Auto Scaling groups with ECS is the correct choice because it directly addresses the core requirements: high availability (via multi-AZ deployment), automatic scaling based on metrics like CPU utilization, and cost optimization by launching/terminating instances automatically. While SAE and ECI are good for specific serverless or containerized workloads, Auto Scaling with ECS provides the most flexible and foundational compute scaling solution for a general web application expecting variable traffic.

Immutable Object Storage (Bucket Lock) is specifically designed for Write-Once-Read-Many (WORM) compliance scenarios. It allows you to set legal holds or retention periods (e.g., 7 years) during which objects cannot be deleted or modified, even by the bucket owner. Versioning alone keeps old versions but doesn’t prevent their deletion. A bucket policy can be circumvented by the root account. Server-side encryption protects data at rest but does not enforce retention.

Using Cloud Enterprise Network (CEN) to interconnect VPCs across regions allows traffic to flow over Alibaba Cloud’s private backbone network. Inter-region data transfer via CEN is typically significantly cheaper than transferring the same data over the public internet. This solution keeps resources optimally placed while reducing costs for cross-region communication. Migrating all instances may not be optimal if other services depend on Shanghai, and bandwidth sharing doesn’t address cross-region pricing.

In Alibaba Cloud, if an ECS instance with an associated EIP is stopped and then restarted (in a “Stopped” state, not a reboot), the EIP is automatically unbound. This is a common operational behavior to prevent IP conflicts and charges for unused resources. The administrator must manually re-bind the EIP to the instance after it enters the “Running” state again. The other options are less likely as the instance is accessible via its private IP, ruling out security group or vSwitch issues at this layer.

ApsaraDB for RDS (MySQL) is the optimal choice for a relational, transactional workload like a product catalog. It provides a fully managed relational database service that supports complex SQL queries, joins, and ACID transactions. Table Store is a NoSQL wide-column store, better for massive scale with simple queries. Redis is an in-memory key-value store for caching and session storage. AnalyticDB is optimized for analytical (OLAP) workloads, not transactional (OLTP) operations.

Resource Access Management (RAM) is specifically designed for this purpose. It allows you to create and manage sub-users (like developers), user groups, and roles. You can assign fine-grained permissions (via policies) to control exactly which actions they can perform on which resources (e.g., “ECS:StartInstance” on a specific instance). This follows the principle of least privilege and provides centralized access control. KMS manages encryption keys, ActionTrail logs API calls, and Security Center identifies threats.

The combination of Object Storage Service (OSS) as the origin storage and Content Delivery Network (CDN) is the standard solution for low-latency global content delivery. CDN caches static content at edge nodes worldwide, so user requests are served from the nearest node, drastically reducing latency and improving download speeds. SLB and Auto Scaling deal with compute load balancing, not static content acceleration. GA accelerates dynamic content and non-web protocols over Alibaba Cloud’s backbone.

Deploying resources across multiple Availability Zones (AZs) within a region is the core Alibaba Cloud best practice for high availability. AZs are physically separated, independent data centers with their own power and networking. Distributing ECS instances across AZs ensures that a failure in one AZ does not take down the entire application tier. The other options improve network configuration, performance, or backup but do not by themselves provide infrastructure-level redundancy against data center failure.

Scheduled Scaling is ideal for predictable, periodic traffic patterns like hourly events. You can configure the Auto Scaling group to add a specific number of instances 5 minutes before the event starts and remove them 10 minutes after it ends. This ensures capacity is available precisely when needed and terminated immediately after, minimizing costs. Dynamic scaling based on metrics has a lag and might not react quickly enough for a 2-3 minute burst. Manual scaling is operationally inefficient.

Auto Scaling groups with ECS is the correct choice because it directly addresses the core requirements: high availability (via multi-AZ deployment), automatic scaling based on metrics like CPU utilization, and cost optimization by launching/terminating instances automatically. While SAE and ECI are good for specific serverless or containerized workloads, Auto Scaling with ECS provides the most flexible and foundational compute scaling solution for a general web application expecting variable traffic.0