Worst Fit Policy

Duration: 4 min

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Module outline

  1. HPSC Interview Preparation: Interview Preparation
  2. Python Language: Introduction to Python, Tokens, Variables, Data Types, I/O, Operators & Expressions, Control Flow & Looping, Collections & Strings, Functions & Modules, File Handling & Data Analysis, DSA using Python
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  8. Digital Logic & Circuits: Digital Systems & Boolean Basics, Logic Gates & Hardware, Boolean Expression, Boolean Minimization, Combinational Circuit, Sequential Circuits, Number System, Number Representation
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  10. Compiler Design: Intro to Compilers, Lexical Analysis, Grammar & CFG, Syntax Analysis: Top-Down, Syntax Analysis: Bottom-Up, Semantic Analysis & SDT, Intermediate Code Gen, Code Optimization, Run Time Environment
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  17. Full Mock Test:
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AI summary & chapters

AI Summary

An AI-generated summary of this video lecture.

This educational video provides a detailed lecture on memory allocation policies in operating systems, specifically analyzing the Worst Fit and Best Fit strategies. The instructor uses a digital whiteboard to display definitions, advantages, and disadvantages while simultaneously drawing memory diagrams to demonstrate how each policy handles process requests and memory fragmentation. The lesson is structured to compare how different algorithms impact the efficiency of memory usage.

Chapters

  1. 0:00 – 2:00 00:00-02:00

    The session begins with the Worst fit policy, defined on the slide as a method that searches the entire memory to allocate the largest partition possible. The instructor draws a horizontal memory block divided into partitions labeled with sizes 8, 5, 30, 20, and 40. He explains that for a process request of size 5, the policy selects the largest available partition, which is 40. He marks this partition with hatching to indicate allocation and calculates the remaining free space as 35 (40 minus 5). The slide text highlights that this policy performs best in variable size partitioning but has a significant disadvantage: it performs worst in fixed size partitioning, resulting in large internal fragmentation because large chunks of memory are used for small processes. He emphasizes that while this leaves a large hole, it might not be useful if the next request is small, but generally, it keeps larger contiguous blocks available.

  2. 2:00 – 4:14 02:00-04:14

    The lecture shifts to the Best fit policy, described on the slide as searching the entire memory to allocate the smallest partition capable of holding the process. The instructor draws a new memory diagram with partitions of sizes 20, 5, 30, and 28. For a request of size 2, he identifies the partition of size 5 as the best fit because it is the smallest partition that can accommodate the request. He allocates the process there, leaving a small remainder of 3. The slide notes that while this performs best in fixed size partitioning, it is difficult to implement and performs worst in variable size partitioning because the remaining spaces are often very small and unusable, leading to external fragmentation. The video concludes with a brief mention of Next fit policy as a modification of best fit where searching starts from the current position rather than the beginning, aiming to reduce search time.

The video effectively contrasts Worst Fit and Best Fit policies by defining their search strategies and illustrating their impact on memory fragmentation through concrete numerical examples. It highlights the trade-off between leaving large holes (Worst Fit) and minimizing wasted space at the cost of creating unusable fragments (Best Fit).

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