SRTF Implementation

Duration: 7 min

This video lesson is available to enrolled students.

Inside: a video lesson and guided study material.

Module outline

About Course: About the Course
General Aptitude & Reasoning: Number System, Percentage, Ratio and Proportion (Ratios), Average, Profit and Loss, Simple Interest and Compound Interest, Time and Work, Speed Time and Distance, Mixture and Alligation, Data Interpretation, Algebra, Probability, Permutation and Combination, Venn Diagram, HCF LCM, Divisibility Rules, Age Problems, Pipe and Cistern, Partnership, Linear Algebra, Sequence and Series (Series), Statistics, Powers and Exponents (Surds and Indices), Arithmetic, Mensuration and Geometry, Calculus, Trigonometry, Vectors, Speed Mathematics and Basic Mathematics, Non Verbal Reasoning (Spatial Aptitude) (Spatial Reasoning) (Visual Reasoning), Analogy, Series (Number and Letter Series) (Numerical Relations and Reasoning), Puzzle Solving (Puzzles), Syllogisms, Deductive and Inductive Reasoning (Logical Deduction and Induction) (Prepositional Reasoning), Calendar, Seating Arrangements, Coding Decoding, Blood Relations, Directions (Direction Test), Clock, Analytical Reasoning (Counting Figures Reasoning), Ranking, Order and Sequence, Logical Sequence Of Words, Inequality, Data Sufficiency, Cubes & Dices
Computer Networks & Cybersecurity: Introduction to CN, Data Communication, DLL: Access Control, DLL: Flow Control, DLL: Error Control, DLL: Framing, Data Link Layer - Ethernet, Net Layer: IPv4 & Proto, Net Layer: IP Addressing, Net Layer:Routing Protocol, Transport Layer Services, TL: Congestion & UDP, Application Layer, Hardware Basics, Network Security
Programming, Data Structures & Algorithms: C Fundamentals, Control Flow, Functions, Arrays & Pointers, Storage Classes, Structures & Enums, DMA, Macros, Scoping & File Handling, OOPS with C++, Introduction to DS, Array, Stack, Queue, Linked List, Tree, Graphs, Hashing, Algorithm Analysis, Time Complexity Analysis, Sorting Algorithms, Greedy Algorithms, Dynamic Programming, Minimum Spanning Trees, Shortest Path Algos, Advanced Algorithms
AI, Machine Learning, Big Data & Cloud: Emerging Tech Trends, Artificial Intelligence, Big Data Systems, Approaches to AI, Search Algorithms, Knowledge Representation, Cloud Computing
Digital Logic & Computer Organization: Digital Systems & Boolean Basics, Logic Gates & Hardware, Boolean Expression, Boolean Minimization, Combinational Circuit, Sequential Circuits, Number System, Number Representation, Basics of COA, Floating Point Rep, Cache Memory Organization, Input Output Organisation, Pipelining, Instr Formats & Modes, Register Transfer and Microoperations, Control Unit Design, Programming the Basic Computer
TOC & Compiler: Introduction to TOC, Deterministic FA (DFA), Non-Deterministic FA, Regular Expressions, Grammar, Regular Language Properties, Moore & Mealy Machines, Pushdown Automata & CFG, Turing Machines, Complexity Theory, 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
Discrete Mathematics: Set Theory, Relations, Functions, Graph Theory, Group Theory, Propositional and Predicate Logic
Operating System: Introduction to OS, Process Management, CPU Scheduling, Process Synchronization, Threads & Process Creation, Deadlock, Memory Management, Virtual Memory, Disc Scheduling, File Management, Unix OS, Linux OS, Distributed Systems
Software Engineering: Fundamentals of Software Engineering, Software Requirements and Quality Assurance, Software Design, Estimation and Metrics, Software Testing, Software Maintenance and Configuration Management, Object Oriented Technology, HTML Basics, JavaScript-Basics, Java, XML
Database Management System: Basics of DBMS, ER Diagram, Relational Model & Functional Dependencies, Keys & Integrity Constraints, Normalization (1NF - BCNF), Decomposition Properties & 4NF, File Organization & Indexing, Relational Algebra, SQL, Relational Calculus, Transaction Management, Concurrency Control, Database Recovery, Data Warehousing & Mining, Enhanced Data Models, NoSQL
Other Recurring CS Topics: Basics of Computer Graphics, 2-D Geometrical Transforms and Viewing, Multimedia
Live Classes: ISRO Live classes
Full Mock Test:

AI summary & chapters

AI Summary

An AI-generated summary of this video lecture.

This educational video lecture focuses on the Shortest Job First (SJF) CPU scheduling algorithm and addresses its primary limitation: the inability to know the exact CPU burst time of a process beforehand. To overcome this, the instructor introduces the concept of predicting the next CPU burst using the exponential averaging technique. The lecture details the mathematical formula for this prediction, defining the variables involved, including the smoothing factor alpha. The instructor provides a step-by-step calculation example on a whiteboard and demonstrates the application of the formula using a process table. Finally, the video concludes by discussing the theoretical nature of this scheme, noting its limitations when burst times vary significantly or remain constant.

Chapters

0:00 – 2:00 00:00-02:00
The lecture begins with the instructor standing in front of a slide that states, 'As SJF is not implementable, we can use the one technique where we try to predict the CPU burst of the next coming process.' He explains that since the Shortest Job First algorithm requires knowledge of the burst time which is not available, a prediction method is necessary. The slide text further clarifies, 'The method is used as exponential averaging technique, where we consider the previous value and previous prediction.' The instructor emphasizes that this technique relies on historical data to estimate future behavior, setting the stage for the mathematical derivation to follow.
2:00 – 5:00 02:00-05:00
The instructor transitions to the core formula displayed on the screen: 'Tau(n+1) = alpa tn + (1- alpa) tau n'. He explains that Tau(n+1) represents the predicted burst time for the next process, tn is the actual burst time of the current process, and tau(n) is the previous predicted burst time. He writes on the whiteboard to illustrate the concept, listing 'M1' with values '250' and '300'. He calculates a resulting value of '275' to show how the average works. He then discusses the parameter 'alpha' (alpha), writing 'alpha = 0', 'alpha = 1', and 'alpha = 0.5' to demonstrate how different weights affect the prediction, explaining that alpha determines the influence of the most recent history versus older history.
5:00 – 6:40 05:00-06:40
A table appears on the screen with columns labeled 'Process', 't', and 'tau'. The rows list processes P1, P2, P3, and P4. For P1, the actual time 't' is 10 and the initial 'tau' is 20. The instructor proceeds to calculate the 'tau' values for subsequent processes based on the formula. He writes '15' in the tau column for P2 and '13.5' for P3, showing the iterative nature of the prediction. The video concludes with a slide stating, 'This idea is also more of theoretical importance as most of the time the burst requirement of the coming process may vary by a large extent and if the burst time requirement of all the process is approximately same then there is no advantage of using this scheme.' This highlights the practical limitations of the exponential averaging technique in real-world scenarios.

The video provides a comprehensive overview of how to handle the unpredictability of CPU burst times in the Shortest Job First scheduling algorithm. It moves from identifying the problem to introducing the exponential averaging formula, explaining the role of the alpha parameter, and demonstrating calculations through both whiteboard examples and a process table. The lesson concludes by contextualizing the technique as theoretically important but practically limited when process burst times are inconsistent or uniform.