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Feb 13, 2026

17 min read

A Practical Strategy for Running EC2 Auto Scaling VM Clusters in Production

Auto Scaling looks simple on paper. When traffic increases, more EC2 instances are launched. When traffic drops, instances are terminated. In production, this is exactly where things start to go wrong. Most Auto Scaling failures are not caused by scaling itself. They happen because the system was never designed for instances to appear and disappear freely. Configuration drifts between machines, data is still tied to local disks, load balancers route traffic too early, or new instances behave differently from old ones. When scaling kicks in, these weaknesses surface all at once. A stable EC2 Auto Scaling setup depends on one core assumption: any virtual machine can be replaced at any time without breaking the system. The following sections break down the practical architectural decisions required to make that assumption true in real production environments. 1. Instance Selection and Classification Auto Scaling does not fix poor compute choices. It only multiplies them. When new instances are launched, they must actually increase usable capacity instead of introducing new performance bottlenecks. For this reason, instance selection should start from how the workload behaves in production, not from cost alone or from what has been used historically. Different EC2 instance families are optimized for different resource profiles, and mismatching them with the workload is one of the most common causes of unstable scaling behavior. Comparison of Common Instance Families Instance Family Technical Characteristics Typical Workloads Compute Optimized (C) Higher CPU-to-memory ratio Data processing, batch jobs, high-traffic web servers Memory Optimized (R/X) Higher memory-to-CPU ratio In-memory databases (Redis), SAP, Java-based applications General Purpose (M) Balanced CPU and memory Backend services, standard application servers Burstable (T) Short-term CPU burst capability Dev/Staging environments, intermittent workloads In production, instance sizing should be revisited after the system has been running under real load for a while. Actual usage patterns—CPU, memory, and network traffic—tend to differ from what was assumed at deployment. CloudWatch metrics, together with AWS Compute Optimizer, are enough to show whether an instance type is consistently oversized or already hitting its limits. Note on Burstable (T) instances: In CPU-based Auto Scaling setups, T3 and T4g instances can be problematic. Once CPU credits are depleted, performance drops hard and instances may appear healthy while responding very slowly. When scaling is triggered in this state, the Auto Scaling Group adds more throttled instances, which often makes the situation worse instead of relieving load. Mixed Instances Policy To optimize cost and improve availability, Auto Scaling Groups should use a Mixed Instances Policy. This allows you to: Combine On-Demand instances (for baseline load) with Spot Instances (for variable load), reducing costs by 70–90%. Use multiple equivalent instance types (e.g., m5.large, m5a.large) to mitigate capacity shortages in specific Availability Zones. 2. AMI Management and Immutable Infrastructure If any virtual machine can be replaced at any time, then configuration cannot live on the machine itself. Auto Scaling creates and removes instances continuously. The moment a system relies on manual fixes, ad-hoc changes, or “just this one exception,” machines start to diverge. Under normal traffic, this rarely shows up. During a scale-out or scale-in event, it does—because new instances no longer behave like the old ones they replace. This is why the AMI, not the instance, is the deployment unit. Changes are introduced by building a new image and letting Auto Scaling replace capacity with it. Nothing is patched in place. Nothing is carried forward implicitly. Instance replacement becomes a controlled operation, not a source of surprise. Hardening Operating system updates, security patches, and removal of unnecessary services are done once inside the AMI. Every new instance starts from a known, secured baseline. Agent integration Systems Manager, CloudWatch Agent, and log forwarders are part of the image itself. Instances are observable and manageable the moment they launch, not after someone logs in to “finish setup.” Versioning AMIs are explicitly versioned and referenced by tag. Rollbacks are performed by switching versions, not by repairing machines in place. 