On April 6th, 2022, Google Cloud established a new partnership with CAST, to help accelerate the migration and application modernization programs of customers worldwide, complementing the Google capabilities already available through the Google Cloud Application Modernization Program (CAMP).

Application Rationalization (App Rat) is the first step towards a cloud adoption or migration journey, through which you go over the application inventory to determine which applications should be Retired, Retained, Refactored, Replatformed, or Reimagined.

Why is this important to you?

Have the majority of your in-house applications still not moved to the cloud? How much time does your development team spend on support (bug fix, tickets, etc.) versus feature(s) development? Have Infrastructure/Platform dependencies ever delayed product rollout? Would an auto-scalable, managed cloud, increase stakeholder buy-in?

Can Google simplify this journey?

Google Cloud Application Modernization Program (CAMP) has been designed as an end-to-end framework to help guide organizations through their modernization journey by assessing where they are today, and provide a path forward. When it comes to App Rationalization, this depends on what your role is.

Step 1 (Assess): Who is the target audience? The Platform team (or) the Application team?

This determines what kind of challenges we are trying to solve. For e.g. the centralized platform team wants to set some guardrails on how the App teams deploy their apps. Streamlining this would allow the platform team to mature themselves into the SRE territory. The application team, on the other hand, loves flexibility, and the ability to perform Continuous Delivery.

These examples are only the tip of the iceberg. Most of the enterprise customers have a majority of their applications in the legacy world. Unless we move those business critical applications to the cloud, it’s impossible to mature as an enterprise. For more information, check State of DevOps 2021 report.

Step 2 (Analyze): Google Cloud offers the tooling and the framework to analyze your legacy applications.

Platform Owner (persona), usually have very little information on which workloads are a good fit for modernization.

Google’s StratoZone® SaaS platform provides customers with a data-driven cloud decision framework. The StratoProbe® Data Collector Application delivers the ability to easily deploy and scale the discovery of a customer’s IT environment for Private, Public, or Hybrid-cloud planning. To ease and accelerate the VM migration journey, Google Cloud offers assistance and guidance in making the right decisions when deciding to go to cloud.

Google’s mFit aims at unblocking customers in their transformation by providing workload selection for successful on-boarding, at scale, to Anthos, GKE and Cloud Run , in both pre-sales (e.g. proof-of-concept/proof-of-value) and post-sales (e.g. pilot and at scale execution) scenarios.

App and/or Business Owners (persona), get involved in a 1-week workshop, using CAST Highlight, which would provide rapid portfolio assessment through automated source code analysis for Cloud Readiness, Open Source risks, Resiliency, and Agility.

Step 3 (Plan & execute): Each organization is different. Some may follow the “Migration Factory” approach, and some may follow “Modernization Factory”, and some may follow both. Irrespective of which approach you choose to follow, it is important to plan just enough, so that you can start your execution. Ensure to set the OKRs, that would help with the right measurements, before you start the execution. The actual learning from the execution helps the team(s) to learn more about the cloud migration process, and refine it based on their organization.

Using CAST Highlight in the assessment step previously, we get the recommendation for the analyzed applications. From there, for certain workloads, we can use Migrate to Containers, to automate the containerization of suitable workloads. However, there are certain applications that require manual code changes. You have a few options for that,

• Our experts can help you get started.
• Our partners can help you

Step 4 (Measure & reiterate): Measure the progress using the predefined metrics in the previous step. Celebrate the wins. Consistently share the learnings and best practices with the developer community. Pick the next challenge

Take the next step

Tell us what you’re solving for. A Google Cloud expert will help you find the best solution.

We’re thrilled to share the news that leading global research and advisory firm Forrester Research has named Google Cloud a Leader in the recently published report The Forrester Wave^TM: Public Cloud Container Platforms, Q1 2022. Forrester evaluated the container and cloud-native offerings of a select group of top public cloud container platform vendors across 29 comprehensive criteria. 

We are proud that Forrester evaluated the strength and cohesion of our offerings, including Google Kubernetes Engine (GKE), Cloud Run, Anthos, Cloud Build, Cloud Deploy, Cloud Code and more, writing that, “Google Cloud is the best fit for firms that want extensive cutting-edge cloud-native capabilities for distributed workloads spanning public cloud, private cloud, and multicloud environments.” 

Google Cloud received the highest possible scores in the criteria of service and application catalogs, microservice development support, service mesh support, serverless and FaaS support, DevOps automation, container image support, control plane configuration, hybrid cloud support, container networking, product vision, supporting products and services, market approach, revenue, and breadth of offering. We also achieved the highest score in the Strategy category of all the vendors evaluated.

