Preventing Technical Issues on Amazon

DevOps & SRE Perspective: Lessons from the Amazon Web Services US-East-1 Outage ! 1. Outage context • AWS reported “increased error rates and latencies for multiple AWS Services in the US-EAST-1 Region,” later identifying issues around the Amazon DynamoDB API endpoint and DNS resolution as the likely root cause. • The region is a critical hub for many global workloads — meaning any failure has broad impact. • From the trenches: “Just got woken up to multiple pages. No services are loading in east-1, can’t see any of my resources. Getting alerts lambdas are failing, etc.” 2. What this means for SRE/DevOps teams • Single-region risk: Relying heavily on one region (or one availability zone) is a brittle strategy. Global services, control planes, identity/auth systems often converge here — so when it fails, the blast radius is massive. • DNS and foundational services matter: It’s not always the compute layer that fails first. DNS, global system endpoints, shared services (like DynamoDB, IAM) can be the weak link. • Cascading dependencies: A failure in one service can ripple through many others. E.g., if control-plane endpoints are impacted, your fail-over mechanisms may not even activate. • Recovery ≠ full resolution: Even after the main fault is resolved, backlogs, latencies, and unknown state issues persist. Teams need to monitor until steady state is confirmed. 3. Practical take-aways & actions •Adopt a multi-region / multi-AZ fallback strategy: Ensure critical workloads can shift automatically (or manually) to secondary regions or providers. •Architect global state & control plane resilience: Make sure services like IAM, identity auth, configuration, and global databases don’t concentrate in one point of failure. •Simulate DNS failures and control-plane failures in chaos testing: Practice what happens when DNS fails, when endpoint resolution slows, when the control plane is unreachable. •Improve monitoring + alerting on “meta-services”: Don’t just monitor your app metrics—watch DNS latency/resolve errors, endpoint access times, control-plane API errors. •Communicate clearly during incidents: Transparency and frequent updates matter. Teams downstream depend on accurate context. •Expect eventual consistency & backlog states post-recovery: After the main fix, watch for delayed processing, stuck queues, prolonged latencies, and reconcile state when needed. 4. Final thought This outage is a stark reminder: being cloud-native doesn’t eliminate infrastructure risk — it changes its shape. As practitioners in DevOps and SRE, our job isn’t just to prevent failure (impossible) but to anticipate, survive, and recover effectively. Let’s use this as an impetus to elevate our game, architect with failure in mind, and build systems that fail gracefully. #DevOps #SRE #CloudReliability #AWS #Outage #IncidentManagement #Resilience

In the business of running technology services, invisibility is the reward for doing your job well. If everything works, no one notices. If something breaks, everyone does. For years, I accepted that quiet reality — visibility often arrives dressed as failure. It can feel like a thankless seat. But here is the uncomfortable truth I have learned over decades: If you are only valuable when things are broken, you are not yet operating at mastery. The real job in technology leadership is not solving problems. It is preventing them. And your best work should happen when nothing is wrong. When systems are failing, you are reactive. When systems are stable, you are strategic. Most teams relax when things are calm. I coach mine differently. When everything is working — that is when the real work begins. Because stability is not rest. It is runway. Runway to: • Challenge your own architecture • Eliminate hidden fragility • Pay down technical debt • Automate manual dependencies • Document what only one person knows • Ask the uncomfortable question: “If we double tomorrow, what breaks first?” Firefighting builds stamina. Prevention builds discipline. This mindset has been deeply shaped for me by The 7 Habits of Highly Effective People. Be Proactive — Don’t just measure response time. Measure incident avoidance. Begin With the End in Mind — If resilience is the goal, your calendar must reflect it. Put First Things First — Important work rarely screams. It quietly saves you. Sharpen the Saw — Continuous improvement is not motivational language. It is survival. Practical shifts any tech leader can implement immediately: 1. Create a monthly “Prevented Failures” review. Celebrate risks eliminated, not just outages resolved. 2. Ring-fence 15–20% of team capacity for improvement work. If you don’t schedule it, crisis will. 3. Maintain a visible technical debt register with owners and deadlines. What is not reviewed will not be resolved. 4. After every quarter of stability, conduct a “comfort audit.” Ask: What risk are we ignoring because nothing is burning? Solving problems proves competence. Preventing problems proves leadership. Anyone can join an outage bridge call. Few have the discipline to make that bridge unnecessary. Invisible excellence may not trend. But it compounds. And over time, that is what builds trust. #Resilience #UnseenHeros! #Stability

