High Availability and Scalability in the Cloud
Slide deck explaining high availability and scalability in the cloud, how they address different problems (failures vs demand), redundancy and failover, scaling strategies, and common scenarios and pitfalls.
Benefits of High Availability and Scalability in the Cloud
Introduction to the benefits of high availability and scalability in cloud computing, covering how these concepts help maintain service usability and performance.
Benefits of High Availability and Scalability in the Cloud
Introduction to the benefits of high availability and scalability in cloud computing, covering how these concepts help maintain service usability and performance.
Failure problems vs demand problems
High availability handles failures; scalability handles changing demand. Failures and outages point to high availability. Traffic and load spikes point to scalability. Cloud can help, but design still matters. Goal: keep the service usable.
High availability (HA)
High availability keeps your app accessible during failures. Failures can be small or broad. The target is 'service stays usable'. Not 'nothing ever fails'. Reduced downtime for users.
Redundancy + failover
Redundancy gives backups; failover switches traffic to them. Redundancy: more than one instance or component. Failover: reroute traffic to a healthy instance. Handles failures without full outages. Avoids single points of failure.
Scalability
Scalability means adding or removing capacity as demand changes. Scale up when demand rises. Scale down when demand falls. Keep performance acceptable. Scaling down helps control cost.
Rule of thumb
Failures point to availability; load changes point to scaling. Failure or outage story points to high availability. Traffic or load story points to scalability. Pick the main stressor first. Both can matter, but don't blur the terms.
Cloud helps, but doesn't auto-fix
Cloud gives building blocks; your design makes the system resilient. Faster provisioning and adjustments. Reliability features (multiple instances, placement options). You must design for redundancy and scaling. Single instance can still fail you.
Scenario: failure with no user impact
A failure occurred, but the service stayed accessible. Category: high availability. Failure still happened. Redundancy kept service running. Failover avoided visible downtime.
Scenario: short-lived traffic spike
Scale up for the spike, then scale down to control cost. Category: scalability. Scale up to stay responsive. Scale down after the event. Avoid paying for idle peak capacity.
Myth: cloud = highly available by default
Hosting location doesn't remove single points of failure. One instance equals single point of failure. High availability needs redundancy plus failover. Health checks help detect problems. Provider guarantees depend on the service.
Service Level Agreement (SLA)
An SLA sets uptime expectations for a defined service under defined conditions. SLA equals documented service commitment. Often expressed as availability percentage. Sets expectations, not architecture. Monitoring plus design still required.
Common pitfalls
Most mistakes come from assuming the cloud replaces design work. Mixing availability with scalability. Assuming cloud equals HA automatically. Forgetting scaling down. Treating SLA as 'set-and-forget'.