the public cloud, the unit of Dell Technologies is counting on a massive installed base of on-premises customers to move and expand their existing investments in VMware vSphere on public clouds. While there’s no doubt that approach will appeal to significant part of the VMware installed base, many customers have already invested in the hypervisor platforms used by AWS, IBM and Microsoft.

    When it comes to lifting and shifting an existing legacy applications into the public cloud, VMware clearly has the inside track. But when it comes to development of greenfield cloud-native applications, VMware vSphere is not often the first choice of application developers, many of which view VMware vSphere as part of an inflexible on-premises legacy IT environment that they deliberately left behind.

    To its credit, however, VMware is making significant strides in embracing advanced automation technologies that one day soon might put those developer concerns to rest once and for all.

    — Mike Vizard

  • Akamai Extends Scope of DevSecOps Reach

    Akamai Extends Scope of DevSecOps Reach

    Akamai pressed its case for becoming a strategic DevSecOps platform by adding a range of security capabilities to its network service platform that help isolate applications from cybersecurity threats.

    The company has added support for scripting to automate application security using its Kona Site Defender web application firewall as well as improved management and versioning support for application programming interfaces (APIs). Those capabilities complement an Akamai API Gateway for securing and managing APIs.

    Akamai is also updating Akamai Bot Manager, adding a client SDK for mobile applications to help mitigate distributed denial of service (DDoS) attacks. The company also is adding capacity to its scrubbing centers to further defend against large-scale DDoS attacks.

    Ari Weil, vice president of product marketing for Akamai, said the company is evolving into a platform that securely provides web applications with access to caching software and network infrastructure that dynamically scale up and down as required.

    The goal is to make the Akamai network a seamless extension of any DevOps processes that developers can programmatically invoke on demand regardless of whether their applications reside in a public cloud, on-premises or in a third-party hosting center, said Weil. Because Akamai sits between users and production applications, Akamai now plays a critical role in terms of bother DevOps and DevSecOps, he said.

    In term of accomplishing that goal, Weil conceded Akamai doesn’t have as much visibility in the DevOps community as it would like. The company has integrated its platform into continuous integration/continuous development (CI/CD) platforms such as Jenkins to reduce as many steps as possible for DevOps teams. But developers are not always fully cognizant of the security services that Akamai makes programmatically available, he said.

    Weil noted that as DevSecOps continues to gain ground, Akaimai expects many developers will increasingly appreciate those security services, especially as cyberattacks become both more frequent and lethal. A 2017 security report published by Akamai finds DDoS and web application attacks rose 14 percent and 10 percent, respectively, year over year.

    In effect, Akamai turns web application security into a large-scale zero-trust managed service that is available on thousands of points-of-presence (PoPs) around the world. In time, developers will only have to express their intent concerning the level of security to be applied. The execution of that intent will all be automated, Weil said.

    In an ideal world, all cybersecurity battles would be fought as far away from applications as possible. Rather than focusing solely on the edge of the corporate network, Akamai is encouraging DevOps teams to think in terms of layers of defense that starts where applications are cached at the edge of the network. In theory, platforms such as Akamai provide a lot more visibility into what types of cyberattacks are circulating in the wild hopefully long before they ever get a chance to impact an application. The challenge, however, lies in distinguishing between what security technology needs to be embedded in the application itself versus an on-demand service that’s only a simple API call away.

    — Mike Vizard

  • Google Taps Netflix for Open Source Canary Analysis

    Google Taps Netflix for Open Source Canary Analysis

    Google and Netflix today jointly announced an open-source automated canary analysis service, dubbed Kayenta, that applies statistical analytics to optimize DevOps processes.

    Andrew Phillips, a product manager for Google Cloud Platform (GCP), said goal is to make it easier to consume advanced analytics by integrating Kayenta with the open source Spinnaker continuous delivery pipeline platform developed by Google. However, because Kayenta is based on software developed by Netflix, an IT organization can feed data from any cloud or on-premises IT environment into the canary analysis service, said Phillips. Kayenta also can ingest data from a broad range of monitong tools, including Stackdriver, Prometheus and Datadog.

