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As a result of cloud scalability and the dramatic gains in efficiency it enables, companies are discovering best practices for cloud-native application development. These best practices have been translated into cloud architecture patterns designed to solve problems in cloud-native applications.
Let’s examine six popular cloud architecture patterns and discuss the ones best suited for resilient chip design.
1. Horizontally Scaling Compute Pattern
In this pattern, compute nodes are horizontally scaled to utilize cloud resources effectively and increase operational efficiency. This pattern can be leveraged to ensure applications are allocated resources in a cloud-native manner. Potential benefits for applications using this pattern include enhanced scalability, availability, cost optimization, and user experience.
2. Queue-Centric Workflow Pattern
This pattern focuses on the asynchronous delivery of command requests sent from the user interface to a back-end service for processing. You can use this pattern to decouple application tiers, especially between the web user interface and service tiers. Messages are queued and communicated from the web tier to the service tier in one direction. Reliable cloud queue services simplify implementation.
3. Auto-Scaling Pattern
This pattern makes horizontal scaling more practical and cost-effective by automating routine scaling activities for greater efficiency and cost optimization. Cloud-native applications can easily handle the dynamic increase or decrease in resource levels.
4. Database Sharding Pattern
This pattern focuses on horizontally scaling data through sharding (dividing up data from a single database across two or more databases). Using this approach, you can overcome size, query performance, and transaction throughput limitations of traditional single-node databases. With managed sharding support, the economics of sharding a database become favorable.
5. Node Failure Pattern
This pattern addresses application response when a compute node shuts down or fails. You can use this pattern to prepare, handle, and recover from occasional disruptions and failures of compute nodes where your application is running. A cloud application that does not account for node failure scenarios will not be reliable.
6. Multisite Deployment Pattern
This advanced pattern focuses on deploying a single application to more than one data
center to improve the user experience for geographically dispersed users. To improve performance, users should be distributed so that more than one data center provides sufficient value. This pattern is also helpful for applications requiring a failover strategy if one data center is unavailable.
|Cloud Architecture Patterns||Description||Benefits|
|Horizontally Scaling Compute Pattern||Horizontally scaling compute nodes for efficient utilization of cloud resources and operational efficiency||Availability, cost optimization, scalability, user experience|
|Queue-Centric Workflow Pattern||Asynchronous delivery of command requests sent from the user interface to a back-end service for processing||Availability, reliability, scalability, user experience|
|Auto-Scaling Pattern||Automating operations to make horizontal scaling more practical and cost-effective||Cost optimization, scalability|
|Database Sharding Pattern||Horizontally scaling data through sharding (allot data from a single database across two or more databases)||Scalability, user experience|
|Node Failure Pattern||Specifies how an application should respond when the compute node where it is running shuts down or fails||Availability, user experience|
|Multisite Deployment Pattern||Deploying a single application to more than one data||Availability, reliability, scalability, user experience|
The top cloud architecture design principles important for chip design and verification include operational excellence, reliability, efficiency, cost optimization, and security. While none of the cloud architecture patterns discussed above provide all these benefits, the two that come the closest are the Horizontally Scaling Compute Pattern and the Multisite Deployment Pattern.
The Horizontal Scaling Compute Pattern deals effectively with the following issues:
This pattern is often combined with the Node Termination Pattern (which addresses concerns when releasing compute nodes) and the Auto-Scaling Pattern (which involves automating processes).
The Multisite Deployment Pattern deals effectively with the following issues:
Once you have chosen the best cloud architecture pattern, you can leverage it for various chip design and verification tasks.
Cloud-based electronic design automation (EDA) tools can be used to design and verify chips. They have traditionally been optimized for local on-premises infrastructure, requiring substantial investments to succeed. Yet, the benefits of moving them to the cloud outweigh the technical and economic challenges.
In a well-architected cloud deployment, chip designers can deploy their applications quickly and efficiently. As a result, the cloud is often the preferred choice for startups or firms needing increased computing capacity quickly.
Synopsys is the industry’s largest provider of electronic design automation (EDA) technology used in the design and verification of semiconductor devices, or chips. With Synopsys Cloud, we’re taking EDA to new heights, combining the availability of advanced compute and storage infrastructure with unlimited access to EDA software licenses on-demand so you can focus on what you do best – designing chips, faster. Delivering cloud-native EDA tools and pre-optimized hardware platforms, an extremely flexible business model, and a modern customer experience, Synopsys has reimagined the future of chip design on the cloud, without disrupting proven workflows.
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