3. Storage Strategy for Stateless Scaling Local state does not survive that assumption. This is where many otherwise well-designed systems quietly violate their own scaling model. Data is written to local disks, caches are treated as durable, or files are assumed to persist across restarts. None of these assumptions hold once Auto Scaling starts making decisions on your behalf. To keep instances replaceable, the system must be explicitly stateless. EBS and gp3 volumes EBS is suitable for boot volumes and ephemeral application needs, but not for persistent system state. gp3 is preferred because performance is decoupled from volume size, making instance replacement predictable and cheap. Externalizing persistent data Any data that must survive instance termination is moved out of the Auto Scaling lifecycle: Shared files → Amazon EFS Static assets and objects → Amazon S3 Databases → Amazon RDS or DynamoDB Accepting termination as normal behavior Instances are not protected from termination; the architecture is. When an instance is removed, the system continues operating because no critical data depended on it. 4. Network and Load Balancing Design If any virtual machine can be replaced at any time, the network layer must assume that failure is normal and localized. Network design cannot treat an instance or an Availability Zone as reliable. Auto Scaling may remove capacity in one zone while adding it in another. If traffic routing or health evaluation is too strict or too early, instance replacement turns into cascading failure instead of controlled churn. Multi-AZ Deployment: Auto Scaling Groups should span at least three Availability Zones. This ensures that instance replacement or capacity loss in a single zone does not remove the system’s ability to serve traffic. Instance replaceability only works if the blast radius of failure is limited at the AZ level. Health Check Grace Period: Load balancers evaluate instances mechanically. Without a grace period, newly launched instances may be marked unhealthy while the application is still warming up. This causes instances to be terminated and replaced repeatedly, even though nothing is actually wrong. A properly tuned grace period (for example, 300 seconds) prevents instance replacement from being triggered by normal startup behavior. Security Groups: Instances should not be directly exposed. Traffic is allowed only from the Application Load Balancer’s security group to the application port. This ensures that new instances join the system through the same controlled entry point as existing ones, without relying on manual rules or implicit trust. 5. Advanced Auto Scaling Mechanisms If instances can be replaced freely, scaling decisions must be accurate enough that replacement actually helps instead of amplifying instability. Relying only on CPU utilization assumes traffic patterns are simple and linear. In real production systems, traffic is often bursty, uneven, and driven by application-level behavior rather than raw CPU usage. Fixed threshold models tend to react too late or overreact, turning instance replacement into noise instead of recovery. Advanced Auto Scaling mechanisms exist to keep instance churn controlled and intentional. Dynamic Scaling Dynamic scaling adjusts capacity in near real time and is the foundation of self-healing behavior. Target Tracking is the most commonly recommended approach. A target value is defined for a metric such as CPU utilization, request count, or a custom application metric. Auto Scaling adjusts instance count to keep the metric close to that target. This avoids hard thresholds that trigger aggressive scale-in or scale-out events. Target Tracking is recommended because it: Keeps load at a stable, predictable level Reduces both under-scaling and over-scaling Minimizes manual tuning as traffic patterns change To ensure fast reactions, detailed monitoring (1-minute metrics) should be enabled. This is especially critical for workloads with short but intense traffic spikes, where metric latency can directly impact service stability. Predictive Scaling Predictive scaling uses historical data—typically at least 14 days—to detect recurring traffic patterns. Instead of reacting to load, the Auto Scaling Group prepares capacity ahead of time. This is especially relevant when instance startup time is non-trivial and late scaling would violate latency or availability expectations. Warm Pools Warm Pools address the gap between instance launch and readiness. Instances are kept in a stopped state with software already installed When scaling is triggered, instances move to In-Service much faster Replacement speed improves without permanently increasing running capacity 6. Testing and Calibration If instances are meant to be replaced freely, scaling behavior must be tested under conditions where replacement actually happens. Auto Scaling configurations that look correct on paper often fail under real load. Testing is not about proving that scaling works in ideal conditions, but about exposing how the system behaves when instances are added and removed aggressively. Load Testing: Tools such as Apache JMeter are used to simulate traffic spikes. The goal is not just to trigger scaling, but to observe whether new instances stabilize the system or introduce additional latency. Termination Testing: Instances are deliberately terminated to verify ASG self-healing behavior and service continuity at the load balancer. Cooldown Periods: Cooldown intervals are adjusted to prevent thrashing—rapid scale-in and scale-out caused by overly sensitive policies. Replacement must be deliberate, not reactive noise. Conclusion Auto Scaling works only when instance replacement is treated as a normal operation, not an exception. When that assumption is enforced consistently across the system, scaling stops being fragile and starts behaving in a predictable, controllable way under real production load. If you are operating Auto Scaling workloads on AWS and want to validate this in practice, Haposoft can help. Reach out if you want to review your current setup or pressure-test how it behaves when instances are replaced under load.