    Phillips said the goal is to continually iterate on Kayenta to make it simple enough for almost any organization to take advantage of canary analysis, which enables DevOps teams to track metrics such as memory usage of a critical component. Kayenta fetches user-configured metrics from their sources, runs statistical tests and provides an aggregate score for the canary. Kayenta can automatically promote or fail “the canary” or trigger a request for human approval based on those test results.

    Specifically, Kayenta creates a pair of control/experiment time series datasets and calls a canary judge, which performs statistical tests, evaluating each metric individually and returns an aggregate score from 0 to 100 using preconfigured metric weights. Depending on the configuration, the score is then classified as “success,” “marginal” or “failure.” Success promotes the canary and continues the deployment, a marginal score triggers a human approval path, and a failure triggers a roll back.

    The basic canary concept involves routing a small subset of production traffic through a deployment that reflects the changes (the canary) and a newly deployed instance that has the same code and configuration as production environment, which serves as the baseline for testing. The analytics engine then compares the metrics between the canary and the baseline to identify any significant impacts the changes might have on the production environment. Kayenta in effect provides a mechanism for modeling risks based on proposed changes to the production environment, said Phillips.

    Google and Netflix expect third-party vendors will soon be incorporating Kayenta into a variety of DevOps tools and cloud platforms. In the meantime. Google views Kayenta as one more way of enticing DevOps teams to take advantage of Spinnaker to manage their DevOps pipelines in the cloud.

    Historically, setting up a canary analysis system has been a complex endeavor requiring significant engineering skills. Google is moving to democratize canary analysis by making it available as a service that can be invoked on demand, said Phillips.

    Phillips said Kayenta provides access to data that can be used to drive a wide range of predictive analytics applications that can be used to automate DevOps processes. It will be a while before Kayenta data is used to drive artificial intelligence (AI) models to automate those DevOps processes.

    Given the rate at which applications are being added and updated in production environment relying on manual testing is quickly becoming unfeasible. The days when DevOps teams needed to manually create their own statistical analytics models are clearly coming to a merciful end.

    — Mike Vizard

  • NGINX Extends Microservices Ambitions

    NGINX Extends Microservices Ambitions

    NGINX has extended the reach and scope of a namesake application platform that the company is positioning as a bridge between legacy applications and emerging microservices architectures.

    The NGINX Application Platform now includes NGINX Unit 1.0, an open source application server designed to simplify microservices across multiple application types simultaneously using a single instance of a server. It supports Go, Perl, Python, PHP and Ruby, with additional support planned for Java and JavaScript support soon. Sidney Rabsatt, vice president of product management at NGINX, said NGINIX Unit 1.0 is different from other application servers in that it is configured via an application programming interface (API) and is 30 percent faster than existing app servers.

    NGINX in the second quarter plans to add new monitoring dashboards, alerting and management via NGINX Controller R1 offering that provides the capabilities of a service mesh for microservices without having to set up a separate dedicated framework. Service meshes are a critical capability because they provide a means to manage interdependent microservices at scale.

    Finally, NGINX Plus R15 for enterprise customers builds on new HTTP/2, gRPC and JavaScript features that have been added to the core open source NGINX platform to provide support for high-availability clustering and OpenID Connect capabilities. These capabilities extend the ability to deploy NGINX as a web server, load balancer, API gateway, Kubernetes Ingress controller and sidecar proxy, said Rabsatt.

    Rabsatt noted most IT organizations do not want to set up separate infrastructure for managing microservices. Managing legacy and microservices-based applications in DevOps environments will be a fact of life for years to come, he said, adding that based on the number of downloads of NGINX software being used on Kubernetes platforms running Docker containers, that message is already resonating.

    NGINX claims users have pulled a billion NGINX instances from the Docker Store, including more than a million pulls of the NGINX Kubernetes Ingress controller. According to the company, more than 3 million NGINX instances are deployed in production microservices environments across more than 250 customers. That’s critical for the company because some rivals have positioned service meshes as an existential threat to the NGINIX platform. Overall, NGINIX says there are more than 11 million instances of NGINX are downloaded each month, an average of four instances every second.

