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The Software-as-a-Service (SaaS) delivery model has been garnering a lot of attention lately, thanks to the flexibility, scalability, security, cost-efficiency, and platform-agnostic support it brings to the table in diverse and challenging environments.
This blog gives you an empirical analysis of the factors that will influence and impact your SaaS design and architecture in the preliminary stages of its development and the long haul, irrespective of the stack of services it supports.
Your SaaS Architecture design must embrace these key factors.
Single-tenant or multi-tenant isolation offers many advantages when it comes to data and code portability, usage-based scalability, infrastructure components, and customized database & application management.
At the application level, a single URL is used by end-users to access an application, while automatic inference is made to individual tenants with the username, thus avoiding name clashes.
At the database level, the tenant ID is defined for a table by the SaaS provider. The tenant can access the relevant database with only the data, security features, and resources relevant to the tenant ID.
At the infrastructure level, only the hardware, files, programs, sessions, utility functions, and other computing resources/activities relevant to the tenant are mapped to the tenant account.
Here are some of the security considerations for your SaaS architecture design.
Implement controlled infrastructure access mechanisms and rules at different checkpoints.
This is achieved by the ideal implementation of authorization and authentication check tools, identity brokers, and identity providers (AWS Cognito).
Implement role-based access management, data segregation, and encryption.
Ensure that only the relevant information is accessible to end-users, and information flow is encrypted.
Auto-scaling uses load balancers to address issues with load distribution, server responsiveness, unexpected data influx, storage underutilization or overutilization, and continued availability.
Predictive scaling helps automatically scale the compute capacity in advance for traffic changes using ML technology.
Automatic scaling automatically increases the size of your Auto Scaling group when the demand goes up and vice versa. As the capacity changes, the EC2 instances being added or removed get registered or deregistered with the load balancer.
The AWS Elastic Load Balancer automatically distributes incoming application traffic across multiple targets like AWS EC2 instances, containers, IP addresses, and provider functions based on availability zones.
RDS Storage Auto Scaling automatically scales storage capacity in alignment to growing database workloads, with zero downtime or unutilized resources.
This service helps track tenant application performance, developer access to resources and authentication to services, system availability, and performance. Application performance monitoring using services like Cloudwatch, RDS DB Insights, cross-tenant event tracking, application logs, and data audit trail using services like AWS CloudWatch, and logs monitoring with AWS elastic search and Kibana leverage the application monitoring process.
The clients can gauge the availability of resources for better infrastructure utilization by the users logged in.
Monitoring using logs can capture multiple login attempts, unusual activity patterns, and data movement to report access-based incidents and implement auto-blocking.
The event history of your SaaS account activity, including actions taken through the Management Console, the SaaS SDKs, command-line tools, etc. monitors infrastructure performance in services like AWS CloudTrail.
DevOps is a de facto standard that ensures innovation, adaptability, visibility, closer collaboration, security, and agility in the CI/CD pipeline for development, testing, staging, production, auditing, reviewing, event logging and managing the entire project cycle seamlessly.
AWS CodePipeline, CodeBuild, and CodeDeploy are DevOps services from Amazon.
Here’s why DevOps is important for your SaaS architecture design.
Analytics provide insights to the customers so that data-driven business decisions can drive innovation and scale (AWS Quicksight is an analytics tool).
SaaS analytics can help with the following:
Billing in SaaS is done for tiers packaged and priced based on subscriptions (daily, monthly, or annually) and functionality-based package levels (basic, advanced, professional) based on the usage analytics of a tenant that is collected.
Resource utilization to a defined limit and throttling once the limit is reached is an auto-scaling strategy followed by SaaS providers. Some of the most effectuve throttling mechanisms include:
Here the tenant is charged for usage of the isolated infrastructure. The two key billing types include service provider accounts billing and VPC billing.
Here, each resource type may need a different cost aggregation approach for individual tenant pools. An activity-to-consumption mapping is essential to this model, while event logging for each tenant helps derive a cost equation.
Tenant consumption and activity metrics like bandwidth, storage usage, number of users, etc. are taken into consideration for this model.
The infrastructural resources package upgrade or downgrade should be flexible based on tenant sizing needs and approved activity levels. Calculations of compute consumption and analysis of storage costs are essential to determine the sizing and scale.
It is important to note that there is no “one size fits all” implementation for SaaS solutions. The unique needs of customers, vendors, businesses, and markets shape the strategy, tools, frameworks, patterns, practices, technology stack, etc. accounted for in a given environment.
