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Gartner has released its list of 10 strategic technology trends for 2026, highlighting how AI, platforms, and security are becoming core to modern systems. Rather than future concepts, the trends reflect changes already affecting how teams build, scale, and govern technology today. Why These Trends Matter in 2026 The short answer is that experimentation is no longer enough. Many organizations have already tried AI, automation, or advanced analytics in isolated projects. What’s happening now is a shift from trial to commitment. Once these technologies move into core systems, the cost of poor architectural and governance decisions becomes very hard to undo. The 2026 trends highlight where that pressure is coming from. Platforms are expected to support increasingly complex AI workloads without exploding costs. Security teams are dealing with threats that move too quickly for purely reactive defenses. At the same time, regulations and geopolitical realities are starting to influence where data lives and how infrastructure is designed. What makes the 2026 trends stand out is how closely they connect. Advances in generative AI lead naturally to agent-based systems, which in turn increase the need for more context-aware and domain-specific models. As AI moves deeper into core systems, governance, security, and data protection stop being secondary concerns. To make this complexity easier to navigate, Gartner groups the trends into three themes: The Architect, The Synthesist, and The Vanguard. This framing helps teams look at the stack as a sequence of concerns, not ten separate problems. Top 10 Strategic Technology Trends for 2026 Gartner’s 2026 list includes the following ten trends: AI-Native Development Platforms AI Supercomputing Platforms Confidential Computing Multiagent Systems Domain-Specific Language Models Physical AI Preemptive Cybersecurity Digital Provenance AI Security Platforms Geopatriation 1. AI-Native Development Platforms AI-native development platforms reflect how generative AI is becoming part of everyday software development, not a separate tool. Developers are already using AI to write code, generate tests, review changes, and produce documentation. The shift in 2026 is that this usage is moving from informal experimentation to more structured, platform-level adoption. As AI becomes embedded in development workflows, questions around code quality, security boundaries, and team practices start to matter just as much as speed. 2. AI Supercomputing Platforms AI supercomputing platforms address the growing demands of modern AI workloads. Training, fine-tuning, and running large models require far more compute than traditional enterprise systems were designed to support. This puts pressure on infrastructure choices, from hardware and architecture to how shared compute resources are managed. In practice, teams are being forced to think more carefully about cost, capacity, and control as AI workloads scale. 3. Confidential Computing Confidential computing focuses on protecting data while it is being processed, not just when it is stored or transmitted. As AI systems handle more sensitive data, traditional security boundaries are no longer enough. This trend reflects a growing need to run analytics and AI workloads in environments where data remains protected even from the underlying infrastructure. For many teams, it shifts security discussions closer to architecture and runtime design. 4. Multiagent Systems Multiagent systems describe a move away from single, monolithic AI models toward collections of smaller, specialized agents working together. Each agent handles a specific task, while coordination logic manages how they interact. This approach makes automation more flexible and scalable, but it also introduces new operational concerns. Visibility, control, and failure handling become critical as agents are given more autonomy across workflows. 5. Domain-Specific Language Models Domain-specific language models are built to operate within a particular industry or functional context. Instead of general-purpose responses, these models are trained or adapted to understand domain terminology, rules, and constraints. The trend reflects growing demand for higher accuracy and reliability in production use cases, especially in regulated or complex environments. As a result, data quality and domain knowledge become just as important as model size. 6. Physical AI Physical AI brings intelligence out of purely digital systems and into the physical world. This includes robots, drones, smart machines, and connected equipment that can sense, decide, and act in real environments. The trend reflects growing interest in using AI to improve operational efficiency, safety, and automation beyond screens and dashboards. For most teams, the challenge is less about experimentation and more about integrating AI reliably with hardware, sensors, and real-world constraints. 7. Preemptive Cybersecurity Preemptive cybersecurity shifts the focus from reacting to incidents toward preventing them before damage occurs. As attack surfaces expand and threats move faster, traditional detection-and-response models struggle to keep up. This trend reflects growing use of AI and automation to anticipate risks, identify weak signals, and block threats earlier in the attack lifecycle. Security becomes more about continuous risk reduction than isolated incident handling. 8. Digital Provenance Digital provenance is about verifying where data, software, and AI-generated content come from and whether they can be trusted. As AI systems produce more outputs and rely on more external inputs, knowing the origin and integrity of digital assets becomes critical. This trend reflects rising concern around tampered data, unverified models, and synthetic content. Provenance adds traceability to systems that would otherwise be opaque. 9. AI Security Platforms AI security platforms focus on securing AI systems as a distinct layer, rather than treating them as just another application. As organizations use a mix of third-party models, internal tools, and custom agents, visibility and control become harder to maintain. This trend reflects the need for centralized oversight of how AI is accessed, how data flows through models, and how risks such as data leakage or misuse are managed. For many teams, AI security is becoming a dedicated discipline rather than an extension of traditional security tools. 10. Geopatriation Geopatriation addresses the growing impact of geopolitics and regulation on technology architecture. Data residency rules, supply chain risks, and regional regulations are increasingly influencing where workloads can run and how systems are designed. This trend reflects a shift away from fully globalized cloud strategies toward more regional or sovereign approaches. In practice, it forces teams to consider flexibility, portability, and compliance as core architectural concerns. Conclusion The 2026 technology trends above reflect a clear shift in how technology is being used and governed. AI is moving deeper into core systems, automation is expanding across workflows, and trust is becoming a technical requirement rather than an assumption. These trends are less about predicting the future and more about describing the conditions teams are already working under. For organizations across the tech industry, the value of this list is not in adopting every trend at once, but in understanding how they connect. Decisions around platforms, orchestration, and governance are increasingly linked. The sooner teams recognize those links, the easier it becomes to make technology choices that hold up over time.