    Rabsatt said most developers have made it clear they don’t have the patience to wait for IT operations teams to stand up dedicated microservices infrastructures. Most IT operations teams still don’t have the skills required. NGINX provides the ability to deploy microservices at scale using a platform most developers already know how to programmatically invoke on their own, he said.

    At the same time, NGINX is also committed to making available best practices that DevOps teams can employ when trying to manage what amounts to a new type of hybrid computing environment spanning legacy applications and microservices, he added.

    It remains to be seen exactly where the divide between microservices and legacy applications will be laid down inside most organizations. Some will draw a proverbial line in the sand, while others will look to deconstruct existing applications into series of microservices. Regardless of the approach taken, all those existing legacy applications will not be disappearing anytime soon.

    — Mike Vizard

  • DevOps and Cloud: A Symbiotic Relationship Aimed at Business Success

    DevOps and Cloud: A Symbiotic Relationship Aimed at Business Success

    DevOps and cloud computing are not mutually exclusive, though their relationship has often been uncertain and confusing from a layman’s perspective. While cloud computing relates to technology and services, DevOps is more about processes and process improvement. However, both DevOps and cloud computing are vital in every organization’s journey toward an effective digital transformation.

    Cloud-centric DevOps Automation

    Cloud computing’s centralized nature enables a standard and centralized platform for DevOps automation. Using a cloud platform resolves issues in centralized software deployment caused by the distributed nature of enterprise systems.

    DevOps objectives including continuous integration (CI) and continuous delivery (CD) are supported by most of the private and public cloud service providers. This ensures centralized governance and control, in addition to reducing the costs associated with DevOps automation on-premises. DevOps utilizes orchestration systems that can proactively monitor the cloud data sets and application workloads. End-to-end automation is a prerequisite for deriving the full advantage from the cloud.

    By expanding the concept of continuous improvement to cloud-based platforms, DevOps ensures lesser system vulnerabilities and greater security.

    DevOps Best Practices for Effective Cloud-Based Computing

    DevOps provides several core capabilities that assist organizations in effectively handling cloud challenges. These include:

    • Infrastructure as Code (IAC): IAC comprises the provisioning of servers and installation of application code, the core components of the system architecture.
    • Automated Application Deployments: DevOps practices facilitate the creation of a fully automated application deployment pipeline that is of great importance in the development life cycle.
    • Application Lifecycle Management (ALM): DevOps principles such as continuous integration and delivery help in effective ALM in the cloud.
    • Continuous Quality Assurance (QA) and Testing: DevOps capabilities address the challenges for QA and testing in the cloud by provisioning multiple test environments using cloud-based resources that result in enhanced quality and productivity.
    • Knowledge-Sharing with the Cloud Provider: DevOps promotes the concept of knowledge sharing between organizations and SMEs of the cloud service providers throughout the application life cycle using online communities that are focused on fostering communication and collaboration, thereby ensuring that most risks are mitigated effectively.

    The Significance of Cloud Computing vis-á-vis DevOps

    Cloud computing complements DevOps in several ways. The application-specific infrastructure in the cloud enables developers to own more components easily and enhance their productivity and efficiency. It allows developers to create the development environments quickly without the need for IT operations to provision resources and infrastructure.

    Cloud computing ensures flexible IT infrastructure in addition to enabling greater business agility. It propels IT transformation by helping organizations in streamlining and embedding DevOps processes.

    DevOps tools and services eliminate human error and help in establishing repeatability by automating and streamlining the process of building and managing code.

    IT Transformation Enabled by DevOps with Cloud Technology

    Cloud computing coupled with DevOps ensures that scalability becomes an integral part of application development, thereby contributing to reduced infrastructure costs and increased global reach.

    Cloud-based operations increase the availability and failover ability of applications in addition to eliminating downtime, which leads to improved business reliability and rise in customer satisfaction.

