Well-Architected Framework Series – Part 3 – Reliability – Instigate reliability into your cloud workloads!

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Reliability Pillar

Well-Architected Pillar: Reliability

The ability of a product or service to perform virtuously and consistently over a definite period is directly dependent on well-defined principles, planned monitoring processes, and change management techniques. The reliability pillar of the well-architected framework (WAF) focuses on failure management and enables systems to dominantly and automatically recuperate from infrastructure, server downtime, security breaches, unexpected errors, software failures, network interruptions & delays.

The AWS well-architected framework comprises of substantial techniques to assess the cloud architecture and implement scalable designs based on the five pillars of operational excellence, security, reliability, performance efficiency and cost optimization. Today, we delve deeper into the reliability pillar, the fourth in this WAF series.

Building a strong foundation based on-premises architecture is quite demanding owing to lack of flexibility, adaptability, single points of failure, and prevailing manual practices. By applying the well-structured reliability pillar solutions on the cloud, infrastructure becomes more secure, resilient, and fault-tolerant, change management processes turn more consistent and workloads perform expected operations invariably and accurately.

Design principles for the reliability of cloud workloads

Reliability and high availability are the prime concerns in a distributed setup and the objective of the reliability pillar is to ensure cloud capabilities are operated and tested through its entire lifecycle to enhance the credibility of workloads on cloud. Following the path of well-defined design principles can definitely increase reliability on cloud:

  • Define application availability to meet business requirements: The first step is to understand workload consumption patterns and distinguish the level of application availability, as not all applications are in full capacity use. It is also crucial to identify dependent and redundant components to recognize fundamental single points of failure in order to ensure design capabilities incorporate mitigation strategies to address identified errors. Establishing application availability and failure recovery metrics such as mean time to recover (MTTR), mean time between failures (MTFB), recovery time objectives (RTO), and recovery point objectives (RPO) that are in line with SLAs, aid in curtailing redundancy levels, mitigation costs, and downtime risks.  
  • Identify, assess and automate failure recovery: Apply monitoring controls to detect all fault points and modes, categorize failure types, identify critical dependencies and analyze the potential impact of each failure to design a recovery strategy and build resilient applications on the cloud. Once these strategies are exercised, automating notification, alerts and failure tracking can be triggered to encourage automated remediation processes. Intelligent automation can further enable an advanced predictive analysis of failures even before they occur.
  • Evaluate and devise recovery strategies: Unlike the on-premise setup, testing and evaluation are conducted to manage workloads and validate recovery strategies on the cloud. Automation can be used to replicate or recreate failures that have occurred before in order to discover the exact failure points, which can then be tested and resolved before an actual failure occurs, minimizing risks.
  • Conceiving scalability and high availability: To avoid an entire system to fail, applying redundancy to SPOFs is a must. Every important component of an application should exist in multiple instances, where in the event of a disaster, effective backup and recovery techniques can be initialized to reduce downtime and minimize the impact of a single failure on the overall workload. Quick real-time failure detection through continuous monitoring, isolation of critical resources, offloading network traffic, and regulating predictive analysis of errors coupled with availability and performance evaluation of data storage services ensure high availability and reliability on the cloud. At the application level, it is vital to determine, auto-scale, and route the increase in workload requests via appropriate capacity planning to control any disruptions.
  • Monitor workload utilization: Measuring demand with respect to workload utilization and capacity monitoring helps balance optimal levels of resources in real-time scenarios. Under or over-provisioning can be maintained via automation techniques while also implementing alerting systems and end-to-end monitoring techniques to curb failures.

Best practice areas of reliability on cloud

  1. Foundations: Most of the leading cloud providers ensure that before architecting any workloads on the cloud, it is essential to set up an adequate network and compute capacity that facilitates on-demand allocation and sizing of resources. Service quotas in AWS, quotas on Azure portal or GCP’s service quota model, help monitor and manage default service limits to control the number of services provisioned as well as limit the rate of API requests for multi-cloud workload architectures. Amazon’s CloudFront, Route 53 and API Gateway, Azure Network Watcher, and GCP’s Network Intelligence Center module provides the entire network topology that encompasses end-to-end connectivity, IP address management, and domain name resolution. Utilizing these cloud-native services gives you the ability for a strong foundation and that is the key to building your cloud portfolio.
  1. Workload Architecture: On top of the strong foundation, applications definitely require a robust architecture and kits like AWS and Azure SDK, AWS programming toolkit and Google Cloud’s SDK command-line tools provide simple coding, scripting, and language-specific APIs, command-line tools, and libraries assisting in establishing reliable workload architecture. These are deliberately designed to support all possible software as well as the complex infrastructure on the cloud. Workloads are expected to communicate across distributed networks and systems and run reliably regardless of data loss or latency without affecting other components or workloads.
  1. Change Management: Any changes in the cloud service, application, or environment need to be constantly monitored and administered for increased operational reliability on the cloud. Workload behavior can be tracked with automated controls to keep a check on changes like rise or fall in the demand for additional servers depending on number of users, a new feature or security patch deployment, etc. Permissions are assigned to specific resources and audits of the change history are maintained to keep the teams mindful of the expected KPI responses and deviations if any. Automation accelerates the change management process and identifies any impact on reliability effectively. Amazon web services provides tools such as AWS CloudTrail, AWS Config, AWS CloudFormation, AWS OpsWorks for monitoring, logging & alerting in case of any deviations. Similarly Microsoft Azure enables users to utilize Azure Log Analytics, Azure automation & control, Azure App Service and Google cloud provider gives Google Stackdriver and GCP deployment manager as some key monitoring, logging, and alerting cloud services.
  1. Failure Management: Moderately complex systems are susceptible to failures, and it is necessary that workloads are aware of failure occurrence and are able to endure as well as auto remediate them. Automated system monitoring facilitates the self-rectification of errors. For instance, alerts are triggered when metrics cross thresholds, prompting the systems to auto to remediate the issues or isolate fault areas and replace them with replicated systems to continue to run the tests. The key is to conduct regular backups and automated tests of workloads, record failures, and examine its recovery process after notable changes are made to them. This helps assess workload resiliency, mitigate single points of failure if any and build a robust recovery system to make the environment more fault-tolerant. There are multiple dedicated services for specific activities like AWS CloudWatch, Azure Monitor, GCP’s Cloud Monitoring and Logging, AWS and Azure Recovery Backup & Site Recovery, Amazon Glacier, Azure Archival storage, and GCP Nearline and Coldline services. At times they may become a bit too much to consume and articulate and that’s where tools like CloudEnsure come in handy by bringing everything in one place and empowering business owners also to understand their cloud.

Conclusion

In order to be always in conformity with business specifications and requirements with the cloud workloads, it is necessary to adapt to continuous governance practices. The more reliable your policies and processes, the easier to manage them through people and hence reducing the human error factor to have a more reliable & robust cloud portfolio. Cloud governance platforms like Cloud Ensure offer the five foundational pillars of the well-architected framework that help build consistent, reliable, and effective workloads while guaranteeing scalability, security, and high availability of cloud systems.

The reliability pillar renders effective change and failure management techniques, ensures continuous monitoring, testing, and uninterrupted operations, and supports automated recovery strategies that enhance the durability and self-healing capacity of cloud applications and services.

The next article, the last of the WAF series, will talk about Operational Excellence.

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