Oct 21, 2025

20 min read

AWS us-east-1 Outage: A Technical Deep Dive and Lessons Learned

On October 20, 2025, an outage in AWS’s us-east-1 region took down over sixty services, from EC2 and S3 to Cognito and SageMaker, disrupting businesses worldwide. It was a wake-up call for teams everywhere to rethink their cloud architecture, monitoring, and recovery strategies. Overview of the AWS us-east-1 Outage On October 20, 2025, a major outage struck Amazon Web Services’ us-east-1 region in Northern Virginia. This region is among the busiest and most relied upon in AWS’s global network. The incident disrupted core cloud infrastructure for several hours, affecting millions of users and thousands of dependent platforms worldwide. According to AWS, the failure originated from an internal subsystem that monitors the health of network load balancers within the EC2 environment. This malfunction cascaded into DNS resolution errors, preventing key services like DynamoDB, Lambda, and S3 from communicating properly. As a result, applications depending on those APIs began timing out or returning errors, producing widespread connectivity failures. More than sixty AWS services, including EC2, S3, RDS, CloudFormation, Elastic Load Balancing, and DynamoDB were partially or fully unavailable for several hours. AWS officially classified the disruption as a “Multiple Services Operational Issue.” Though temporary workarounds were deployed, full recovery took most of the day as engineers gradually stabilized the internal networking layer. Timeline and Scope of Impact Event Details Start Time October 20, 2025 – 07:11 UTC (≈ 2:11 PM UTC+7 / 3:11 AM ET) Full Service Restoration Around 10:35 UTC (≈ 5:35 PM UTC+7 / 6:35 AM ET), with residual delays continuing for several hours Region Affected us-east-1 (Northern Virginia) AWS Services Impacted 64 + services across compute, storage, networking, and database layers Severity Level High — classified as a multiple-service outage affecting global API traffic. Status Fully resolved by late evening (UTC+7), October 20 2025. During peak impact, major consumer platforms, including Snapchat, Fortnite, Zoom, WhatsApp, Duolingo, and Ring, etc reported downtime or degraded functionality, underscoring how many global services depend on AWS’s Virginia backbone. AWS Services Affected During the Outage The outage affected a broad range of AWS services across compute, storage, networking, and application layers. Core infrastructure saw the heaviest impact, followed by data, AI, and business-critical systems. Category Sub-Area Impacted Services Core Infrastructure Compute & Serverless AWS Lambda, Amazon EC2, Amazon ECS, Amazon EKS, AWS Batch Storage & Database Amazon S3, Amazon RDS, Amazon DynamoDB, Amazon ElastiCache, Amazon DocumentDB Networking & Security Amazon VPC, AWS Transit Gateway, Amazon CloudFront, AWS Global Accelerator, Amazon Route 53, AWS WAF AI/ML and Data Services Machine Learning Amazon SageMaker, Amazon Bedrock, Amazon Comprehend, Amazon Rekognition, Amazon Textract Data Processing Amazon EMR, Amazon Kinesis, Amazon Athena, Amazon Redshift, AWS Glue Business-Critical Services Communication Amazon SNS, Amazon SES, Amazon Pinpoint, Amazon Chime Integration & Workflow Amazon EventBridge, AWS Step Functions, Amazon MQ, Amazon API Gateway Security & Compliance AWS Secrets Manager, AWS Certificate Manager, AWS Key Management Service (KMS), Amazon Cognito These layers failed in sequence, causing cross-service dependencies to break and leaving customers unable to deploy, authenticate users, or process data across multiple regions. How the Outage Affected Cloud Operations When us-east-1 went down, the impact wasn’t contained to a few services, it spread through the stack. Core systems failed in sequence, and every dependency that touched them started to slow, timeout, or return inconsistent data. What followed was one of the broadest chain reactions AWS has seen in recent years. 1. Cascading Failures The multi-service nature of the outage caused cascading failures across dependent systems. When core components such as Cognito, RDS, and S3 went down simultaneously, other services that relied on them began throwing exceptions and timing out. In many production workloads, a single broken API call triggered full workflow collapse as retries compounded the load and spread the outage through entire application stacks. 