Introduction

In multi-threaded programming, resources management is crucial for maintaining the efficiency and performance of applications. Threads are sequences of programmed instructions that can be managed independently by a scheduler. In the lifecycle of a thread, it can be in one of several states - among these states are `sleeping` and `waiting`. Understanding how much resources these states consume is key to optimizing application performance and ensuring resources are efficiently allocated.

Thread States Overview

Threads in a typical program can be in one of these states:

1. New: The thread is created but not yet started.
2. Runnable: The thread is executing in the Java virtual machine but it may be waiting for other resources such as processor time.
3. Blocked: The thread is waiting for a monitor lock to enter a synchronized block/method.
4. Waiting: The thread is waiting indefinitely for another thread to perform a particular action.
5. Timed Waiting: The thread is waiting for another thread to perform a particular action for up to a specified waiting time.
6. Terminated: The thread has completed its execution.

Among these, `sleeping` and `waiting` are states where threads are not actively executing but still hold some level of resource allocation.

Sleeping Threads

Definition

A thread goes into a "sleeping" state when a method such as `Thread.sleep(milliseconds)` is invoked. This means that the thread pauses execution for a specific amount of time.

Resource Consumption

• CPU Usage: Sleeping threads do not use CPU cycles as their execution is paused. However, they still remain part of the system's ready queue until they wake up.
• Memory Footprint: They hold onto the memory resources used during their execution, such as stack and heap memory, since they are paused, not terminated.
• Synchronization: Do not hold locks unless explicitly programmed to do so at the point of sleep.

Example

• CPU Usage: Similar to sleeping threads, waiting threads do not consume CPU cycles.
• Memory Footprint: They continue to consume memory by retaining stack, heap, and thread-local storage.
• Synchronization: These threads are often part of a synchronized operation and typically have a more extended impact on scheduling and threading overhead.