    DevOps automation and IAC help in reducing the complexity of the cloud and improving system maintenance. Additionally, the automated repeatable processes increase security controls. Process streamlining and faster access to the development environments ensure improved product time to market.

    In Conclusion

    By realizing the importance and prioritizing the usage of DevOps in the cloud, organizations can derive a huge number of potential benefits including enhanced agility and reduced operational costs.

    — Veritis

  • VMware Adds Analytics to Automate IT Operations Based on Intent

    VMware Adds Analytics to Automate IT Operations Based on Intent

    VMware is moving to make it easier for organizations to automate data centers and public cloud based on its core hypervisor technology by injecting advanced analytics into its VMware vRealize Suite, enabling them to manage IT environments based on intent rather than having to manually manage every process themselves.

    At the core of those update is the Capacity Analytics Engine, which is being added to version 6.7 of vRealize Operations. Mahesh Kumar, senior director of product marketing for cloud management at VMware, said the capacity analytics engine makes use of Autoregressive Integrated Moving Average (ARIMA) techniques to forecast future system events based on historical data points that identify a long-term trend of sudden spikes in usage. Armed with that data, vRealize Operations can now provide cost analytics that include advice on ways to adjust application size and reclaim idle resources on either a public cloud or inside a local data center. IT organizations can also run “what if” scenarios, Kumar said.

    On top of that capability, VMware is also now adding predictive analytics that enables IT organizations to define a set of intents such as workload balancing to reduce software license cost, prioritize one set of application to maintain a service level agreement (SLA) or make sure certain workloads only run on servers that have been shown to comply with a specific regulation or mandate. Tighter integration between vRealize Operations 6.7 and vRealize Automation 7.4 then can be continuously employed to automate DevOps processes in a closed-loop manner at higher levels of scale without having to add additional headcount, Kumar said.

    In addition, the latest version of VRealize Automation includes more than 120 free, curated blueprints and OVF (Open Virtualization Format) files out of the box. VMware has teamed with Bitnami to add 20 new blueprints and 100-plus new OVFs of applications and databases including GitLab, Hadoop, Jenkins and MongoDB to speed application development and deployment. VMware also updated the Custom Form Designer and added multitenancy capabilities.

    Finally, VMware vRealize Operations is sporting a new user interface and integrates with VMware Wavefront monitoring software.

    Longer term, Kumar said vRealize Operations will be where VMware applies machine learning algorithms to enable artificial intelligence (AI) models to automate the management of VMware environments.

    Collectively, Kumar said the VMware vRealize Suite moves IT organizations that have standardized on VMware in both their local data centers and in the public cloud much farther down the DevOps maturity curve. In effect, VMware vRealize Suite creates an “easy button” for managing complex VMware environments spanning multiple clouds, Kumar said.

    VMware is also betting that AI models will make it a lot easier for organizations to make a cultural adjustment as the management of compute, storage and networking becomes more unified. It’s not clear how long it will take for those machine learning algorithms to manifest themselves in VMware management software. But it’s clear most IT organizations running VMware soon will need to rethink who is responsible for running what inside an IT operations team.

    — Mike Vizard

  • IBM Unfurls Cloud Platform for Building Microservices

    IBM Unfurls Cloud Platform for Building Microservices

    IBM has unveiled a new development environment in the cloud designed to make it easier to build microservices.

    John Duimovich, IBM CTO for Java, said Microclimate provides a complete framework building applications based on microservices written in Java, Node.js or Swift.

    Microclimate runs on an instance of IBM Cloud Private, a private cloud based on Docker containers that is hosted on the IBM public cloud. Once those applications are developed, organizations can either deploy them on-premises or in a public cloud of their choice.

    Microclimate makes use of Kubernetes and Docker containers to create and orchestrate the microservices development process, which can quickly become a complex endeavor. Because of the issue, Duimovich said, more organizations will be pushing development of microservices into cloud computing environments that provide access to what amounts to a turnkey application development pipeline.

