Cloud Design Patterns are the starting point to create scalable and secure applications. It comprises of combining several cloud services to accomplish the goals. Design patterns are moreover guidelines to use different types of cloud services and configurations. The primary emphasis is on making services and tools available to the cloud users that delivers with a difference. Accurate use of Cloud Design Patterns channelizes the effective implementation of the strategy. Cloud designs help us to delineate our approach.
Which are the widely used Cloud Design Patterns?
It is not important what is widely used, select the patterns that elevate the user experience.
Reasons to use Cloud Design Patterns:
- Enhance cloud architecture
- Ease migration
- Building scalable applications
- Brings consistency across the lifecycle
- Zero maintenance of deployed applications
- Increased overall productivity
Considerations for Cloud Design Patterns:
- Better network utilization and network expandability
- Define and meet cloud design objectives
- Fixed and variable costs involved
- Focus on speed and performance
Types of Cloud Design Patterns:
Discussing Patterns When to use and Resolution.
Ambassador: Acts as a proxy to monitor performance. Services send network requests on behalf of an application. Use it for a single instance or several clients. Build a common set of connectivity features for multiple frameworks or languages.
Anti-Corruption Layer: This layer will translate received from one subsystem raised to another subsystem. This pattern is useful to create communication between two systems, keeping one system unaffected. It makes sure to maintain the transaction and data consistency. Helpful in the migration plan spread over various stages.
Asynchronous Request-Reply: Where backend processing is complex and frontend response needs to be crystal clear. Useful when service calls need to be integrated with legacy architectures that do not support prevailing callback technologies. The application makes a synchronous call to API to initiate backend operation and it responds to return appropriate code.
Backends for Frontends: Create one backend per user interface and consider how many backends you need to deploy. This cloud design pattern lets many interfaces interact with the backend but is not effective when they make the same requests. Instead of creating a new backend, you can continue to use the existing backend for multiple services.
Bulkhead: Isolate the affected service if an issue is traced. It allows you to preserve some functionality. While other features of the application work uninterruptedly. Isolate the critical consumers from the standard ones. Separate the services in bulkheads by deploying them into distinct processes, virtual machines, or containers.
Cache-Aside: To load data from the data store to the cache. It improves performance as cache allows speedy and repetitive access. If demand is unpredictive and the cache does not provide read-through, write-through operations. Solutions to prepopulate the data that applications might need except when the data set is static.
Choreography: Decentralized control enables each component to be part of decision making related to business operations. Service is independent of a central orchestrator. This pattern is beneficial if you need to add, remove, or update new services often. Each service completes an operation but is completely unaware of the whole workflow.
Circuit Breaker: Improves stability of an application as it handles faults that take time to recover from. It acts as a proxy for operations likely to fail and also maintains the number of recent failures. It prevents remote access if the operation is likely to fail.
Claim Check: Large messages are split into smaller processes for improved efficiency and to protect the message bus. Bandwidth consumption becomes manageable. Bus service notifies and the plugin makes it possible to read the message from Blob Storage. Use it to define a pattern for smaller messages to skip and process only large messages.
Compensating Transaction: This pattern undoes the series of steps performed and maintains operational consistency. Applying this requires precautionary measure such as not to lose data. The connecting steps have a sequence and therefore should be cancelled in that order. Recommended for applications that run on the cloud and incessantly modify data.
Competing Consumers: To handle a huge number of requests received from manifold tenants that are unpredictable. Design messages are deliverable to all the consumer instances; eliminating order dependency to run it parallelly. Best for the tasks that do not need to be run in a particular sequence.
Compute Resource Consolidation: Consolidation of multiple tasks as a single computational unit reduces costs. It runs its own independent virtual environment. Not advisable to use for processing highly sensitive data. The group features provided by the environment based on criteria and costs.
CQRS: Command and Query Responsibility Segregation improves performance and scalability. Queries do not modify the database on their own. CQRS reads and writes the workloads independently. Higher security is due to the selective entities performing writes on the data.
Deployment Stamps: Stamps are the multiple independent copies of application components deployed across various regions. It allows to isolate the customer data or group resources as a different set of infrastructures. Resource Manager Templates can help to define the stamp. This pattern works powerfully if each stamp is addressed separately.
Event Sourcing: Append-only store to record the complete series of events describing actions taken on data-driven by events. Capture specific events that do not directly update the datastore but records it to handle at a suitable time. An audit trail can be of use to track and monitor actions.
External Configuration Store: Deployment of the package to a central location provides better control of configuration data. The challenge is to manage changes to local configurations in multiple locations. Suitable for a standard configuration that does not support the storage of complex data types.
Federated Identity: It authenticates an external identity provider. The identity provider manages the identities. Applications that integrate with third-party services wherein the shared resources and various IT systems have merged. It simplifies the development as it allows users to authenticate identity providers in return it reduces the administrative work.
Gatekeeper: A dedicated host serves as a broker to validate and pass requests between clients and applications. Avail sufficient security to run the application with this pattern. It automatically rejects the requests that fail to meet validations.
Gateway Aggregation: It clubs multiple requests made to the backend in order to perform an operation. The gateway processes each request by forwarding it to various backend systems. Improves performance of high latency networks. It’s useful when a client needs to communicate with several backend services.
Throttling: Set the maximum capacity before and after autoscaling. Check how actively is the service used as it affects the responsiveness and availability of the application. This pattern controls the consumption of resources by an application.
Valet Key: Introduces a high level of security to restrict access from a public connection using token/valet key. It permits access for a specified period that too for a specific resource. Effective to offload data transfer from the application.
Boost Cloud Architecture using Cloud Design Patterns:
- Automate administrative tasks
- Centralize control over administration
- Workload balancing across virtual servers
- Service load balancing
- Elastic disk provisioning
- Resource availability in real-time
- Organized resource pooling for greater support
- Reserved access of resources for protected sharing
- Achieve zero downtime
Cloud design patterns offer a huge selection, try to opt for apt patterns. Explore other design patterns such as Geodes to serve client requests of different regions, Leader Election collaborates tasks to manage other instances and pipes and filters are to divide tasks for the complex processing.