Google Play is enforcing support for 16KB memory page size on newer Android versions. While most apps are unaffected, Android apps that use native code may fail builds or have updates rejected if they are not updated in time. From November 1st, 2025, all new Android apps and all updates to existing apps submitted to Google Play must support 16KB memory page size. Apps that do not meet this requirement may be rejected during submission, even if they previously worked. This mainly affects apps that include native code. What Is Memory Page Size and Why Android Is Changing It Memory page size is the basic unit Android uses to work with memory. For a long time, this size has effectively been 4KB, and most Android apps, especially those with native code — were built around that assumption. Developers rarely think about it because, until now, it mostly “just worked.” That’s changing as Android starts supporting 16KB memory pages on newer devices. This shift isn’t cosmetic, it’s driven by newer hardware, larger RAM sizes, and the need for more efficient memory handling at the system level. The important part for app owners is that native binaries built with old assumptions may no longer behave the same way unless they’re updated. Which Apps Are Affected by the 16KB Page Size Change This 16KB page size change does not affect every Android app. The risk is mainly tied to native code, because many native libraries were originally built with a 4KB page size assumption that may not hold on newer devices. Usually not affected Apps built purely with Java or Kotlin Apps that do not use the NDK Apps that do not bundle any native SDKs Needs to be checked Apps using the NDK or C and C++ code Apps that include .so libraries Apps using third party SDKs with native code Frameworks with native layers such as React Native, Flutter, or game engines This structure allows teams to quickly identify whether their app is likely affected without digging into low level system details. Being in the “needs to be checked” group does not mean the app is broken, but it does mean native dependencies should be reviewed before the next update. What App Owners Should Do Next For most teams, this is not a large migration project. It is a short verification and cleanup process that helps ensure future Android updates are not blocked by native compatibility issues. Check whether your app includes native code Start by confirming whether the app contains any native components. This includes NDK code written by your team, bundled .so libraries, and native binaries that come from third-party SDKs or frameworks. Even apps written mostly in Java or Kotlin can still include native code indirectly. Review native dependencies and ownership Once native code is identified, list all native artifacts used by the app, including shared libraries and SDKs. At this stage, dependencies are classified into two groups: components the team controls and components provided by third parties. This distinction determines whether an issue can be fixed by rebuilding or requires a vendor update or replacement. Update or rebuild where needed Each native dependency is checked against Android’s 16KB compatibility requirements. SDKs and frameworks are updated to versions that support the new page size where available. For self-built native code, C or C++ libraries are rebuilt using appropriate NDK configurations. If a third-party dependency does not yet support 16KB, it is flagged early so alternatives or mitigation options can be considered. Test and prepare for release After changes are applied, the app is tested in an environment that reflects 16KB page size behavior. Key user flows are verified to ensure no regressions appear at runtime. Once testing is complete, the app is ready for future updates that comply with Google Play requirements. Final Notes The 16KB page size requirement is a platform-level change that mainly impacts apps with native dependencies. The challenge is often not the fix itself, but identifying hidden native risks early enough to avoid blocked updates. To support teams at different stages, we typically help in three focused ways: Impact check to confirm whether an app is affected Native dependency review to identify upgrade or rebuild risks Targeted fixes and validation to ensure future updates can be published smoothly If you’re unsure whether your app needs changes, feel free to get in touch for an initial check.

A well-built AWS VPC creates clear network boundaries for security and scaling. When the core layers are structured correctly from the start, systems stay predictable, compliant, and easier to operate as traffic and data grow. What Is a VPC in AWS? A Virtual Private Cloud (VPC) is an isolated virtual network that AWS provisions exclusively for each account—essentially your own private territory inside the AWS ecosystem. Within this environment, you control every part of the network design: choosing IP ranges, creating subnets, defining routing rules, and attaching gateways. Unlike traditional on-premise networking, where infrastructure must be built and maintained manually, an AWS VPC lets you establish enterprise-grade network boundaries with far less operational overhead. A well-designed VPC is the foundation of any workload deployed on AWS. It determines how traffic flows, which components can reach the internet, and which must remain fully isolated. Thinking of a VPC as a planned digital neighborhood makes the concept easier to grasp—each subnet acts like a distinct zone with its own purpose, access rules, and connectivity model. This structured layout is what enables secure, scalable, and resilient cloud architectures. Standard Architecture Used in Real Systems When designing a VPC, the first step is understanding the core networking components that every production architecture is built on. These components define how traffic moves, which resources can reach the Internet, and how isolation is enforced across your workloads. Once these fundamentals are clear, the three subnet layers—Public, Private, and Database—become straightforward to structure. Core VPC Components Subnets The VPC is divided into logical zones: Public: Can reach the Internet through an Internet Gateway Private: No direct Internet access; outbound traffic goes through a NAT Gateway Isolated: No Internet route at all (ideal for databases) Route Tables: Control how each subnet sends traffic: Public → Internet Gateway Private → NAT Gateway Database → local VPC routes only Internet Gateway (IGW): Allows inbound/outbound Internet connectivity for public subnets NAT Gateway: Enables outbound-only Internet access for private subnets Security Groups: Stateful, resource-level firewalls controlling application-to-application access. Network ACLs (NACLs): Stateless rules at the subnet boundary, used for hardening VPC Endpoints: Enable private access to AWS services (S3, DynamoDB) without traversing the public Internet. Each component above plays a specific role, but they only become meaningful when arranged into subnet layers. IGW only makes sense when attached to public subnets. NAT Gateway is only useful when private subnets need outbound access. Route tables shape the connectivity of each layer. Security Groups control access between tier to tier. This is why production VPCs are structured into three tiers: Public, Private, and Database. Now we can dive into each tier. Public Subnet (Internet-Facing Layer) Public subnets contain the components that must receive traffic from the Internet, such as: Application Load Balancer (ALB) AWS WAF for Layer-7 protection CloudFront for global edge delivery Route 53 for DNS routing This ensures inbound client traffic always enters through tightly controlled entry points—never directly into the application or database layers. Private Subnet (Application Layer) Private subnets host the application services that should not have public IPs. These typically include: ECS Fargate or EC2 instances for backend workloads Auto Scaling groups Internal services communicating with databases Outbound access (for package updates, calling third-party APIs, etc.) is routed through a NAT Gateway placed in a public subnet. Because traffic can only initiate outbound, this layer protects your application from unsolicited Internet access while allowing it to function normally. Database Subnet (Isolated Layer) The isolated subnet contains data stores such as: Amazon RDS (Multi-AZ) Other managed database services This layer has no direct Internet route and is reachable only from the application tier via Security Group rules: This strict isolation prevents any external traffic from reaching