2. Data Consistency Problems The outage severely disrupted data consistency across multiple services. Failures between RDS and ElastiCache led to cache invalidation problems, while DynamoDB Global Tables suffered replication delays between regions. In addition, S3 and CloudFront returned inconsistent assets from edge locations, causing stale content and broken data synchronization across distributed workloads. 3. Authentication and Authorization Breakdowns AWS’s identity and security stack also experienced significant instability. Services like Cognito, IAM, Secrets Manager, and KMS were all affected, interrupting login, permission, and key management flows. As a result, many applications couldn’t authenticate users, refresh tokens, or decrypt data, effectively locking out legitimate access even when compute resources remained healthy. 4. Business Impact Scenarios The outage hit multiple workloads and customer-facing systems across industries: E-commerce → Payment and order-processing pipelines stalled as Lambda, API Gateway, and RDS timed out. SES and SNS failed to deliver confirmation emails, affecting checkout flows on platforms like Shopify Plus and BigCommerce. SaaS and consumer apps → Authentication via Cognito and IAM broke, causing login errors and session drops in services like Snapchat, Venmo, Slack, and Fortnite. Media & streaming → CloudFront, S3, and Global Accelerator latency led to buffering and downtime across Prime Video, Spotify, and Apple Music integrations. Data & AI workloads → Glue, Kinesis, and SageMaker jobs failed mid-run, disrupting ETL pipelines and inference services; analytics dashboards showed stale or missing data. Enterprise tools → Office 365, Zoom, and Canva experienced degraded performance due to dependency on AWS networking and storage layers. Insight: The outage showed that even “multi-AZ” redundancy within a single region isn’t enough. For critical workloads, true resilience requires cross-region failover and independent identity and data paths. Key Technical Lessons and Reliable Cloud Practices The us-east-1 outage exposed familiar reliability gaps — single-region dependencies, missing isolation layers, and reactive rather than preventive monitoring. Below are consolidated lessons and proven practices that teams can apply to build more resilient architectures. 1. Avoid Single-Region Dependency One of the clearest takeaways from the us-east-1 outage is that relying on a single region is no longer acceptable. For years, many teams treated us-east-1 as the de facto home of their workloads because it’s fast, well-priced, and packed with AWS services. But that convenience turned into fragility: when the region failed, everything tied to it went down with it. The fix isn’t complicated in theory, but it requires architectural intent: run active workloads in at least two regions, replicate critical data asynchronously, and design routing that automatically fails over when one region becomes unavailable. This approach doesn’t just protect uptime, it also protects reputation, compliance, and business continuity. 2. Isolate Failures with Circuit Breakers and Service Mesh The outage highlighted how a single broken dependency can quickly cascade through an entire system. When services are tightly coupled, one failure often leads to a flood of retries and timeouts that overwhelm the rest of the stack. Without proper isolation, even a minor disruption can escalate into a complete service breakdown. Circuit breakers help contain these failures by detecting repeated errors and temporarily stopping requests to the unhealthy service. They act as a safeguard that gives systems time to recover instead of amplifying the problem. Alongside that, a service mesh such as AWS App Mesh or Istio applies these resilience policies consistently across microservices, without requiring any change to application code 3. Design for Graceful Degradation One of the biggest lessons from the outage is that a system doesn’t have to fail completely just because one part goes down. A well-designed application should be able to degrade gracefully, keeping essential features alive while less critical ones pause. This approach turns a potential outage into a temporary slowdown rather than a total shutdown. In practice, that means preparing fallback paths in advance. Cache responses locally when databases are unreachable, serve read-only data when write operations fail, and make sure authentication remains available even if analytics or messaging features are offline. These small design choices protect user trust and maintain service continuity when infrastructure falters. 