Welcome back! In our previous lessons, we explored how to manage multiple operations simultaneously using advanced concurrency techniques. Building on this foundational knowledge, today’s lesson focuses on Designing a Concurrent Garbage Collector Simulation. We’ll simulate a garbage collector in a concurrent environment, linking our previous exploration of concurrency with practical memory management applications, which are crucial for optimizing large systems.

This lesson will help you deepen your understanding of:

• Thread coordination and synchronization within concurrent systems.
• Leveraging concurrency utilities to simulate complex systems.
• Creating robust applications by mimicking real-world memory management tasks.

We’ll build a simulation that reflects how garbage collectors work in modern programming environments, focusing on efficiency and system stability.

In this lesson, we’ll create a simulation of a garbage collector, where application threads allocate objects and a garbage collector thread removes unused ones. This approach mirrors how memory management happens in real-world systems with multiple processes running simultaneously.

Let’s briefly recap some key concepts we’ll be using: Thread coordination is essential when multiple threads share resources, and concurrency utilities like ConcurrentHashMap ensure safe access to shared data structures. We’ll use these tools to efficiently simulate both the allocation of objects by application threads and the cleaning of memory by the garbage collector.

The application threads simulate processes that allocate and occasionally remove references to objects. Let’s start by setting up the application thread logic.

Here, the ApplicationThread class is initialized with a thread ID, a shared heap (a map representing memory), and a set of roots that reference active objects. Each thread will perform operations on the heap independently. The Map<Integer, Object> represents the heap, which stores objects in memory, and the Set<Integer> represents the root set, which keeps track of objects still in use by the application.

Now, let’s implement the logic for allocating and removing objects from the heap.

In this method:

• random.nextInt(1000) generates a random object ID, simulating a process that dynamically allocates memory by creating new objects with random identifiers.
• heap.put(objectId, new Object()) inserts a new object into the heap, representing memory allocation. This simulates real applications that allocate memory when new objects are created.
• roots.add(objectId) adds the object ID to the root set, indicating that this object is still in use by the application. The root set represents active references in the program, such as variables and data structures.
• The if (random.nextBoolean()) block randomly removes references to objects, simulating the dereferencing or deletion of objects in a real program, making them eligible for garbage collection.

The run method continues executing as long as the simulation is active, simulating real-world processes that constantly allocate and free memory.

The sleepRandom() method introduces a delay to simulate real-world time passing between operations.

The Thread.sleep() method pauses the thread for a random period, simulating the varying time it takes for application threads to perform tasks in a real environment. The randomization adds unpredictability, making the simulation more dynamic and closer to real-world scenarios where processing times vary.

Next, let’s set up the garbage collector. This thread will scan the heap and remove objects that are no longer referenced by any root.

The GarbageCollector class is responsible for identifying and removing unused objects. The Map<Integer, Object> heap holds all allocated objects, while Set<Integer> roots tracks which objects are still referenced and in use. The garbage collector operates independently from the application threads.

Here’s the logic for the garbage collector thread, which periodically scans for unreachable objects and removes them from the heap.

In the snippet above:

• new HashSet<>(roots) creates a copy of the root set. The root set contains references to all objects currently in use by the application. This is crucial for identifying which objects are still "reachable" and should not be collected.
• heap.keySet().retainAll(reachable) performs the actual garbage collection by removing all heap objects that are not present in the reachable set. This simulates how a garbage collector in a real system works by freeing memory consumed by unreferenced objects. The retainAll() method modifies the heap by keeping only the keys (object IDs) that are in the root set.

This method ensures that only objects still referenced by the program remain in memory, simulating efficient memory management in a concurrent environment.

Now, let’s create the ConcurrentGarbageCollector class to manage the lifecycle of the application threads and the garbage collector.

In this class, the ConcurrentHashMap is used for both the heap and the root set to allow thread-safe operations. The volatile boolean running variable ensures that the simulation can be stopped safely from any thread. ExecutorService is used to manage the application threads and garbage collector concurrently, providing an easy way to handle multiple threads without directly managing each one.

Let’s define how the simulation starts, runs for a specified duration, and then stops gracefully.

In the snippet above:

• executor.submit() submits the application threads and the garbage collector to the executor for concurrent execution. Each thread runs independently, simulating real-world multitasking.
• Thread.sleep(10000) pauses the main thread for 10 seconds, allowing the simulation to run. This simulates a time window for the application to operate and allocate objects.
• running = false: This sets the running flag to false, signaling all threads to stop execution. Since the run methods of both the ApplicationThread and GarbageCollector check this flag, they will cease operation when it is set to false, ensuring a controlled shutdown.
• executor.shutdown(): This method initiates an orderly shutdown of the executor service, meaning that no new tasks will be accepted, but existing tasks will continue until completion.
• executor.awaitTermination(5, TimeUnit.SECONDS): This waits up to 5 seconds for the executor to finish executing all tasks. If the tasks are not completed within that time, the shutdown process proceeds forcefully, ensuring that the application terminates within a reasonable time frame. This method helps guarantee that the program doesn't hang indefinitely.

Finally, the Main class ties everything together, starting the application threads and the garbage collector.

The Main class is the entry point for our simulation. It initializes the ConcurrentGarbageCollector, which manages the lifecycle of both the application threads and the garbage collector.

Mastering concurrent programming, including managing memory and coordinating threads, is essential for developing efficient, scalable applications. Here’s why this simulation is important:

• Thread Coordination: Understanding how threads interact and share resources is crucial for building concurrent applications that are efficient and avoid race conditions or deadlocks.

• Memory Management: By simulating garbage collection, you gain insight into how modern environments manage memory. This knowledge is applicable in optimizing performance, reducing memory leaks, and enhancing system stability in real-world applications.

• Concurrency Tools: Using utilities like ConcurrentHashMap and ExecutorService simplifies thread management and guarantees safe access to shared resources. These tools are indispensable when developing multi-threaded systems.

This simulation demonstrates how concurrent garbage collection can optimize memory management, ensuring that resources like memory are not wasted on unused objects. By using these concurrency tools, we maintain control over the number of running tasks, ensuring a balanced system load, which is critical in real-world applications. The safe use of ConcurrentHashMap ensures that multiple threads can access and modify shared data structures without causing data inconsistencies or race conditions.

Now that you've seen how a concurrent garbage collector simulation works, it's time to move to the practice section and apply these concepts to similar scenarios. In the practice, you'll explore different memory management tasks and expand your understanding of concurrent systems.