the database, greatly reducing risk and helping organizations meet compliance standards like PCI DSS and GDPR. AWS VPC Best Practices You Should Apply in 2025 Before applying any best practices, it’s worth checking whether your current VPC is already showing signs of architectural stress. Common indicators include running out of CIDR space, applications failing to scale properly or difficulty integrating hybrid connectivity such as VPN or Direct Connect. When these symptoms appear, it’s usually a signal that your VPC needs a structural redesign rather than incremental fixes. To address these issues consistently, modern production environments follow a standardized network layout: Public, Private Application, and Database subnets, combined with a controlled, one-directional traffic flow between tiers. This structure is widely adopted because it improves security boundaries, simplifies scaling, and ensures compliance across sensitive workloads. #1 — Public Subnet (Internet-Facing Layer) Location: Two subnets distributed across two Availability Zones (10.0.1.0/24, 10.0.2.0/24) Key Components: Application Load Balancer (ALB) with ACM SSL certificates AWS WAF for Layer-7 protection CloudFront as the edge CDN Route 53 for DNS resolution Route Table: 0.0.0.0/0 → Internet Gateway Purpose: This layer receives external traffic from web or mobile clients, handles TLS termination, filters malicious requests, serves cached static content, and forwards validated requests into the private application layer. #2 — Private Subnet (Application Tier) Location: Two subnets across two AZs (10.0.3.0/24, 10.0.4.0/24) Key Components: ECS Fargate services: Backend APIs (Golang) Frontend build pipelines (React) Auto Scaling Groups adapting to CPU/Memory load Route Table: 0.0.0.0/0 → NAT Gateway Purpose: This tier runs all business logic without exposing any public IPs. Workloads can make outbound calls through the NAT Gateway, but inbound access is restricted to the ALB. This setup ensures security, scalability, and predictable traffic control. #3 — Database Subnet (Isolated Layer) Location: Two dedicated subnets (10.0.5.0/24, 10.0.6.0/24) Key Components: RDS PostgreSQL with Primary + Read Replica Multi-AZ deployment for high availability Route Table: 10.0.0.0/16 → Local (No Internet route) Security: Security Group: Allow only connections from the Application Tier SG on port 5432 NACL rules: Allow inbound 5432 from 10.0.3.0/24 and 10.0.4.0/24 Deny all access from public subnets Deny all other inbound traffic Encryption at rest (KMS) and TLS in-transit enabled Purpose: Ensures the database remains fully isolated, protected from the Internet, and reachable only through controlled, auditable application-layer traffic. #4 — Enforcing a Secure, One-Way Data Flow No packet from the Internet ever reaches RDS directly. No application container has a public IP. Every hop is enforced by Security Groups, NACL rules, and IAM policies. Purpose: This structured, predictable flow minimizes the blast radius, improves auditability, and ensures compliance with security frameworks such as PCI DSS, GDPR, and ISO 27001. Deploying This Architecture With Terraform (Code Example) Using Terraform to manage your VPC (the classic aws vpc terraform setup) turns your network design into version-controlled, reviewable infrastructure. It keeps dev/stage/prod environments consistent, makes changes auditable, and prevents configuration drift caused by manual edits in the AWS console. Below is a full Terraform example that builds the VPC and all three subnet tiers according to the architecture above. 1. Create the VPC Defines the network boundary for all workloads. 2. Public Subnet + Internet Gateway + Route Table Public subnets require an Internet Gateway and a route table allowing outbound traffic. 3. Private Application Subnet + NAT Gateway Allows outbound Internet access without exposing application workloads. 4. Database Subnet — No Internet Path Database subnets must remain fully isolated with local-only routing. 5. Security Group for ECS Backend Restricts inbound access to only trusted ALB traffic. 6. Security Group for RDS — Only ECS Allowed Ensures the database tier is reachable only from the application layer. 7. Attach to ECS Fargate Service Runs the application inside private subnets with the correct security boundaries. Common VPC Mistakes Make (And How to Avoid Them) Many VPC issues come from a few fundamental misconfigurations that repeatedly appear in real deployments. 1. Putting Databases in Public Subnets A surprising number of VPCs place RDS instances in public subnets simply because initial connectivity feels easier. The problem is that this exposes the database to unnecessary risk and breaks most security and compliance requirements. Databases should always live in isolated subnets with no path to the Internet, and access must be restricted to application-tier Security Groups. 2. Assigning Public IPs to Application Instances Giving EC2 or ECS tasks public IPs might feel convenient for quick access or troubleshooting, but it creates an unpredictable security boundary and drastically widens the attack surface. Application workloads belong in private subnets, with outbound traffic routed through a NAT Gateway and operational access handled via SSM or private bastion hosts. 3. Using a Single Route Table for Every Subnet One of the easiest ways to break VPC isolation is attaching the same route table to public, private, and database subnets. Traffic intended for the Internet can unintentionally propagate inward, creating routing loops or leaking connectivity between tiers. A proper design separates route tables: public subnets route to IGW, private subnets to NAT Gateways, and database subnets stay local-only. 4. Choosing a CIDR Block That’s Too Small Teams often underestimate growth and allocate a VPC CIDR so narrow that IP capacity runs out once more services or subnets are added. Expanding a VPC later is painful and usually requires migrations or complex peering setups. Starting with a larger CIDR range gives your architecture room to scale without infrastructure disruptions. Conclusion A clean, well-structured VPC provides the security, scalability, and operational clarity needed for any serious AWS workload. Following the 3-tier subnet model and enforcing predictable data flows keeps your environment compliant and easier to manage as the system grows. If you’re exploring how to apply these principles to your own infrastructure, Haposoft’s AWS team can help review your architecture and recommend the right improvements. Feel free to get in touch if you’d like expert guidance.

The React team has disclosed additional security vulnerabilities affecting React Server Components, discovered while researchers were testing the effectiveness of last week’s critical patch (React2Shell). While these newly identified issues do not enable Remote Code Execution, they introduce serious risks, including Denial of Service (DoS) attacks and potential source code exposure. Due to their severity, immediate upgrades are strongly recommended. Overview of the Newly Disclosed Vulnerabilities Security researchers identified two new vulnerability classes in the same React Server Components packages affected by CVE-2025-55182. High Severity: Denial of Service (DoS) CVE-2025-55184 CVE-2025-67779 CVSS Score: 7.5 (High) A maliciously crafted HTTP request sent to a Server Function endpoint can trigger an infinite loop during deserialization, causing the server process to hang and consume CPU indefinitely. Notably, even applications that do not explicitly define Server Functions may still be vulnerable if they support React Server Components. This vulnerability enables attackers to: Disrupt service availability Degrade server performance Potentially cause cascading infrastructure impact The React team has confirmed that earlier fixes were incomplete, leaving several patched versions still vulnerable until this latest release. Medium Severity: Source Code Exposure CVE-2025-55183 CVSS Score: 5.3 (Medium) Researchers discovered that certain malformed requests could cause Server Functions to return their own source code when arguments are explicitly or implicitly stringified. This may expose: Hardcoded secrets inside Server Functions Internal logic and implementation details Inlined helper functions, depending on bundler behavior Important clarification: Only source-level secrets may be exposed. Runtime secrets such as process.env.SECRET are not affected. What Is Affected and Who Needs to Take Action The newly disclosed vulnerabilities impact the same React Server Components packages as the previously reported