For the given allocation and max below, what is the need matrix?Allocation23AB52C1D1 0O13346MaxAB२3 2Dए

Name: For the given allocation and max below, what is the need matrix?Alloc
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Description: For the given allocation and max below, what is the need matrix?Allocation23AB52C1D1 0O13346MaxAB२3 2Dए

Allocation Matrix: The allocation matrix, which is a two-dimensional matrix, shows how many of each…

Answered: For the given allocation and max below,…

Engineering

Computer Science

For the given allocation and max below, what is the need matrix? Allocation 3 A B 5 2 0 с 1 D 4 O 1 0 3 3 4 6 Max A B ( 2 3 2 5 3 २ ए.

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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With the help of quadratic probing solve the following and decide what index student ids should be inserted. Arraysize = 20. Index = Id%ArraySize. You are given the following student ids to be inserted: 170011, 210012, 330004, 140011, 630013, 420011, 110011, 500004
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A university assigns student IDs of the form 1, 2, 3, ... such that if n students are currently enrolled, then the next student to enroll will receive and ID of n + 1. Consider the following algorithm that accomplishes this. // Global variable storing number of students currently enrolled current_student_count = 0 // Function that reads the above global variable, calculates new ID, and increments the global count assign_new_id(): count = current_student_count new_id = count + 1 current_student_count = count + 1 return new_id (a) If two different threads run the above code in parallel to enroll two different students, it is possible for the two students to receive the same ID, and for the current_student_count to have a wrong value. Explain how this is possible. (b) Modify the code above so that the problem in (a) does not happen.
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The formula C=numpy.add(A, B) adds the two matrices A and B and stores the result in C.• To subtract matrix B from matrix A, enter C=numpy.subtract(A, B). The result is stored in C.• C=numpy.divide(A, B): Split matrix A into two equal parts and store the result in C.• C stands for numpy.multiply(A, B): Multiply matrices A and B, then store the result in matrices C.• C=numpy.sum(A): Calculate the sum of each element in the matrix A, then store the result in c R.• C=numpy.sum(A, axis = 0): Create a vector C by adding the columns of the matrix A.• C=numpy.sum(A, axis = 1): Summarize matrix A row-by-row and store the result in the vector C. Create Python code that demonstrates the use of these techniques in a sample matrix.
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