    Duimovich noted it’s possible to more closely mirror the production environment an application will be deployed on using compute resources that can be invoked on demand. Instead of developing an application on a laptop, developers can stress test microservices in the cloud by, for example, engage in “chaos monkey” testing, wherein developers allow a microservice to fail to see how the overall application reroutes requests to other microservices, said Duimovich.

    Developers also can engage in A/B testing in a way that would not be economically feasible to engage in when applications are developed on-premises simply because of the amount of additional IT infrastructure that would have to acquired and provisioned, said Duimovich.

    The cloud also makes it easier for teams of developers to collaborate on the development of microservices-based applications regardless of geographic location, Duimovich added. Developers may still choose to write code on their local PC, but when it comes time to load it into an application, the Microclimate platform makes it easier for everyone on the team to observe how that microservice behaves within the context of a larger application.

    IBM will also make available diagnostics tools to enable developers to more easily discover why their applications might be behaving in a certain way.

    While not every application may lend itself to being constructed using microservices, many organizations are now concluding they are simply a better way to develop reliable software. In addition, developers can be given ownership of specific sets of microservices end to end, which not only gives them more control but also makes them accountable for their overall quality. Of course, building applications made up of hundreds of microservices is going to require organization to implement DevOps best practices, which Duimovich said IBM is more than willing to share with customers.

    No one knows for sure when most applications running in a production environment will be made mainly of microservices. But given the rate at which they are now being used to develop applications, it’s now only a matter of time before such applications become dominant.

    — Mike Vizard

  • Google Unveils Suite of APM Tools

    Google Unveils Suite of APM Tools

    Google today announced that it is making available application performance management (APM) tools based on the same technology it employs to manage its internal IT environment.

    Morgan McLean, a product manager at Google, said Stackdriver APM is based the open source StackDriver Trace and Debugger tools Google originally developed for its DevOps teams.

    The APM offering, available in beta, also includes Stackdriver Profiler, a tool Google developed to enable DevOps teams to explore how their code is running in a production environment.

    Google also said it has integrated Stackdriver Debugger with GitHub Enterprise and GitLab to extend the number of repositories developers can use to create mirrors of their code.

    DevOps teams will have the option of deploying Google APM tools either on-premises or on a public cloud.

    McLean noted that rather than throwing additional compute resources at a performance issue, Stackdriver Profile enables DevOps teams to discover and remediate issues and ultimately reduce costs. To minimize the impact of Stackdriver Profile on the performance of a production application, Google opted to employ statistical methods to create a profile of a piece of code. Specifically, Stackdriver Profiler collects data via lightweight sampling-based instrumentation that runs across every instance an application. Data showing CPU time, wall time, RAM used and contention, for example, is then displayed on a flame chart.

    At the core of the Google APM toolkit is a the Stackdriver Trace software the company developed to collect latency data about applications deployed in a distributed computing environment. McLean said that in the age of microservices it’s not feasible to managed modern distributed applications without any ability to instrument them. For many IT organizations, however, the cost of acquiring APM software or employing a cloud service has forced them to limit their use of APM technologies to only their most critical applications.

    Google is trying to remove cost as significant APM factor, which ultimately will result in the applications deployed on the Google Cloud Platform (GCP) becoming more robust and reliable. The Google APM tools can be applied to applications running on virtual machines and containers, as well as the Google AppEngine, a framework Google exposes on its cloud service to enable the development and hosting of applications. In effect, Google is making it easier for organizations to consume open source tools that IT operations previously would have had to stitch together on their own.

    Reliance on APM tools is at the heart of any DevOps process. But DevOps processes are not universally employed, largely because in addition to acquiring new tools, organizations must evolve their processes and culture. The debate many of them have comes down to an argument over whether they need to buy tools before they change those processes or change their processes to justify buying the tools. Most often, it’s the initiative of handful of developers that force the issue. It’s only once that team is successful that senior managers start to think about replicating the DevOps process employed by that team across the rest of the organization. Via Stackdriver APM, Google is clearly hoping to accelerate the rate at which those transition to DevOps occur.

    — Mike Vizard