Linear Data StructuresA linear data structure is a structure where elements are arranged in a sequential order.Examples: Linked List, Array, Vector, Stack, Queue.1) Linked ListA structure where nodes containing data are connected via pointers, maximizing space efficiency.Insertion/Deletion: O(1)Search/Access: O(n) OperationTime ComplexityNotesLookupO(n)Must traverse from headAssignO(n)Same reaso..

URI, URL and URNA Uniform Resource Identifier (URI)is a string of characters that uniquely identify a name or a resource on the internet.URIs have two specializations known as Uniform Resource Locator (URL), and Uniform Resource Name (URN).A URI identifies a resource by name, location, or both.A Uniform Resource Locator (URL)is a type of URI that specifies not only a resource, but **how to reach..

CPU SchedulerThe CPU scheduler assigns CPU time to threads (units of work) from processes according to a scheduling algorithm.These algorithms aim to:Maximize CPU utilizationMaximize the amount of work done in a given timeMinimize the number of processes in the ready queueMinimize response time Non-Preemptive SchedulingIn non-preemptive scheduling, a process voluntarily releases the CPU;the OS d..

Shared ResourceA shared resource refers to resources or variables within a system that multiple processes or threads can access concurrently — such as monitors, printers, memory, files, or data.When two or more processes attempt to read or write to a shared resource simultaneously,this situation is called a race condition — where the timing or order of access affects the outcome.Critical Section..

NAT (Network Address Translation) is a networking technique that modifies the IP address information in packet headers while the packets pass through a router or firewall.Its primary purpose is to map private (internal) IP addresses to public (external) IP addresses, and vice versa. Real-World ExampleSuppose two employees (e.g., Manager Hongcheol and Deputy Gayoung) each have private IP address..

Time ComplexityTime complexity describes how the number of operations grows as the input size N increases.It measures how much time an algorithm takes, independent of hardware or programming language.We focus on the number of operations, not exact timings.To express it, we use Big-O notation, which reflects the most significant term in the function T(n) — the one that dominates as n grows. Comm..