4. Strengthen Observability and Proactive Alerting During the us-east-1 outage, many teams learned about the disruption not from their dashboards, but from their users. That delay cost hours of downtime that could have been mitigated with better observability. Building a resilient system starts with seeing what’s happening — in real time and across multiple data sources. To achieve that, monitoring should extend beyond AWS’s native tools. Combine CloudWatch with external systems like Prometheus, Grafana, or Datadog to correlate metrics, traces, and logs across services. Alerts should trigger based on anomalies or trends, not just static thresholds. And most importantly, observability data must live outside the impacted region to avoid blind spots during regional failures. 5. Build for Automated Recovery and Test Resilience The outage showed that relying on manual recovery is a costly mistake. When systems fail at scale, waiting for human response wastes valuable time and magnifies the impact. A reliable system must detect problems automatically and trigger recovery workflows immediately. CloudWatch alarms, Step Functions, and internal health checks can restart failed components, promote standby databases, or reroute traffic without human input. The best teams also treat recovery as a continuous process, not an emergency fix, ensuring automation is built, tested, and improved over time. True resilience goes beyond automation. Regular chaos experiments help verify that recovery logic works when it truly matters. Simulating database timeouts, service latency, or full region loss exposes weak points before real failures do. When recovery and testing become routine, teams stop reacting to incidents and start preventing them. Action Plan for Teams Moving Forward The AWS outage reminded us that no cloud is truly fail-proof. We know where to go next, but meaningful change takes time. This plan helps teams make steady, practical improvements without disrupting what already works. Next 30 days Review how your workloads depend on AWS services, especially those concentrated in a single region. Set up baseline monitoring that tracks latency, errors, and availability from outside AWS. Document incident playbooks so response steps are clear and repeatable. Run small-scale failover tests to confirm that backups and DNS routing behave as expected. Next 3–6 months Roll out multi-region deployment for high-impact workloads. Replicate critical data asynchronously across regions. Introduce controlled failure testing to verify that automation and fallback logic hold up under stress. Begin adding auto-recovery or self-healing workflows for key services. Next 6–12 months Evaluate hybrid or multi-cloud options to reduce vendor and regional risk. Explore edge computing for latency-sensitive use cases. Enhance observability with AI-assisted alerting or anomaly detection. Build a full business continuity plan that covers both technology and operations. Haposoft has years of hands-on experience helping teams design, test, and scale reliable AWS systems. If your infrastructure needs to be more resilient after this incident, our engineers can support you in building, testing, and maintaining that foundation. Cloud outages will always happen. What matters is how ready you are when they do. Conclusion That hiccup in AWS us-east-1 showed just how vulnerable everything is, actually. Now it’s about learning to bounce back, running drills, then getting ready for what happens next time. True dependability doesn’t appear instantly; instead, it grows through consistent little fixes so things don’t fall apart when trouble strikes. We’re still helping groups create cloud setups meant to withstand failures. This recent disruption teaches us lessons; consequently, our future builds will be more robust, straightforward, also ready for whatever happens.

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