issue, and affect a range of commonly used frameworks and bundlers. Teams should review their dependency tree carefully to determine whether an upgrade is required. Affected Packages and Versions These vulnerabilities affect the same packages and version ranges as the previously disclosed React Server Components issue. Affected packages react-server-dom-webpack react-server-dom-parcel react-server-dom-turbopack Vulnerable versions 19.0.0 → 19.0.2 19.1.0 → 19.1.3 19.2.0 → 19.2.2 Fixed Versions (Required Upgrade) The React team has backported fixes to the following versions: 19.0.3 19.1.4 19.2.3 If your project uses any of the affected packages, upgrade immediately to one of the versions above. ⚠️ If you already updated last week, you still need to update again. Versions 19.0.2, 19.1.3, and 19.2.2 are not fully secure. Impacted Frameworks and Bundlers Several popular frameworks and tools depend on or bundle the vulnerable packages, including: Next.js React Router Waku @parcel/rsc @vite/rsc-plugin rwsdk Refer to your framework’s upgrade instructions to ensure the correct patched versions are installed. Who Is Not Affected Apps that do not use a server Apps not using React Server Components Apps not relying on frameworks or bundlers that support RSC React Native Considerations React Native applications that do not use monorepos or react-dom are generally not affected by these vulnerabilities. For React Native projects using a monorepo, only the following packages need to be updated if they are installed: react-server-dom-webpack react-server-dom-parcel react-server-dom-turbopack Upgrading these packages does not require updating react or react-dom and will not cause version mismatch issues in React Native. Recommended Solutions and Mitigation Strategy While upgrading to the fixed versions is mandatory, these vulnerabilities also expose broader weaknesses in dependency management and secret handling that teams should address to reduce future risk. Immediate Fix All affected applications should upgrade immediately to one of the patched versions: 19.0.3 19.1.4 19.2.3 Previously released patches were incomplete, and hosting provider mitigations should be considered temporary safeguards only, not a long-term solution. Updating to the fixed versions remains the only reliable mitigation. Automate Dependency Updates to Reduce Exposure Time Modern JavaScript ecosystems make it difficult to manually track security advisories across all dependencies. Using tools such as Renovate or Dependabot helps automatically detect vulnerable versions and create upgrade pull requests as soon as fixes are released. This reduces response time and lowers the risk of running partially patched or outdated packages in production. Ensure CI/CD Pipelines Can Absorb Security Upgrades Safely Frequent dependency upgrades are only safe when supported by reliable automated testing. Maintaining comprehensive CI/CD pipelines with sufficient test coverage allows teams to apply security updates quickly while minimizing the risk of breaking changes. This enables faster remediation when new vulnerabilities are disclosed. Remove Secrets from Source Code to Limit Blast Radius Secrets embedded directly in source code may be exposed if similar vulnerabilities arise again. Store secrets using managed services such as AWS SSM Parameter Store or AWS Secrets Manager Implement key rotation mechanisms without downtime Even if source code is exposed, properly managed runtime secrets significantly limit real-world impact. Why Follow-Up CVEs Are Common After Critical Disclosures It is common for critical vulnerabilities to uncover additional issues once researchers begin probing adjacent code paths. When an initial fix is released, security researchers often attempt to bypass it using variant exploit techniques. This pattern has appeared repeatedly across the industry. A well-known example is Log4Shell, where multiple follow-up CVEs were reported after the first disclosure. While additional disclosures can be frustrating, they usually indicate: Active security review Responsible disclosure A healthy patch and verification cycle Final Notes Some hosting companies set up quick fixes, yet those aren't enough on their own. Keeping dependencies updated is still a top way to stay safe from new supply-chain risks. If your application uses React Server Components, reach out to Haposoft now! We'll figure out what’s impacted while taking care of the update without mess. It means going through your dependencies one by one, making sure everything builds right in the end.

On December 3, 2025, the React team revealed a critical Remote Code Execution vulnerability in React Server Components (RSC). It affects several RSC packages and some of the most widely used React frameworks, including Next.js. A fix is already out, so the urgent step now is simply checking whether your project uses these packages—and updating to the patched versions if it does. Overview of the Vulnerability A newly reported flaw allows unauthenticated Remote Code Execution (RCE) on servers running React Server Components. Type: Unauthenticated Remote Code Execution CVE: CVE-2025-55182 (NIST , GitHub Advisory Database) Severity: CVSS 10.0 (Maximum severity) This means an attacker could execute arbitrary code on the server without any form of authentication, giving them full control of the affected environment. The issue is caused by a flaw in how React decodes payloads sent to React Server Function endpoints. A maliciously crafted HTTP request can trigger unsafe deserialization, leading to remote code execution. React will publish additional technical details once the patch rollout is fully completed. Scope of Impact Any application that supports React Server Components may be exposed, even if it never defines any Server Function endpoints. The vulnerability exists in the underlying RSC support layer used by multiple frameworks and bundlers. Your application is not vulnerable if: Your React code does not run on a server, or Your application does not use a framework, bundler, or plugin that supports React Server Components. Traditional client-only React applications are unaffected. Affected Versions and Components The vulnerability is tied to specific versions of the React Server Components packages and to the frameworks that depend on them. Identifying whether your project uses any of these versions is the first step in determining your exposure. Vulnerable Packages The issue affects the following packages in versions 19.0, 19.1.0, 19.1.1, and 19.2.0: react-server-dom-webpack react-server-dom-parcel react-server-dom-turbopack Affected Frameworks and Bundlers Several frameworks that rely on these packages are also impacted, including: Next.js React Router (when using unstable RSC APIs) Waku @parcel/rsc @vitejs/plugin-rsc Redwood SDK Security Fix and Recommended Actions The React team has released patched versions, and major frameworks have issued corresponding updates. Applying these fixes promptly is the only reliable way to remove the vulnerability from affected projects. Patched Versions The React team has released fixed versions: 19.0.1 19.1.2 19.2.1 (or any version newer than these) Upgrading to a patched release is mandatory to eliminate the vulnerability. Framework Updates Framework maintainers have also published security updates. For example, Next.js users must upgrade to one of the following patched versions: next@15.0.5 next@15.1.9 next@15.2.6 next@15.3.6 next@15.4.8 next@15.5.7 next@16.0.7 Other ecosystems (React Router, Redwood, Vite plugin, Parcel, Waku, etc.) also require upgrading to their latest patched versions. What Development Teams Should Do Now We recommend the following immediate steps: Audit all projects to confirm whether React Server Components or related frameworks are in use. Check package versions for the affected libraries listed above. Upgrade to the patched versions immediately if your application falls within the impacted scope. Review deployment environments for any unusual activity (optional but advisable for security). Document and report the findings to your internal security or project stakeholders. Conclusion This vulnerability (CVE-2025-55182) is one of the most severe vulnerabilities ever disclosed within the React ecosystem, and it may impact a wide range of modern React-based applications. To maintain security and prevent potential exploitation, all teams should: Review their applications, Identify affected components, and Apply the necessary upgrades without delay. If you need a security audit or patch support within your React-based web development projects, Haposoft is ready to step in.

