Compaction in Memory Management: Reducing External Fragmentation and Optimizing Memory Usage

Understand compaction, a memory management technique used to reduce external fragmentation. This guide explains how compaction works, its advantages (creating larger contiguous free blocks), disadvantages (overhead, potential for long delays), and its role in improving memory utilization.

Compaction in Memory Management

Understanding Memory Compaction

Compaction is a memory management technique used to reduce external fragmentation. External fragmentation occurs when there's enough free memory in total, but it's scattered into many small, non-contiguous blocks. This makes it impossible to allocate a contiguous block of memory needed for a new process, even though there's sufficient memory available. Compaction addresses this by moving all allocated memory blocks together, creating a single large block of contiguous free space.

How Compaction Works

Compaction involves shifting all allocated memory blocks to one end of the memory space. This creates a single contiguous free block at the other end, large enough to accommodate larger processes. The free space is consolidated into one larger block that can then be used to fulfill requests for contiguous memory.

Diagram illustrating memory compaction

(The image would illustrate memory before and after compaction, showing how fragmented free space is combined into a larger block.)

Advantages of Compaction

  • Reduces external fragmentation.
  • Allows larger processes to be loaded even if enough total memory isn't contiguously available.

Disadvantages of Compaction

Compaction has drawbacks:

  • Performance Overhead: Moving memory blocks consumes considerable CPU time and resources, making the system less responsive.
  • Inefficiency: The system becomes temporarily less efficient while the compaction process is running.

Consider a system that takes 6 nanoseconds (ns) to copy 1 byte of data:

Copying 256MB (256 * 220 bytes) takes approximately 256 * 220 * 6 ns = 1.57 seconds. This illustrates the significant time involved in moving large amounts of data.