Google Kubernetes Engine

Website

cloud.google.com

Categories

#Developer Tools #Containers As A Service #DevOps Tools #Cloud Computing

Amazon Web Services Lambda

Website

docs.aws.amazon.com

Categories

#Development #Cloud Computing #VPS #Cloud Infrastructure

Google Kubernetes Engine features and specs

• Managed Service
  GKE is a fully managed service, which means Google takes care of tasks like provisioning, maintenance, and updates of the cluster, reducing the operational burden on users.
• Scalability
  GKE offers robust scalability options, allowing you to easily scale your applications up or down based on demand. This is facilitated through auto-scaling features for both nodes and pods.
• Integration with Google Cloud Services
  GKE integrates seamlessly with other Google Cloud services such as Cloud Storage, BigQuery, and more, providing a streamlined experience for leveraging multiple cloud tools.
• Security
  GKE offers advanced security features like private clusters, and integrates with Google Cloud IAM, which allows for fine-grained access control, helping to secure your Kubernetes environment.
• Ease of Use
  GKE's comprehensive dashboard, command-line interface, and supporting documentation make it easy to deploy, manage, and monitor Kubernetes clusters.
• Global Reach
  With GKE, you can deploy clusters across multiple regions and zones, giving you the ability to build highly available, geographically dispersed applications.

Possible disadvantages of Google Kubernetes Engine

• Cost
  While GKE offers extensive features, it can be more expensive compared to other Kubernetes solutions, especially when additional services and high-availability features are utilized.
• Limited Customization
  As a managed service, GKE has some limitations in terms of customization and control over the underlying infrastructure compared to self-managed Kubernetes environments.
• Complexity
  Despite its ease of use features, GKE still requires a certain level of expertise to efficiently manage Kubernetes clusters, which can be a steep learning curve for beginners.
• Dependence on Google Cloud
  Using GKE ties you to the Google Cloud ecosystem, which may limit flexibility if you decide to migrate to a different cloud provider or adopt a multi-cloud strategy.
• Resource Constraints
  Like all cloud services, GKE nodes can be subject to resource limits and quotas imposed by Google Cloud, which can impact performance if not properly managed.
• SLA and Downtime
  While Google Cloud offers Service Level Agreements (SLAs), there is still a risk of downtime which could affect your applications. Additionally, relying on a third-party provider means issues may take time to resolve.

Amazon Web Services Lambda features and specs

• Scalability
  AWS Lambda automatically scales your application by running your code in response to each trigger, handling scaling from a few requests per day to thousands per second.
• Cost Efficiency
  Lambda charges you only for the compute time you consume, without the need to pay for idle server time, which helps optimize cost efficiency.
• No Server Management
  You don't have to manage any servers or infrastructure, allowing you to focus on writing code and developing functionality rather than maintaining hardware.
• Event-driven Model
  Lambda integrates seamlessly with other AWS services and can be triggered using AWS events, making it highly suitable for building event-driven and serverless architectures.
• Flexible Scaling and Processing
  AWS Lambda provides flexibility in resource allocation, allowing you to adjust memory and execution timings to optimize performance for different workloads.

Possible disadvantages of Amazon Web Services Lambda

• Cold Start Latency
  The first invocation of a Lambda function can experience a delay known as 'cold start,' which can affect applications sensitive to latency.
• Execution Timeout
  Lambda functions have a maximum execution timeout of 15 minutes, which may not be suitable for long-running processes or tasks.
• Resource Limitations
  Lambda has limitations on memory, disk space, and other resources, which can restrict the functions it can perform depending on application requirements.
• Vendor Lock-In
  Using AWS Lambda heavily can lead to vendor lock-in, making it challenging to migrate to different platforms without significant re-architecting.
• Complexity in Large Systems
  For large systems with numerous functions and dependencies, managing and orchestrating Lambda functions can become complex and challenging.

Getting Started with Containers and Google Kubernetes Engine (Cloud Next '18)

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