Workloads spike, drop, and shift without warning, and fixed servers rarely keep up. AWS Lambda serverless architecture approaches this with a simple idea: run code only on events, scale instantly, and remove the burden of always-on infrastructure. It’s a model that reshapes how event-driven systems are designed and operated. Architecture of a Serverless System with AWS Lambda Event-driven systems depend on a few core pieces, and aws lambda serverless architecture keeps them tight and minimal. Everything starts with an event source, flows through a small, focused function, and ends in a downstream service that stores or distributes the result. Event Sources AWS Lambda is activated strictly by events. Typical sources include: S3 when an object is created or updated API Gateway for synchronous HTTP calls DynamoDB Streams for row-level changes SNS / SQS for asynchronous message handling Kinesis / EventBridge for high-volume or scheduled events CloudWatch Events for cron-based triggers Each trigger delivers structured context (request parameters, object keys, stream records, message payloads), allowing the function to determine the required operation without maintaining state between invocations. Lambda Function Layer Lambda functions are designed to remain small and focused. A function typically performs a single operation such as transformation, validation, computation, or routing. The architecture assumes: Stateless execution: no in-memory persistence between invocations. Externalized state: stored in services like S3, DynamoDB, Secrets Manager, or Parameter Store. Short execution cycles: predictable runtime and reduced cold-start sensitivity. Isolated environments: each invocation receives a dedicated runtime sandbox. This separation simplifies horizontal scaling and keeps failure domains small. Versioning and Aliases Lambda versioning provides immutable snapshots of function code and configuration. Once published, a version cannot be modified. Aliases act as pointers to specific versions (e.g., prod, staging, canary), enabling controlled traffic shifting. Typical scenarios include: Blue/Green Deployment: switch alias from version N → N+1 in one step. Canary Deployment: shift partial traffic to a new version. Rollback: repoint alias back to the previous version without redeploying code. This mechanism isolates code promotion from code packaging, making rollouts deterministic and reversible. Concurrency and Scaling Lambda scales by launching separate execution environments as event volume increases. AWS handles provisioning, lifecycle, and teardown automatically. Invocation-level guarantees ensure that scaling behavior aligns with event volume without manual intervention. Key controls include: Reserved Concurrency — caps the maximum number of parallel executions for a function to protect downstream systems (e.g., DynamoDB, RDS, third-party APIs). Provisioned Concurrency — keeps execution environments warm to minimize cold-start latency for latency-sensitive or high-traffic endpoints. Burst limits — define initial scaling throughput across regions. Reference Pipeline (S3 → Lambda → DynamoDB/SNS → Glacier) A common pattern in aws lambda serverless architecture is event-based data processing. This pipeline supports workloads such as media ingestion (VOD), IoT telemetry, log aggregation, ETL preprocessing, and other burst-driven data flows. Example flow: Integration Patterns in AWS Lambda Serverless Architecture Lambda typically works alongside other AWS services to support event-driven workloads. Most integrations fall into a few recurring patterns below. Lambda + S3 When new data lands in S3, Lambda doesn’t receive the file — it receives a compact event record that identifies what changed. Most of the logic starts by pulling the object or reading its metadata directly from the bucket. This integration is built around the idea that the arrival of data defines the start of the workflow. Typical operations Read the uploaded object Run validation or content checks Produce transformed or derivative outputs Store metadata or results in DynamoDB or another S3 prefix Lambda + DynamoDB Streams This integration behaves closer to a commit log than a file trigger. DynamoDB Streams guarantee ordered delivery per partition, and Lambda processes batches rather than single items. Failures reprocess the entire batch, so the function must be idempotent. Use cases tend to fall into a few categories: updating read models, syncing data to external services, publishing domain events, or capturing audit trails. The “before” and “after” images included in each record make it possible to detect exactly what changed without additional queries. Lambda + API Gateway Unlike S3 or Streams, the API Gateway path is synchronous. Lambda must complete within HTTP latency budgets and return a well-formed response. The function receives a full request context—headers, method, path parameters, JWT claims—and acts as the application logic behind the endpoint. A minimal handler usually: Validates the inbound request Executes domain logic Writes or reads from storage Returns JSON with proper status codes No queues, no retries, no batching—just request/response. This removes the need for EC2, load balancers, or container orchestration for API-level traffic. Lambda + Step Functions Here Lambda isn’t reacting to an event, it’s being invoked as part of a workflow. Step Functions control timing, retries, branching, and long-running coordination. Lambda performs whatever unit of work is assigned to that state, then hands the result back to the state machine. Workloads that fit this pattern: multi-stage data pipelines approval or review flows tasks that need controlled retries processes where orchestration is more important than compute Lambda + Messaging (SNS, SQS, EventBridge, Kinesis) Each messaging service integrates with Lambda differently: SNS delivers discrete messages for fan-out scenarios. One message → one invocation. SQS provides queue semantics; Lambda polls, receives batches, and must delete messages explicitly on success. EventBridge routes structured events based on rules and supports cross-account buses. Kinesis enforces shard-level ordering, and Lambda processes batches sequentially per shard. Depending on the source, the function may need to handle batching, ordering guarantees, partial retries, or DLQ routing. This category is the most varied because the semantics are completely different from one messaging service to another. Recommended Setup for AWS Lambda Serverless Architecture A practical baseline configuration that reflects typical usage patterns and cost behavior for a Lambda-based event-driven system. Technical Recommendations A stable Lambda-based architecture usually follows a small set of practical rules that keep execution predictable and operations lightweight: Function Structure Keep each Lambda focused on one task (SRP). Store configuration in environment variables for each environment (dev/staging/prod). Execution Controls Apply strict timeouts to prevent runaway compute and unnecessary billing. Enable retries for async triggers and route failed events to a DLQ (SQS or SNS). Security Assign least-privilege IAM roles so each function can access only what it actually needs. Observability Send logs to CloudWatch Logs. Use CloudWatch Metrics and X-Ray for tracing, latency analysis, and dependency visibility. Cost Profile and Expected Savings Below is a reference cost breakdown for a typical Lambda workload using the configuration above: Component Unit Price Usage Monthly Cost Lambda Invocations $0.20 / 1M 3M ~$0.60 Lambda Compute (512 MB, 200 ms) ~$0.0000008333 / ms ~600M ms ~$500 S3 Storage (with lifecycle) ~$0.023 / GB ~5 TB ~$115 Total – – ≈ $615/month With this model, teams typically see 40–60% lower cost compared to fixed server-based infrastructures, along with near-zero operational overhead because no servers need to be maintained or scaled manually. Cost Optimization Tips Lambda charges based on invocations + compute time, so smaller and shorter functions are naturally cheaper. Event-driven triggers ensure you pay only when real work happens. Apply multi-tier S3 storage: Standard → Standard-IA → Glacier depending on access frequency. Conclusion A serverless architecture aws lambda works best when the system is designed around clear execution paths and predictable event handling. With the right structure in place, the platform stays stable and cost-efficient even when workloads spike unexpectedly. Haposoft is an AWS consulting partner with hands-on experience delivering serverless systems using Lambda, API Gateway, S3, DynamoDB and Step Functions. We help teams review existing architectures, design new AWS workloads and optimize cloud cost without disrupting operations. If you need a practical, production-ready serverless architecture, Haposoft can support you from design to implementation.