Checked the Amazon Web Services (AWS) Health Dashboard after the recent Availability Zone disruption in the United Arab Emirates, where a data center was reportedly hit by external objects, leading to service impact. Incidents like this aren’t dramatic. They’re educational. Some clear software design lessons: 1. Single AZ is a risk If your app runs in just one Availability Zone and that AZ has issues, your app goes down with it. Multi-AZ means spreading compute, load balancers, and databases across isolated data centers so traffic can shift if one fails. 2. Define RTO and RPO early RTO (Recovery Time Objective) is how long you can afford downtime. RPO (Recovery Point Objective) is how much data you can afford to lose. If you don’t define these upfront, you can’t design backups, replication, or failover properly. 3. Retries need control When a dependency is failing, blind retries can overload it further. Use exponential backoff and circuit breakers so your system reduces pressure instead of amplifying it. 4. Observability is critical Metrics, logs, tracing, and alerts decide how fast you detect and understand impact. If users are the first to report issues, you’re already behind. 5. Disaster recovery must be tested A DR document isn’t enough. Simulate AZ failure. Test failover. Practice restoring backups. You don’t want your first real test to be during production impact. Cloud infrastructure is highly reliable. Application resilience still depends on your architecture choices. Design systems assuming components can fail and make sure that failure does not bring everything down.

Outages should be viewed as indicators of stress within a business model rather than simple glitches. Recent incidents, such as the Amazon Web Services (AWS) DNS failure and Vodafone’s UK outage, highlight a critical issue: many so-called "resilient" architectures may actually function as single points of failure, despite appearing to have multi-cloud alternatives. If an Industry 4.0 operation relies on only one cloud region, DNS path, or vendor control plane, true resilience is lacking, and reliance on fortunate circumstances may be the case. Addressing this requires a shift towards designing systems that anticipate failure. Strategies may include prioritizing local-edge operation technology (OT) to maintain essential functions, employing active-active configurations across multiple regions and providers, ensuring diverse peering and identity paths, utilizing dual-carrier connectivity, and implementing private 5G networks for reliable control. Regulatory bodies such as DORA, NIS2, and UK Operational Resilience will likely seek concrete evidence of resilience rather than presentations. While achieving true resilience involves costs, it is important to consider that unplanned downtime can result in significant financial losses and damage customer trust. Recommended practices include conducting regular “Failure Day” exercises, mapping third-party dependencies down to the API level, and revising key performance indicators (KPIs) from uptime to fault tolerance. This approach can help ensure that, in the event of disruptions in systems like us-east-1, operational capabilities remain intact and financial performance is protected. At #BellLabsConsulting we have a full methodology to prevent events such as these, but also have a faster response when they happen.

AWS went sideways this morning, and many veterinary hospitals felt it. Today, Monday, October 20, a major tech incident happened in a huge Amazon facility called US-EAST-1. This caused a lot of hiccups, like things running super slow or just failing, for many online services. While recovery started quickly, some practices felt the lingering effects through the morning and into this afternoon. What is AWS, in plain English? AWS (Amazon Web Services) is a massive, secure building where software companies rent space for their programs. Instead of owning their own server room, they rent Amazon's. Why it matters: Most modern software your practice uses, from cloud PIMS to texting apps, lives in a place like AWS. When a problem hits one region (like US-EAST-1), it can instantly affect many different apps you rely on daily. If your vendor hosts there, you may have noticed: ✅ Slow or failed logins to your PIMS or client portals. ✅ Online forms, telemedicine links, and refill requests timing out. ✅ Client texts and virtual receptionist tools delayed or offline. ✅ Payments, ePrescriptions, and lab integrations refusing to work. This incident is a critical reminder: "Cloud" doesn't mean "Always On." Reliability is something your vendor must actively plan for. The hospital managers who handled this best had a plan. You need one too! Here are four easy, non-technical steps you can take: • Ask the "Where Are You?" Question: Ask every critical vendor: "Where are you running your system, and could a problem in that single location shut us down?" • Confirm the Backup Plan: Ask for proof that your vendor runs in more than one secure facility (Amazon calls this "multi-AZ") and can seamlessly move to another region if necessary. • Get Ready to Go Manual: Document your offline fallbacks. What's the step-by-step process for manual payments, printed intake forms, and filling prescriptions when systems are down? • Know How to Get Incident Updates: Bookmark your vendor’s public status page and confirm how quickly they alert your team about major issues. At VetSoftwareHub.com, I am adding a simple vendor resilience checklist that practices can use. Comment “checklist” or DM me, I will share a copy and then publish it. #veterinary #vetmed #veterinarymedicine #veterinarysoftware #cloud #resilience #practicemanagement

