TPS00-J. Use thread pools to enable graceful degradation of service during traffic bursts

Many programs must address the problem of handling a series of incoming requests. The Thread-Per-Message design pattern is the simplest concurrency strategy wherein a new thread is created for each request [[Lea 00]]. This design pattern is generally preferred to sequential executions of time consuming, I/O bound, session based, or isolated tasks.

However, this design pattern also has several pitfalls, including overheads of thread-creation and scheduling, task processing, resource allocation and deallocation, and frequent context switching [[Lea 00]]. Furthermore, an attacker can cause a denial of service by overwhelming the system with too many requests all at once. Instead of degrading gracefully, the system becomes unresponsive, causing a denial of service. From a safety perspective, one component can exhaust all resources because of some intermittent error, starving all other components.

Thread pools allow a system to service as many requests as it can comfortably sustain, rather than terminating all services when presented with a deluge of requests. Thread pools overcome these issues by controlling the maximum number of worker threads that can be initialized and executed concurrently. Every object that supports thread pools accepts a Runnable or Callable<T> task and stores it in a temporary queue until resources become available. Because the threads in a thread pool can be reused and efficiently added or removed from the pool, thread life-cycle management overhead is minimized.

Noncompliant Code Example

This noncompliant code example demonstrates the Thread-Per-Message design pattern. The class RequestHandler provides a public static factory method so that callers can obtain its instance. The handleRequest() method is subsequently invoked to handle each request in its own thread.

class Helper {
  public void handle(Socket socket) {
    //...
  }
}

final class RequestHandler {
  private final Helper helper = new Helper();
  private final ServerSocket server;

  private RequestHandler(int port) throws IOException {
    server = new ServerSocket(port);
  }

  public static RequestHandler newInstance() throws IOException {
    return new RequestHandler(0); // Selects next available port
  }

  public void handleRequest() {
    new Thread(new Runnable() {
      public void run() {
        try {
          helper.handle(server.accept());
        } catch (IOException e) {
          // Forward to handler
        }
      }
    }).start();
  }

  // ... other methods such as shutting down the thread pool and task cancellation ...
}

The Thread-Per-Message strategy fails to provide graceful degradation of service. As more threads are created, processing continues normally until some scarce resource is exhausted. For example, a system may only allow a limited number of open file descriptors even though several more threads can be created to service requests. When the scarce resource is memory, the system may fail abruptly, resulting in a denial of service.

Compliant Solution

This compliant solution uses a fixed thread pool that places an upper bound on the number of concurrently executing threads. Tasks submitted to the pool are stored in an internal queue. This prevents the system from being overwhelmed when trying to respond to all incoming requests and allows it to degrade gracefully by serving a fixed number of clients at a particular time [[Tutorials 08]].

// class Helper remains unchanged

final class RequestHandler {
  private final Helper helper = new Helper();
  private final ServerSocket server;
  private final ExecutorService exec;

  private RequestHandler(int port, int poolSize) throws IOException {
    server = new ServerSocket(port);
    exec = Executors.newFixedThreadPool(poolSize);
  }

  public static RequestHandler newInstance(int poolSize) throws IOException {
    return new RequestHandler(0, poolSize);
  }

  public void handleRequest() {
    Future<?> future = exec.submit(new Runnable() {
      @Override public void run() {
	try {
  	  helper.handle(server.accept());
	} catch (IOException e) {
          // Forward to handler
        }
      }
    });
  }
}

According to the Java API documentation for the Executor interface [[API 06]]:

[The Interface Executor is] An object that executes submitted Runnable tasks. This interface provides a way of decoupling task submission from the mechanics of how each task will be run, including details of thread use, scheduling, etc. An Executor is normally used instead of explicitly creating threads.

The ExecutorService interface used in this compliant solution derives from the java.util.concurrent.Executor interface. The ExecutorService.submit() method allows callers to obtain a Future<V> object. This object encapuslates the as-yet-unknown result of an asynchronous computation, and enables callers to perform additional functions such as task cancellation.

The choice of the unbounded newFixedThreadPool is not always optimal. Refer to the API documentation for choosing between newFixedThreadPool(), newCachedThreadPool(), newSingleThreadExecutor() and newScheduledThreadPool() to meet specific design requirements [[API 06]].

Risk Assessment

Using simplistic concurrency primitives to process an unbounded number of requests may result in severe performance degradation, deadlock, or system resource exhaustion and denial-of-service.

Automated Detection

TODO

Related Vulnerabilities

Apache Geronimo 3838

References

[[API 06]] Interface Executor
[[Lea 00]] Section 4.1.3 Thread-Per-Message and 4.1.4 Worker Threads
[[Tutorials 08]] Thread Pools
[[Goetz 06]] Chapter 8, Applying Thread Pools
[[MITRE 09]] CWE ID 405 "Asymmetric Resource Consumption (Amplification)", CWE ID 410 "Insufficient Resource Pool"

      12. Locking (LCK)