When you submit an app to Google Play, most early failures surface in Closed Testing, not the final review. What we share here comes from real testing practice, and it’s what made handling those failures predictable for us. What Google Play Closed Testing Is Closed Testing is where Google first checks your app using real user activity, so it matters to understand what this stage actually requires. Where Closed Testing Fits in the Submission Process When you submit an app to Google Play, it doesn’t go straight to the final review. Before reaching that stage, every build must pass through Google’s internal testing tracks—Internal Testing → Closed Testing → Open Testing. Closed Testing sits in the middle of this flow and is the first point where Google expects real usage from real users. If the app fails here, it never reaches the actual “Submit for Review” step. That’s why many teams face repeated rejections without realizing the root cause comes from this stage, not the final review. Google Play Closed Testing in Simple Terms Google Play Closed Testing is a private release track where your app is shared with a small group of testers you select. These testers install the real build you intend to ship and use it in everyday conditions. The goal is straightforward: Google wants to see whether the app behaves like a complete product when real people interact with it. In this controlled environment, Google observes how users move through your features, how data is handled, and whether the experience matches what you describe in your Play Console settings. This is essentially Google’s early check to confirm that the app is stable, transparent, and built for genuine use—not just something assembled to pass review. What Google Expects During Closed Testing The core function of Google Play Closed Testing is to verify authenticity. Google wants evidence that your app is functional, transparent, and ready for real users, not a rushed build created solely to pass review. To make this evaluation, Google looks for a few key signals: Real testers using real, active Google accounts Real usage patterns, not one-off opens or artificial interactions Consistent engagement over time, typically around 14 days for most app types Actions inside your core features, not empty screens or placeholder flows Behavior that aligns with your Data Safety, privacy details, and feature declarations Evidence that the app is “alive”, such as logs, events, and navigation patterns generated from authentic interactions Google began tightening its review standards in 2023 after more unfinished and auto-generated apps started slipping into the submission flow. Instead of relying only on manual checks, Google now leans heavily on the activity recorded during Closed Testing to understand how an app performs under real use. This gives the review team a clearer picture of stability, data handling, and readiness—making Closed Testing a much more decisive step in whether an app moves forward. Why Google Play Closed Testing Is So Hard to Pass Most teams fail Closed Testing because their testing behavior doesn’t match the actual evaluation signals Google uses. The table below compares real developer mistakes with Google’s real criteria, so you can see exactly why each issue leads to rejection. Common Issues During Testing What Google Actually Checks Teams treat Closed Testing like internal QA. Testers only tap around the interface and rarely complete real user journeys. Google checks full, natural flows. It expects onboarding → core action → follow-up action. Shallow tapping does not confirm real functionality, so Google marks the test as lacking behavioral proof. Testers open the app once or twice and stop. Most activity happens on day 1, then engagement drops to zero. Google checks multi-day usage patterns. It needs recurring activity to evaluate stability and real adoption. One-off launches look like artificial or incomplete testing → fail. Core features remain untouched because testers don’t find or understand them. Navigation confusion prevents users from triggering important flows. Google checks whether declared core features are actually used. If users don’t naturally reach those flows, Google cannot validate them → flagged as “unverified behavior.” Permissions are declared but no tester enters flows that use them. e.g., camera, location, contacts, or other data-related actions never get triggered. Google cross-checks declared permissions with real behavior. If a permission never activates during testing, Google treats the Data Safety form as unverifiable → extremely high rejection rate. Engagement collapses after the first day. Testers lose interest quickly, resulting in long periods of zero activity. Google checks consistency over time (≈14 days). When usage dies early, the system sees weak, unreliable activity that does not resemble real-world usage → rejection. Passing Google Play Closed Testing: A Real Case Study Closed Testing turned out to be far stricter than we expected. What looked like a simple pre-release step quickly became the most decisive part of the review, and our team had to learn this the hard way—through three consecutive rejections before finally getting approved. The Three Issues That Held Us Back in Closed Testing These were the three recurring problems that blocked our app from moving past Google Play’s Closed Testing stage. #Issue 1: Having Testers, but Not Enough “Real” Activity In the first attempt, we only invited one person to join the test, so the app barely generated any meaningful activity. Most of the usage stopped at simple screen opens, and none of the core features were exercised in a way Google could evaluate. With such a small and shallow pattern, the system couldn't treat it as real user participation. The build was rejected right away for not meeting the minimum level of authentic activity. #Issue 2: Misunderstanding the “14-Day Activity” Requirement For the second round, we expanded the group to twelve testers, but most of them stopped using the app after just a few days. The remaining period showed almost no engagement, which meant the full 14-day window Google expects was never actually covered. Although the number of testers looked correct, the lack of continuous usage made the test inconclusive. Google dismissed the submission because the activity dropped off too early. #Issue 3: No Evidence of Real Activity (Logs, Tracking, or Records) By the third attempt, we finally kept twelve testers active for the entire duration, but we failed to capture what they did. There were no logs showing feature flows, no tracking to confirm event sequences, and no recordings for actions tied to sensitive permissions. From Google's viewpoint, the numbers in the dashboard had nothing to support them. Without verifiable evidence, the review team treated the activity as unreliable and rejected the build again. What Finally Helped Us Pass Google Play Closed Testing To fix the issues in the earlier attempts, the team reorganized the entire test instead of adding more testers at random. Everything was structured so Google could see consistent, authentic behaviour from real users. A larger tester group created a more reliable activity curve The previous rounds didn’t generate enough meaningful activity, so we increased the number of people involved. The larger group created a more natural engagement pattern that gave Google more complete usage signals to review. Extending the testing period from 14 to 17 consecutive days To avoid the early drop-off that hurt our earlier attempts, we kept the test running a little longer than the minimum 14 days. The longer duration prevented mid-test gaps and helped Google see continuous interaction across multiple days. Introducing a detailed daily checklist so testers covered the right flows Instead of letting everyone tap around freely, we provided a short list of the core actions Google needed to observe. A clear checklist guided testers through specific actions each day, producing consistent evidence for the features Google needed to verify. Enabling device-level tracking and full system logs Earlier data was too thin to validate behaviour, so we enabled device-level tracking and full system logs to review and later align with Google’s dashboard. This fixed the “invisible activity” issue from the earlier rounds and gave the review team something concrete to validate. Having testers record short videos of their actions Some flows involving permissions weren’t reflected clearly in logs, so testers recorded short clips when performing these tasks. These videos provided direct confirmation of how camera, file access and upload flows worked. Adding small features and content to encourage natural engagement The previous builds didn’t encourage repeated use, so we added minor features and content updates to create more realistic daily engagement. These adjustments helped testers interact with the app in a way that resembled real usage, not surface-level taps. Release Access Form: A Commonly Overlooked Step in the Approval Process After Closed Testing is completed, Google requires developers to submit the Release Access Form before the app can move forward in the publishing process. It sounds simple, but the way this form is written has a direct influence on the final review. Taking the form seriously, paired with the testing evidence we had already prepared, helped our final submission go through smoothly on the fourth attempt. Here’s what became clear when we worked through it: The answers must reflect the real behaviour of the app — especially the sections on intended use and where user data comes from. Any mismatch creates doubt. Google expects clear descriptions of features, user actions and the scope of testing. Vague explanations often slow the process down. Looking at how other developer communities handled this form helped us understand the phrasing that aligns with Google’s criteria. Final Thoughts Closed Testing is ultimately about proving that your app behaves like a real, ready-to-ship product. Most teams lose time because they only react after a rejection; we prevent 80% of those rejections long before you ship. If you want fewer surprises and a tighter, lower-risk review cycle, talk to us and Haposoft will run the entire review cycle for you.

