Use case

Migration from
Heroku to GCP

from heroku to gcp

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1. Motivation for migration to GCP

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1. Centralized Management:

By consolidating services from different platforms into a single GKE cluster, it helps in centralizing the management and monitoring of applications.

2. GPU Workloads:

GPU instances can be more expensive than CPU instances, so optimizing costs is crucial. GKE node auto-upgrade helps to keep the underlying GPU drivers up to date and give advantages of new features and bug fixes.

3. Infrastructure as Code / Automation:

GCP supports tools like Google Cloud Deployment Manager and Terraform for infrastructure as code. This enables to define the infrastructure using code, making it easier to automate deployments, manage updates, and ensure consistency across environments.

4. Scalability:

Managing increasing demands of the GPU application workload is complex and time-consuming. Google Cloud Platform provides robust scalability options for scale up or down by resizing virtual machine instances, using Kubernetes Engine for containerized applications.

5. Cluster Architecture:

Designing the architecture for GPU clusters, especially with Kubernetes Engine, requires careful consideration of pod scheduling, node scaling, network configuration, and resource quotas to ensure efficient utilization of resources. A multi-Availability Zone (AZ) GKE cluster is designed as it ensures high availability by spanning AZs and preventing single-point failures. GKE standard cluster with GPU based and CPU based nodepool.

2. Solution Design and Implementation

1. Application Deployment:

Containerized applications are deployed to the GKE cluster using Kubernetes manifests. Deployment is based on application workload and their requirements.

2. Networking:

VPC is used to isolate the GKE cluster, connecting via regional subnet with multiple AZs and controlling traffic with Firewall Rules.

3. Security:

Identity and access management (IAM) for precise control over permissions to GKE resources. While network policies for granular network segmentation, and encryption at rest and in transit to protect data integrity.

4. Automation:

IaC tool, Terraform is used to define and manage GKE infrastructure, ensuring consistent deployments and version control.
heroku to gcp

5. Cost utilization:

Spot VM instances within Google Kubernetes Engine (GKE) for GPU workloads for cost optimization, making it an optimal solution for managing GPU workloads efficiently.

6. Monitoring:

Using DCGM Exporter in conjunction with Prometheus within Google Kubernetes Engine (GKE) for GPU workloads enhances monitoring and performance optimization. It helps to gather critical metrics from NVIDIA GPUs, enabling detailed insights into GPU utilization, memory usage, and temperature. Integrating Prometheus allows to collect, store, and analyze these metrics, facilitating proactive resource allocation and workload management.

3. Summary

By consolidating services from different platforms like Heroku, GCP VMs and Azure VMs, IntelliProbe Solutions accomplished the integration of a GKE infrastructure with GPU and CPU base nodepools, enabling them to streamline the management and deployment of their containerized application through a centralized solution. The automation and monitoring capabilities provided by the infrastructure increased operational efficiency and reduced downtime, resulting in improved customer satisfaction and business growth.

Are you looking to Integrate your services with ease, just like IntelliProbe Solutions did. We can help you consolidate platforms, leverage the power of GKE with GPU and CPU-based nodepools, and centralize your containerized application deployment.

With our expertise in automation and monitoring, we’ll supercharge your operational efficiency, minimize downtime, and drive customer satisfaction and business growth to new heights.

Ready to streamline your infrastructure and
boost operational efficiency?

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