跨多台IIS服务器并行执行大量请求的方案咨询

Absolutely, you can split your workload across all 20 IIS servers (both local and remote) to accelerate processing of your 100+ request objects. Given your mix of I/O-bound (backend service calls) and CPU-bound (response calculations) work, here are practical, production-ready approaches tailored to your scenario:

1. 分布式任务队列架构（推荐复杂场景） #

This approach decouples request ingestion from processing, making it easy to scale across servers and handle failures gracefully:

• Step 1: Ingest & Split Tasks
  Your entry API controller receives the full request, splits it into individual or grouped sub-tasks (e.g., 5 requests per task for 20 servers), and publishes each task to a distributed message queue like RabbitMQ or MSMQ. Include a unique correlation ID for each parent request to track all child tasks.
• Step 2: Deploy Task Consumers
  On every one of your 20 IIS servers, deploy a lightweight background consumer (either as an ASP.NET Core hosted service or a separate Windows Service) that listens to the queue. Each consumer pulls tasks, executes the I/O call to your backend service, runs the CPU-bound calculation, and stores the result in a shared data store (e.g., Redis for low-latency temporary storage, or SQL Server for persistence).
• Step 3: Aggregate & Respond
  The entry API waits for all sub-task results to be available. You can implement this with:
  • A Redis atomic counter: Increment a counter each time a result is stored; the API polls until the counter matches the total number of sub-tasks.
  • Redis Pub/Sub: Have consumers publish a "task completed" event, and the entry API subscribes to these events to track progress.
    Once all results are collected, aggregate them and return the final response to the user.

2. 主动HTTP分发+负载均衡（简单直接） #

If you prefer a more tightly coupled approach without a message queue, you can have the entry API directly distribute sub-tasks to other IIS nodes via HTTP:

• Step 1: Set Up Load Balancing
  Use a load balancer (like IIS Application Request Routing (ARR) or Nginx) to route traffic across your 20 IIS servers. Ensure each server exposes a dedicated endpoint (e.g., api/process-subtask) that handles a single or group of request objects.
• Step 2: Parallel Sub-Requests
  In your entry controller, split the original request into batches (e.g., 5 requests per batch for 20 servers). Use IHttpClientFactory (to reuse connections and avoid port exhaustion) with Task.WhenAll to send parallel HTTP requests to the load-balanced endpoint or directly to individual server endpoints.
• Step 3: Handle Fault Tolerance
  Add retry and circuit-breaker logic using libraries like Polly to handle failed requests (e.g., if a server goes down mid-processing). Once all successful responses are received, aggregate them and return the result.

3. 分布式Actor模型（适合.NET栈） #

If your Web API is built on .NET, consider using Orleans (a distributed actor framework) to simplify distributed processing:

• Step 1: Configure Orleans Silos
  Set up each of your 20 IIS servers as an Orleans Silo (a cluster node). Actors (called Grains) run on these silos and handle individual sub-tasks.
• Step 2: Dispatch Tasks via Grains
  Your entry API acts as an Orleans client, creating a Grain for each sub-task (or group of tasks). The Grain executes the I/O and CPU-bound work asynchronously.
• Step 3: Aggregate Results
  Use Task.WhenAll to wait for all Grain calls to complete, then aggregate the results. Orleans automatically handles load balancing across silos, fault tolerance, and actor lifecycle management—so you don’t have to build that logic from scratch.

Key Considerations for All Approaches #

• Task Granularity: Balance task size to avoid overhead (too many tiny tasks) or uneven load (too few large tasks). Aim to split work so each server gets roughly the same amount of processing.
• Fault Tolerance: Always implement retries for I/O calls, and track failed tasks so you can either retry them or notify the user of partial failures.
• Async All the Way: Use async/await throughout your pipeline to avoid blocking threads in IIS, which keeps your server responsive to other requests.
• Monitoring: Add logging and metrics (e.g., track task completion time per server, queue length) to identify bottlenecks and scale as needed.

内容的提问来源于stack exchange，提问作者Developer