Cloudflare is facing a major global outage that has disrupted DNS resolution, CDN traffic and several core network services. The issue began early on November 18, 2025 and quickly affected many of the world’s biggest platforms, including OpenAI, X, Canva and other sites that rely on Cloudflare for performance and security. As Cloudflare works to stabilize its systems, many websites may load slowly, show error messages or fail to respond entirely. This guide explains what is happening and what you can do right now to keep your website online. What’s Going On With Cloudflare Right Now? Cloudflare is investigating a major global outage that began around 6:40 a.m. ET on 18 November 2025 and quickly triggered elevated error rates across multiple regions. Thousands of users reported HTTP 500 internal errors, failed API calls and an inability to access Cloudflare’s dashboard or API endpoints. According to multiple reports from Reuters, AP News and Tom’s Hardware, websites depending on Cloudflare’s CDN or proxy layer simply stopped loading. High-profile platforms including OpenAI, X and Canva were among the most visibly affected, with users encountering timeouts, missing content or Cloudflare challenge errors when trying to access core features. Cloudflare’s CEO acknowledged the disruption and noted that the company saw an unexpected spike in traffic and CPU load that impacted both primary and secondary systems. This instability rippled across Cloudflare’s network, which carries more than 20 percent of global web traffic, according to the Financial Times. While some regions are showing early signs of recovery, Cloudflare has warned that intermittent downtime may continue until the network fully stabilizes. Why So Many Services Went Down Cloudflare’s outage is touching several critical layers of its global network. This is why so many unrelated platforms are failing at the same time. While the scope may vary by region, most disruptions fall into four main areas. These are the services experiencing the most visible impact: DNS resolution: Domains may fail to resolve entirely or return intermittent NXDOMAIN and SERVFAIL errors, making websites appear offline even if servers are healthy. CDN and edge delivery: Users may see slow loading, missing content or 522 and 523 connection errors as traffic struggles to reach Cloudflare’s edge locations. API and Workers: Developers may notice higher latency, failed executions or dropped requests due to instability in Cloudflare’s compute and routing layer. Zero Trust and Email Routing: Authentication, access policies and email rewriting may behave inconsistently, causing login delays or undelivered messages. Websites may appear offline even though the backend is functioning normally. APIs may time out or fail entirely. Some platforms experience slower global performance due to degraded edge capacity. Email routing and authentication services relying on Cloudflare may process more slowly or return errors. For businesses building on Cloudflare, these issues can interrupt workflows, customer access and production systems until the network fully recovers. Emergency Steps to Keep Your Website Running If your website or API relies on Cloudflare, you can take several immediate actions to restore access while Cloudflare continues to recover. These steps focus on bypassing unstable Cloudflare layers and re-routing critical traffic. 1. If Your Domain Uses Cloudflare DNS Moving your domain away from Cloudflare’s DNS temporarily can restore service for most websites. What to do: Change your NameServers back to your domain registrar’s defaults (GoDaddy, Namecheap, MatBao, PAVietnam and others). Or switch to a reliable alternative such as Amazon Route 53. Recreate your existing DNS records (A, AAAA, CNAME, MX and TXT) exactly as they were. This ensures DNS resolution is handled by a stable provider until Cloudflare fully recovers. 2. If You Use Cloudflare Proxy or CDN Cloudflare’s orange-cloud proxy is heavily affected during global outages. Disabling it allows traffic to go directly to your server. You can: Turn off proxy mode so the DNS entry becomes DNS Only. Or point your domain directly to your server’s IP using another DNS provider. This bypasses Cloudflare’s edge entirely and routes requests straight to your origin. 3. If You Rely on Cloudflare Workers, Email Routing or Zero Trust These services may not function reliably under current conditions. Temporary workarounds include: Switching back to your original email provider’s MX records such as Google Workspace, Microsoft 365 or any self-hosted solution. Routing API traffic directly to your backend servers instead of through Workers. Pausing Zero Trust policies that depend on Cloudflare for authentication. Important Notes DNS propagation can take anywhere from a few minutes to an hour depending on your TTL. Do not delete your Cloudflare zone. This complicates restoration once the network stabilizes. Large websites or systems under heavy traffic should test load immediately after switching. Preventing Future Outages While Cloudflare is usually reliable, this incident shows how a single point of failure can affect many unrelated platforms. Businesses that depend on Cloudflare for DNS, CDN, security and API routing should plan for resilience rather than assuming perfect uptime. Build DNS Redundancy DNS is the first layer that fails during a Cloudflare outage. Having a secondary DNS provider allows your domain to stay reachable even if one provider goes down. Reliable options include: ​​​​​Amazon Route 53 Google Cloud DNS NS1 Akamai DNS Made Easy A multi-DNS setup ensures that traffic can be rerouted instantly whenever one network experiences instability. Use More Than One CDN When Possible If your website or application relies heavily on Cloudflare’s edge, consider using a backup CDN for static assets or heavy traffic routes. This prevents a full shutdown if Cloudflare’s delivery network becomes slow or unavailable. Common backup choices include Fastly, CloudFront or Akamai. Design Systems for Failure Modern applications need to assume that providers can fail unexpectedly. A resilient architecture spreads critical services across multiple layers and avoids complete reliance on any single vendor. Practical improvements: Keep a direct IP access path for emergencies Store a copy of static assets outside Cloudflare Use health checks that can switch traffic when errors spike Avoid routing core authentication or critical APIs through a single proxy By preparing ahead, you reduce the risk of a global outage disrupting your customers or internal operations. Final Thoughts and How Haposoft Can Support You Today’s Cloudflare outage is a reminder that even the most trusted internet providers can experience large-scale failures. When core layers like DNS, CDN or security proxies go down, the ripple effect reaches millions of users and businesses within minutes. The best defense is preparation: redundancy, fallback routing and resilient infrastructure. If your website or system is still experiencing issues or you want to avoid disruptions like this in the future, Haposoft can step in immediately. Haposoft Can Help You Stabilize Your Website Right Now Our team can assist with: Reconfiguring DNS records on Route 53 or your registrar Bypassing Cloudflare proxy and routing traffic directly to your servers Restoring API access and email flow without waiting for Cloudflare’s full recovery We can guide you through the entire process so your website comes back online as fast as possible. Improve Reliability with Haposoft’s AWS Solutions Beyond emergency fixes, Haposoft provides end-to-end AWS consulting to help you build stronger and more resilient systems. Our AWS services include: Designing multi-DNS and multi-region architecture Setting up Route 53 with health checks and failover routing Deploying CloudFront as a high-availability CDN alternative Migrating critical services to fault-tolerant AWS infrastructure Implementing monitoring, alerts and disaster-recovery plans If you want your platform to withstand outages like today’s event, Haposoft can help you build the kind of cloud architecture that stays online even when major providers stumble.

