What Is Best-Fit Allocation?

May 13, 2024

Best-fit allocation is a memory management strategy that aims to minimize wasted space when allocating memory blocks. When a process requests memory, this algorithm searches for the smallest available memory block that is large enough to accommodate the requested size. By choosing the closest fitting block, best-fit allocation strives to reduce fragmentation and maximize the efficient use of memory.

What Is Best-Fit Allocation?

Best-fit allocation is used to efficiently allocate memory blocks to processes in computer systems. When a process requests memory, the system searches through available memory blocks to find the smallest block that can accommodate the requested size. This approach aims to reduce wasted space by minimizing the difference between the requested and allocated memory sizes, ensuring that the chosen block fits as closely as possible to the required size.

The strategy minimizes memory fragmentation and optimizes memory utilization by leaving as little unused space as possible after allocation. However, it also tends to leave many small, leftover blocks that may not be useful for future memory requests, leading to fragmentation issues over time. Furthermore, the process of finding the best-fit block requires scanning through available blocks, which can be time-consuming, especially when memory is fragmented. Despite these challenges, best-fit allocation remains a useful technique in memory management, helping developers maximize the effective use of available memory resources.

Best-Fit Allocation Algorithm Overview

The best-fit allocation algorithm is a dynamic memory management strategy. Here's an overview of how it works:

  1. Request for memory. When a program needs memory, it specifies the required size to the memory manager.
  2. Search for suitable block. The memory manager scans through the available free memory blocks and searches for the smallest free block that can accommodate the requested size.
  3. Allocation decision. If a suitable block is found, the memory manager allocates part or all of the block to the requesting process. If the block size matches the requested size perfectly, the entire block is allocated. However, if the block is larger than requested, the leftover space remains available for future allocations.
  4. Update free list. After allocation, the memory manager updates its record of free blocks. The allocated block is either removed entirely or, if only a portion was used, its size and address is updated.
  5. Memory release. When a program finishes using its allocated memory, the block is released back into the free list. The process may include merging adjacent free blocks to minimize fragmentation.

Best-Fit Allocation Pros and Cons

In evaluating best-fit allocation as a memory management technique, it's important to recognize the trade-offs involved. While the approach seeks to minimize wasted memory by precisely fitting allocation requests, it introduces unique challenges that affect performance and memory utilization over time. Understanding the advantages and disadvantages of best-fit allocation helps developers and system architects make informed decisions about when and where to use it effectively.


Despite its complexities, best-fit allocation offers distinct advantages that make it a worthwhile consideration in certain memory management scenarios. Here are some of its key benefits:

  • Minimizes internal fragmentation. By allocating the smallest block that is just large enough to accommodate the requested memory, best-fit allocation minimizes the wasted space within allocated blocks. This helps reduce the amount of unused memory that might otherwise remain in each allocated block if a less precise strategy were used.
  • Optimizes memory utilization. Best-fit ensures that larger memory blocks remain available for subsequent allocations. By fitting smaller requests into the nearest appropriately sized blocks, the strategy leaves larger blocks untouched, providing better options for accommodating future memory needs.
  • Prioritizes efficient matching. The algorithm prioritizes precise matching of request sizes to available blocks, which is beneficial in systems where memory is limited or where precise memory allocations are critical. This approach makes it well-suited for applications that have highly variable memory usage patterns or strict memory constraints.
  • Reduces over-allocation. Best-fit allocation’s primary goal is to closely match the requested memory size with an available block, which results in programs receiving almost exactly the amount of memory they need.
  • Adapts well to diverse workloads. The best-fit approach is flexible in handling varied and unpredictable workloads, making it suitable for environments where the memory requirements of programs change frequently or vary widely.
  • Maintains efficient allocation over time. By systematically finding the smallest fitting block, best-fit allocation helps keep the system in an efficient state. While some fragmentation still occurs, this strategy works well for reducing the overall footprint of allocations, especially during prolonged periods of program execution.
  • Balances memory distribution.  The algorithm is good at balancing allocating small and large blocks across available memory. This balanced distribution prevents small blocks from being left over and unusable due to larger memory requests.


Despite its goal of reducing wasted memory space, best-fit allocation has several notable drawbacks. Understanding these limitations is crucial for choosing your application or system's most appropriate memory management strategy. Here are the primary cons of best-fit allocation:

  • Fragmentation. Best-fit allocation frequently leads to external fragmentation. While it seeks to use the smallest available block that can fit the requested size, it often leaves behind many small, unusable memory fragments. Over time, these fragments accumulate, reducing the amount of contiguous free memory and limiting the system’s ability to handle larger memory requests efficiently.
  • Increased search time. Finding the smallest available block that meets the request size can be computationally expensive. The memory manager needs to scan the entire free list, especially when memory is highly fragmented. This overhead increases the allocation time and adversely affects system performance.
  • Unpredictable allocation performance. The best-fit algorithm may suffer from inconsistent and unpredictable performance as the memory pool becomes increasingly fragmented. Memory allocation times vary depending on the free block sizes and the current memory state, making it harder to ensure predictable performance for critical applications.
  • Difficulty in memory block coalescing. Reclaiming and merging smaller memory fragments into larger contiguous blocks (a process known as coalescing) can be challenging when blocks are scattered across the memory pool. This lack of coalescing hinders the system’s ability to create usable blocks for subsequent allocations.
  • Overhead in memory management. The complexity of managing a fragmented free list can create overhead in memory management. As the number of free blocks grows due to fragmentation, maintaining an accurate free list and processing allocation requests becomes more burdensome.
  • Non-deterministic behavior. Due to fragmentation and the varying sizes of memory requests, the best-fit algorithm may exhibit unpredictable behavior. The efficiency of allocations can change dramatically based on current memory conditions, making it difficult to predict system performance, which is problematic for real-time or time-critical applications.
  • Time-consuming. When searching for the smallest possible block that will satisfy the allocation request, best-fit often examines multiple insufficiently large blocks. This wasted effort increases search times, especially when free memory is fragmented into many small, unusable blocks.
  • Potential for increased page faults. Best-fit allocation can inadvertently increase the frequency of page faults in virtual memory systems. As small blocks get scattered throughout memory, programs might access non-contiguous memory locations frequently, leading to increased paging and reduced performance.

Best-Fit Allocation vs. Worst-Fit Allocation

Best-fit and worst-fit allocation represent contrasting memory management strategies.

Best-fit aims to reduce wasted space by finding the smallest free block that can accommodate a requested size, thereby minimizing the leftover space within an allocated block. This approach seeks to maximize memory utilization and reduce internal fragmentation, but it often leads to a high degree of external fragmentation as many small, unusable memory fragments accumulate. Finding the smallest suitable block also requires scanning the entire free list, increasing the search time, particularly in fragmented memory.

In contrast, worst-fit deliberately allocates the largest available free block, aiming to leave the remaining fragment large enough for future allocation requests. This strategy attempts to minimize external fragmentation by ensuring that leftover free blocks are substantial enough to be useful. However, it tends to increase internal fragmentation because the allocated block is often much larger than needed, leading to wasted space. Additionally, this method requires similar scanning through the free list to find the largest block, which also increases search times.

Anastazija is an experienced content writer with knowledge and passion for cloud computing, information technology, and online security. At phoenixNAP, she focuses on answering burning questions about ensuring data robustness and security for all participants in the digital landscape.