Google Cloud is all-in on containers and cloud native 

The cloud-native tools and technologies created by Google Cloud are already powering the most innovative, scalable and secure apps around the world, from the most exciting digital natives to the most important enterprise industry leaders. Why? Cloud native means building and running modern apps that intentionally take advantage of the global scale, pervasive automation, elastic infrastructure, and secure resiliency of the public cloud. 

For enterprises, cloud native in practice means using containers, Kubernetes, serverless, and DevOps automation to build amazing customer-facing apps quickly, to modernize existing business-critical apps safely, and to operate them all on cost-efficient, powerful, and secure cloud infrastructure globally.Over the past decade, Google Cloud’s technology innovation has fueled various domains of the cloud native ecosystem, such as Kubernetes and Go languages as the foundation, Istio for service mesh, Kubeflow for machine learning, Knative for serverless, and Tekton for CI/CD. Long-term investment and practices in cloud-native power its superior product vision and excellent supporting products and services.The Forrester Wave: Public Cloud Container Platforms, Q1 2022

Dedicated to simplicity, speed, and scale for your modern apps

Our mission is to create, integrate, scale, and secure the best open source and commercial cloud-native technologies – backed by a consistent cloud control plane – so you can spend more time dreaming of ways to delight your customers and less time building and operating platforms. We are committed to leading in cloud-native open source communities and making containers and Kubernetes accessible to everyone, from everywhere. 

Here are a few recent highlights across our container services and tools, aimed at helping you build and modernize your most important apps with cloud native:

The most scalable fully managed Kubernetes service, Google Kubernetes Engine (GKE), achieved an overall solution score of 92 out of 100 in Gartner’s Solution Scorecard. In 2021, we introduced GKE Autopilot, a fully managed, security-hardened Kubernetes service optimized for production workloads. This unique mode of operation allows you to focus on your workloads while Google manages your cluster infrastructure. There’s nothing else like it. Then, we made GKE apps even faster with GKE image streaming. With proven scalability to 15K nodes in a single cluster and innovations such as four-way autoscaling, node auto-upgrades, integrated logging and metrics, cost optimization insights, native backup and restore, and multi-instance GPUs to accelerate machine learning workloads, GKE remains the best choice in managed Kubernetes services.

With Cloud Run, we expanded the range of users who benefit from containers to those who don’t know much about them. Introduced in 2019, Cloud Run combines the serverless attributes of autoscaling and developer experience with the flexibility of containers. Developers can use any language, runtime or binary, and deploy code using buildpacks to automatically build container images from source without worrying about provisioning machines and clusters. Cloud Run goes beyond FaaS and beyond earlier generations of serverless computing. Cloud Run runs more legacy workloads, integrates with Cloud Build for secure and compliant builds, offers deeper cost controls and billing flexibility, and encourages portability. We added support for social feeds, collaborative editing, and multiplayer games that use bidirectional streaming. Minimum instances reduce cold-start delays so you can run more latency-sensitive applications. And recently, we launched support for network file systems, allowing developers to share and persist data between multiple containers and services.

Anthos is at the heart of the Google Distributed Cloud, a portfolio of software and hardware solutions announced in 2021 that extend Google’s container platform services to the data center and the edge. Anthos is how we extend GKE to wherever you need cloud-native apps. Manage clusters on-premises on bare metal and VMware-virtualized servers, on AWS and Azure, and at the edge – all with a Google Cloud-backed control plane for consistent, automated operations at scale. We added a hosted service for configuration management to keep all your clusters in sync, and trimmed our installation footprint and streamlined cluster management with a new multi-cloud API that enables you to use a single API for full lifecycle management of Anthos Kubernetes clusters in AWS or Azure.

Finally, since no public cloud container platform is complete without powerful DevOps tools, we expanded our CI/CD offerings to make your developers even more productive, wherever they build and deploy cloud native apps. Use Cloud Code and Cloud Shell as your go-to cloud-native IDEs. Cloud Build is a fully managed serverless DevOps automation platform for use cases spanning CI/CD, Infrastructure-as-Code, AI/MLOps, and more, across infrastructure GKE, Cloud Run, Cloud Functions and more. Google Cloud Deploy is a fully managed continuous delivery service that provides one-click release promotion and roll-backs, built-in metrics, and out-of-the-box security. Artifact Registry and Container Analysis provide managed artifact repositories, vulnerability scanning, and help secure the software supply chain.Google Cloud has a solid cloud-native innovation roadmap, targeting simplicity at scale for enterprise clients.The Forrester Wave: Public Cloud Container Platforms, Q1 2022

We are delighted and humbled to be recognized as a Leader in public cloud container platforms by Forrester. Grab your copy of The Forrester Wave^TM: Public Cloud Container Platforms, Q1 2022 today and let us know how we can help you build and modernize your most important apps how you want and where you want.