As VOD libraries expand, broadcasters face rising storage demands and slower data access. To address that, we propose a model using Amazon S3 video storage that keeps media scalable, secure, and cost-efficient over time. Why Amazon S3 Video Storage Fits Modern VOD Workflows Launched on March 14 2006, Amazon S3 began as one of the first public cloud storage services. The current API version—2006-03-01—has remained stable for nearly two decades while continuously adding new capabilities such as lifecycle automation, reduced storage tiers, and improved console features. Over more than 15 years of updates, S3 has grown far beyond “a storage bucket” into a global object storage platform that supports replication, logging, and analytics at scale. According to Wikipedia, the number of stored objects increased from about 10 billion in 2007 to more than 400 billion in 2023—illustrating how it scales with worldwide demand for AWS cloud storage and video streaming workloads. Key technical advantages of Amazon S3 video storage: Scalability: Pay only for the data you use—no pre-provisioning or capacity limits. Durability: Designed for 99.999999999 percent (“11 nines”) data durability, ensuring media integrity over time. Cost flexibility: Multiple storage classes allow efficient tiering from frequently to rarely accessed content. Deep AWS integration: Works seamlessly with CloudFront, Lambda, Athena, and Glue to handle video processing and delivery. Security and compliance: Versioning, Object Lock, and CloudTrail logging meet broadcast-grade data-governance requirements. With this maturity, scalability, and reliability, Amazon S3 video storage has become the natural foundation for broadcasters building modern VOD systems. Solution Architecture: Multi-Tier VOD Storage on Amazon S3 The broadcasting team built its VOD system around Amazon S3 video storage to handle about 50 GB of new recordings each day — nearly 18 TB per year. The goal was simple: keep all video available, but spend less on storage that’s rarely accessed. Instead of treating every file the same, the data is separated by lifecycle. New uploads stay in S3 Standard for quick access, while older footage automatically moves to cheaper tiers such as Standard-IA and Glacier. Cross-Region Replication creates a copy in another region for disaster recovery, and versioning keeps track of every edit or replacement. This setup cuts monthly cost by more than half compared with storing everything in a single class. It also reduces manual work - files move, age, and archive automatically based on defined lifecycle rules. The rest of this section breaks down how the system works in practice. (AWS Best Practice) System Overview The storage system is split into a few simple parts, each doing one clear job. Primary S3 bucket (Region A – Singapore): This is where all new videos land after being uploaded from local studios. Editors and producers can access these files directly for a few months while the content is still fresh and often reused. Lifecycle rules for auto-tiering: After the first three months, the system automatically shifts older objects to cheaper storage tiers. It’s handled through lifecycle rules, so there’s no need to track or move files manually. Cross-Region Replication (Region B – Tokyo): Every new file is copied to another region for redundancy. If one region fails or faces downtime, all data can still be restored from the secondary location. Access control and versioning: Access policies define who can read or modify content, while versioning keeps a full history of changes — useful when editors replace or trim video files. Together, these components keep the VOD archive easy to manage: new content stays fast to access, archived footage stays safe, and everything costs far less than a one-tier setup. Optimizing with AWS Storage Classes Each phase of a video’s lifecycle maps naturally to a different AWS storage class. In the early stage, new uploads stay in S3 Standard, where editors still access them frequently for editing or scheduling broadcasts. After the first few months, when the files are mostly finalized, they shift to S3 Standard-IA, which keeps the same quick access speed but costs almost half as much. As the archive grows, older footage that is rarely needed moves automatically to S3 Glacier Instant Retrieval, where it remains available for years at a fraction of the price. Content that only needs to be retained for compliance or historical purposes can be stored safely in S3 Glacier Flexible Retrieval or Deep Archive, depending on how long it needs to stay accessible. This tiered structure keeps the storage lean and predictable. Costs fall gradually as data ages while every file remains retrievable whenever required, something that traditional on-premise systems rarely achieve. It allows broadcasters to manage expanding VOD libraries without overpaying for high-performance storage that most of their content no longer needs. Storage Class Use Case Access Speed Cost Level Typical Retention S3 Standard New uploads and frequently accessed videos Milliseconds High 0–90 days S3 Standard-IA Less-accessed content, still in rotation Milliseconds Medium 90–180 days S3 Glacier Instant Retrieval Older videos that may need quick access Milliseconds Low 6–12 months S3 Glacier Flexible Retrieval Archival content, rarely accessed Minutes to hours Very low 1–3 years S3 Glacier Deep Archive Historical backups or compliance data Hours Lowest 3+ years Automating Data Tiering with Amazon S3 Lifecycle Policy Manually tracking which videos are old enough to move to cheaper storage becomes unrealistic once the archive grows to terabytes. To avoid that, the team set up an Amazon S3 lifecycle policy that automatically transitions data between storage tiers depending on how long each object has been in the bucket. This approach removes manual work and ensures that every file lives in the right tier for its age and access frequency. The rule applies to all objects in the vod-storage-bucket. For roughly the first three months, videos remain in S3 Standard, where they are frequently opened by editors and producers for re-editing or rebroadcasting. After 90 days, the lifecycle rule moves those files to S3 Standard-IA, which keeps millisecond-level access speed but costs around 40% less. When videos reach about six months old, they are transitioned again to S3 Glacier Instant Retrieval, which provides durable, low-cost storage while still allowing quick restores when needed. After three years, the system automatically deletes expired files to keep the archive clean and avoid paying for data no one uses anymore. Below is the JSON configuration used for the policy: What this policy does: After 90 days, objects are moved from S3 Standard to S3 Standard-IA. After 180 days, the same objects move to S3 Glacier Instant Retrieval. After 3 years (1,095 days), the data is deleted automatically. This way, fresh content stays fast, older content stays cheap, and the archive never grows forever. Ensuring Redundancy with Cross-Region Replication (S3 CRR) When broadcasters archive years of video, the question isn’t just cost — it’s “what if a region goes down?” To keep content recoverable, the system enables S3 Cross-Region Replication (CRR). Each new or updated file in the primary bucket is automatically copied to a backup bucket in another AWS region. This setup uses a simple AWS CLI command: When CRR is active, every object uploaded to the vod-storage-bucket is duplicated in vod-backup-bucket, stored in a different region such as Tokyo. If the main region suffers an outage or data loss, the broadcaster can still restore or stream files from the backup. Besides disaster recovery, CRR supports compliance requirements that demand off-site backups and version protection. It also gives flexibility: the destination can use a lower-cost storage class, cutting replication expenses while keeping full data redundancy. Cost Analysis: Amazon S3 Pricing for VOD Workloads To evaluate the actual savings, the team estimated the monthly cost of storing roughly 18 TB of VOD data on Amazon S3. If everything stayed in S3 Standard, the cost would reach about $0.023 per GB per month, or nearly $414 USD in total. This flat setup is simple but inefficient, as older videos that are rarely accessed still sit in the most expensive storage class. With lifecycle tiering enabled, the same 18 TB is distributed across several classes based on how often each dataset is used. Around 4.5 TB of recent videos remain in S3 Standard for fast access, another 4.5 TB shifts to S3 Standard-IA, and the rest (about 9 TB) moves to S3 Glacier Instant Retrieval for long-term retention. Based on AWS’s current pricing, this mix brings the total monthly cost down to around $195–$200, cutting storage expenses by over 50 percent while keeping all assets available when needed. Storage Segment Approx. Volume Storage Class Price (USD / GB / month) Estimated Monthly Cost New videos (0–90 days) 4.5 TB S3 Standard $0.023 ~$103.5 90–180 days 4.5 TB S3 Standard-IA $0.0125 ~$56.25 180 days+ 9 TB S3 Glacier IR $0.004 ~$36 Total 18 TB — — ~$195.75 Final Thoughts The VOD storage model built on Amazon S3 shows how broadcasters can balance scale, reliability, and cost in one system. By combining lifecycle policies, multi-tier storage, and cross-region replication, the workflow stays simple while infrastructure costs drop sharply. With Amazon S3 video storage, broadcasters can scale their VOD systems sustainably and cost-effectively — turning storage from a fixed cost into a flexible, data-driven resource. If your team is looking to modernize or optimize an existing VOD platform, Haposoft can help assess your current setup and design a tailored AWS storage strategy that grows with your needs.