In this post, we implement the Banker’s Algorithm in C++ — a classic deadlock avoidance mechanism in operating systems proposed by Edsger Dijkstra. The algorithm is named after the analogy of a bank that never lends money in a way that could prevent it from satisfying all customers’ future needs.
What is the Banker’s Algorithm?
Before granting any resource request, the OS runs the Banker’s Algorithm to check whether doing so keeps the system in a safe state. A safe state is one where a safe sequence exists — an ordering of all processes such that each can eventually complete using currently available resources plus the resources released by earlier processes in the sequence.
If no safe sequence exists, the state is unsafe, meaning deadlock is possible. The OS denies the request in that case.
Three data structures are needed:
Available[] — Current free units of each resource type.
Allocated[][] — Resources currently held by each process.
Maximum[][] — The maximum resources each process may ever request.
Need[][] — Remaining resources a process may still request: Need[i][j] = Maximum[i][j] - Allocated[i][j]
In this post, we implement the Shortest Job First (SJF) CPU scheduling algorithm in C++. SJF always selects the process with the smallest burst time from the ready queue and executes it next. This greedy approach provably minimizes the average waiting time, making SJF theoretically optimal among all non-preemptive algorithms when all jobs are available simultaneously.
What is SJF Scheduling?
In SJF, the scheduler sorts all ready processes by their burst (execution) time in ascending order and then runs them in that sequence. The shortest job finishes earliest, releasing the CPU quickly for others and keeping the average wait low.
It is non-preemptive — once started, a process runs to completion. The preemptive variant is called Shortest Remaining Time First (SRTF).
In this post, we implement the Round Robin (RR) CPU scheduling algorithm in C++. Round Robin is the most widely used algorithm in time-sharing systems because it gives every process a fair, equal slice of CPU time. Each process gets to run for a fixed duration called the time quantum, after which it is preempted and moved to the back of the ready queue.
What is Round Robin Scheduling?
All processes are arranged in a circular queue. The CPU runs each process for at most Q (time quantum) units. If the process finishes within Q units, it leaves the queue. If it doesn’t finish, it is interrupted and placed at the back to wait for its next turn. This cycle repeats until all processes complete.
Time Quantum (Q) — The maximum CPU time a process gets per round. A smaller Q improves response time but increases context-switch overhead. A very large Q degrades to FCFS behavior.
Waiting Time (WT) — Total time the process spent waiting across all rounds, not including its own execution time.
In this post, we implement a simulation of the Producer-Consumer Problem in C++ using a circular bounded buffer. This is one of the most classic synchronization problems in operating systems, modeling the coordination between a process that generates data (producer) and a process that consumes data (consumer) through a shared buffer of fixed capacity.
What is the Producer-Consumer Problem?
The Producer-Consumer problem (also called the Bounded Buffer problem) defines these rules:
The producer generates items and places them into the buffer — but only if space is available. If the buffer is full, the producer must wait (sleep).
The consumer retrieves and processes items from the buffer — but only if items exist. If the buffer is empty, the consumer must wait (sleep).
Only one entity should access the buffer at a time (mutual exclusion).
This simulation runs 20 steps. At each step, a random number determines whether the producer or consumer acts. A circular buffer is used so the array can be reused efficiently without shifting elements.
In this post, we implement the Priority Scheduling CPU scheduling algorithm in C++. Each process is assigned a priority number, and the process with the highest priority is scheduled first. This algorithm is used in real-time and embedded systems where certain tasks are more urgent than others and must preempt routine work.
What is Priority Scheduling?
In this implementation, a higher priority number means higher urgency (e.g., priority 5 executes before priority 1). The algorithm is non-preemptive — once a process starts, it runs to completion. All processes are assumed to arrive at time 0.
The algorithm works by sorting processes in descending order of priority and then computing scheduling metrics in that order:
Waiting Time (WT): WT[i] = WT[i-1] + BurstTime[i-1] (since all arrive at time 0)
Turnaround Time (TT): TT[i] = WT[i] + BurstTime[i]
The main concern with priority scheduling is starvation — low-priority processes may never get the CPU. The solution is aging: gradually increasing a process’s priority the longer it waits.
In this post, we implement the LRU (Least Recently Used) Page Replacement Algorithm in C++. When a page fault occurs and all frames are occupied, LRU evicts the page that has not been used for the longest time. Unlike FIFO which only tracks load order, LRU tracks usage history, making it significantly more effective in practice.
What is LRU Page Replacement?
LRU exploits the principle of temporal locality — recently used pages are likely to be used again soon. By evicting the page that hasn’t been touched for the longest time, LRU keeps the most actively used pages in memory.
Page Hit — Referenced page is already in a frame (its last-used time is implicitly updated).
Page Fault — Referenced page is not in memory. On a fault with full frames, the frame whose page was accessed least recently is selected for replacement.
LRU identification — For each frame, scan the page history backwards from the current reference to find the most recent access of that frame’s page. The frame with the oldest last-access is the LRU victim.
LRU does not suffer from Bélady’s Anomaly (unlike FIFO) and closely approximates the optimal (OPT) algorithm.
In this post, we implement the First Fit Memory Allocation Algorithm in C++. When a new process (job) needs memory, First Fit scans the list of available memory blocks from the beginning and allocates the first block that is large enough to hold the job. It is the fastest of the three classic memory placement strategies — First Fit, Best Fit, and Worst Fit.
What is First Fit Memory Allocation?
Memory is divided into fixed-size blocks (also called partitions). Each block can hold at most one job at a time. When a job arrives:
Scan memory blocks from block 1 onwards.
Allocate the first free block whose size ≥ job size.
Mark that block as occupied. Stop scanning (no need to look at remaining blocks).
If no block fits, the job remains unallocated.
The leftover space inside an allocated block (block size − job size) is called internal fragmentation. Over time, First Fit tends to leave small, unusable gaps in the lower portion of memory (external fragmentation).