Modern applications need to process large-scale data at millisecond latency to provide experiences like instant gaming leaderboards, fast analysis of streaming data from millions of IoT sensors, or real-time threat detection of malicious websites. In-memory datastores are a critical component to deliver the scale, performance, and availability required by these modern applications. 

Memorystore makes it easy for developers building applications on Google Cloud to leverage the speed and powerful capabilities of the most loved in-memory store: Redis. Memorystore for Redis standard tier instances are a popular choice for applications requiring a highly available Redis instance. Standard tier provides a failover replica across zones for redundancy and provides fast failover with a 99.9% SLA. However, in some cases, your applications will require more read throughput from a standard tier instance. One of the common patterns customers use to scale read queries in Redis is leveraging read replicas.

Introducing Memorystore for Redis Read Replicas

Today we are excited to announce the public preview of Memorystore for Redis Read Replicas, which allows you to seamlessly scale your application’s read requests by 6X with the click of a button.

With read replicas, you can easily add up to five replicas and leverage the read endpoint to automatically load balance read queries across all the available replicas, increasing read performance linearly with each replica added. Additionally, Memorystore’s support for Redis 6 introduced multi-thread I/O, increasing performance significantly for M3 and higher configurations. Combined, you can achieve read requests of more than a million requests per second. 

You will benefit from this new functionality in several ways. You will be able to scale on demand with up to five read replicas and use read endpoint with any redis client to easily load balance read queries across multiple replicas. This new functionality will also improve availability with automatic distribution of replicas across multiple zones. With read replicas, you will also be able to minimize application downtime with fast failover to the replica with the least replication lag. In the future, Memorystore will also easily enable read replicas on existing standard tier instances to increase read throughput. 

You can learn more about how to configure and use read replicas in the Read Replicas Overview. 

Improving performance with Read Replicas and Redis 6

With the launch of read replicas, you can easily increase the read throughput of a Memorystore instance. You can further enhance the read performance by leveraging Redis version 6 along with read replicas.

To understand why combining read replicas and Redis 6 can significantly improve your application’s read performance, let’s look at the various Memorystore configurations that you can use with your applications today. Memorystore Basic and Standard offerings  provide different capacity tiers. The capacity tier determines the single node performance of a basic and standard tier instance. 

The table below outlines the configuration of the various capacity tiers:

Up until version 5, Redis processed commands using a single thread. The processing involved reading the request, parsing the request, processing the request, and writing the response back to the socket. This approach means that all of the processing, which includes writing the response, was sequentially processed by a single vCPU regardless of the number of vCPUs available in the instance.

Redis 6 introduced I\O threading which allows writing the response using parallel threads. This functionality enables Redis 6 to more effectively leverage available vCPUs, thereby increasing the overall throughput compared to lower versions. Memorystore for Redis version 6 leverages I\O threading and automatically configures the optimal number of I\O threads to achieve the best possible performance for the various capacity tiers. We have also improved the overall network throughput for all redis versions by leveraging improvements in the Google Cloud infrastructure. Together these improvements deliver significant performance improvements and come at no additional cost to you. 

So what can you expect from using Redis 6? As outlined in the table, Redis version 5 and lower uses a single thread to process the write requests for all tiers while Redis 6 uses a larger number of I\O threads at higher capacity tiers which provides substantially incremental throughput for higher capacity tiers.

For example, using a Redis 6 standard tier instance with a capacity of 101 GB (M5), we have observed up to a 200% improvement in read/write performance compared to version 5, though the actual value you’ll see is dependent on your workload. You can get the benefits of Redis 6 by upgrading an existing instance or by deploying a new instance.

By enabling read replicas on an instance using Redis 6, you can further improve the read performance. Read throughput scales linearly with the number of replicas so you can increase your read queries on an instance by up to 500% using five read replicas. By combining Redis 6 and read replicas, you can see a 10X increase in read performance compared to a version 5 standard tier instance with no read replicas.

We are excited about the launch of read replicas but this is just one step in our journey to deliver the scale you need at the best price-performance. You can learn more about read replicas pricing on our Memorystore pricing page and get started using the Read Replicas Overview.