“𝗕𝗹𝗮𝘀𝘁 𝗿𝗮𝗱𝗶𝘂𝘀” 𝗶𝘀 𝗮𝗯𝗼𝘂𝘁 𝗹𝗶𝗺𝗶𝘁𝗶𝗻𝗴 𝘁𝗵𝗲 𝘀𝗰𝗼𝗽𝗲 𝗼𝗳 𝗽𝗼𝘁𝗲𝗻𝘁𝗶𝗮𝗹 𝗳𝗮𝗶𝗹𝘂𝗿𝗲. For example, in a multi-account setup with AWS Organizations, you separate workloads into different accounts—production, staging, and dev. If an issue happens in one, it doesn’t bring everything down. 𝐆𝐫𝐞𝐚𝐭 𝐚𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 𝐢𝐬𝐧’𝐭 𝐣𝐮𝐬𝐭 𝐚𝐛𝐨𝐮𝐭 𝐛𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐟𝐨𝐫 𝐬𝐜𝐚𝐥𝐞—𝐢𝐭’𝐬 𝐚𝐛𝐨𝐮𝐭 𝐜𝐨𝐧𝐭𝐚𝐢𝐧𝐢𝐧𝐠 𝐫𝐢𝐬𝐤𝐬 𝐚𝐧𝐝 𝐦𝐢𝐧𝐢𝐦𝐢𝐳𝐢𝐧𝐠 𝐢𝐦𝐩𝐚𝐜𝐭 𝐰𝐡𝐞𝐧 𝐭𝐡𝐢𝐧𝐠𝐬 𝐠𝐨 𝐰𝐫𝐨𝐧𝐠. Here are practical ways to apply this principle: - Use Amazon S3 buckets per workload with least-privilege IAM access, instead of one shared bucket. - Deploy Amazon ECS/EKS services across multiple Availability Zones to avoid AZ-specific outages. - Segment VPCs by environment or business unit, so network misconfigurations don’t ripple across all apps. - Run RDS/Aurora with Multi-AZ deployments so a database failure in one AZ doesn’t impact the app. - Implement Amazon CloudFront with regional edge caches, reducing the impact if an origin has issues. - Split Amazon SQS queues per microservice, so one stuck consumer doesn’t block unrelated workloads. - Apply AWS Lambda concurrency limits per function, ensuring one misbehaving function doesn’t consume all capacity. - Use Service Control Policies (SCPs) in AWS Organizations to enforce guardrails and reduce accidental wide-reaching changes. - Adopt cell-based architecture (e.g., splitting workloads by region or customer segment) so outages are contained to a subset of users. - Use separate KMS keys per application or environment, so key misconfigurations or limits don’t cascade across multiple services. #AWS #SolutionsArchitecture #CloudDesign #Resilience #BestPractices #CloudArchitecture #AWS #SolutionsArchitect #KeepItSimple #ScalableDesign Follow me to get more tips and suggestions for becoming a successful solutions architect.

How to Architect for the "Big Day": A Guide to Handling Spiky Traffic In cloud architecture, a fundamental shift happens when moving from a steady-state application to one built for massive, unpredictable spikes. It’s the evolution from Static to Elastic. If you are preparing for a major launch, flash sale, or viral event on AWS, here is the technical blueprint for building a resilient, decoupled system. 1. The Foundation: Horizontal vs. Vertical Scaling Scaling isn't just about "getting bigger", it’s about getting smarter. Vertical Scaling: Increasing a single server’s CPU/RAM. This usually involves downtime and hits a hard hardware ceiling. Horizontal Scaling: Adding more server instances. On AWS, Auto Scaling Groups (ASG) manage this by automatically launching instances when CPU utilization hits a threshold (e.g., 60%). The Traffic Cop: An Application Load Balancer (ALB) is essential here. It acts as the gateway, instantly discovering new instances and distributing load so no single server is overwhelmed. 2. The "Shock Absorber" Pattern (SQS) A common failure point is the "Provisioning Gap", servers take minutes to boot, but a spike happens in seconds. The Problem: Direct writes can crash a database during a surge. The Solution: Decouple the frontend from the backend using Amazon SQS. The Result: The frontend drops requests into a queue and gives the user an instant "Success" message. The backend pulls from the queue at a safe, steady pace. You don't lose orders; you just buffer the rush. 3. Offloading the Core: Caching Strategies The most efficient way to scale is to stop traffic before it ever hits your servers. At the Edge: Amazon CloudFront caches static content (images/logos) at Edge Locations. This offloads heavy lifting from your origin servers. In-Memory: Amazon ElastiCache (Redis) stores frequent query results. Instead of the database processing the same "Product Inventory" query 10,000 times, it serves it once from memory. 4. Proactive Readiness: "Pre-heating" the Cloud Automation is powerful, but reactive scaling can sometimes be too slow for a "Big Bang" event. Scheduled Scaling: Don't wait for the spike. Set your ASG to double your capacity one hour before the event starts. ELB Pre-warming: For massive, instantaneous surges, standard Load Balancers might not scale fast enough. Open a ticket with AWS to "Pre-warm" your ELB so the front door is wide open from the first second.