Multilevel Queue (MLQ) CPU Scheduling


CPU scheduling with multilevel queues (MLQ) is a scheduling technique implemented in Linux and Windows to arrange procedures over the carrying out on a system's CPU. MLQ divides procedures through numerous waiting lists, each of which has a distinct level of priority. Every queue might come with its own scheduling algorithm, which allows the OS to prioritize various kinds of methods in various ways.

There are several ways for executing the MLQ scheduling algorithm. A commonly used approach is to separate procedures into two separate waiting lists, resulting in the forefront queue getting more priority compared to the background process queue. Operations are allocated time within every line employing a First-Come-First-Served (FCFS) planning method.

In this article, we will be discussing the Multilevel Queue (MLQ) CPU Scheduling, its various approaches, the components, use cases, and examples.

Multilevel Queue (MLQ) CPU Scheduling Approaches

Here are some of the MLQ CPU Scheduling Approaches−

Multilevel Feedback Queue (MLFQ) − The MLFQ approach is a variation of the multilevel queue scheduling algorithm. It employs multiple queues with varying priorities and allocates time slices to processes in each queue. However, the MLFQ algorithm allows processes to move between queues based on their behavior.

Processes that use excessive CPU time or have higher priority may be moved to a higher-priority queue, while processes that wait for I/O or exhibit low CPU usage may be demoted to a lower-priority queue. This dynamic behavior allows the system to adapt to changing process requirements.

Multilevel Priority Queue − In the multilevel priority queue approach, each queue is assigned a different priority level, and processes are scheduled based on their priority. Processes with higher priority are given preference and are executed before lower-priority processes. This approach is suitable for systems where priority-based scheduling is critical, such as real-time systems.

Components of Multilevel Queue (MLQ) CPU Scheduling

Let us now talk about the components of Multilevel Queue (MLQ) CPU Scheduling.

Queues − The multilevel queue scheduling algorithm consists of multiple queues, each with a different priority level. Processes are assigned to these queues based on their priority or other criteria. Each queue can have its own scheduling algorithm or policies.

Scheduling Policies − Each queue in the multilevel queue scheduling algorithm can have its own scheduling policy or algorithm. Common scheduling policies include First-Come-First-Served (FCFS), Round-Robin (RR), Shortest Job Next (SJN), or Priority Scheduling. These policies determine the order in which processes are selected from the queues for execution.

Dispatcher − The dispatcher is responsible for selecting a process from the highest-priority non-empty queue and allocating the CPU to that process. It handles context switching, loading and unloading processes from the CPU, and executing the selected process.

Use Cases of Multilevel Queue (MLQ) CPU Scheduling

Time-Sharing Systems − Multilevel queue scheduling is commonly used in time-sharing systems where multiple users share system resources. The queues can be used to prioritize interactive user processes, background tasks, and system daemons, ensuring fair allocation of resources and responsive user experience.

Server Environments − In server environments, different types of tasks or services may have varying levels of importance or response time requirements. Multilevel queue scheduling allows prioritizing critical services or high-priority tasks over lower-priority background tasks, ensuring that important tasks are completed in a timely manner.


In this example, the python code demonstrates a simple process scheduling algorithm based on priority queues. It creates three queues for different priority levels: high, medium, and low. Processes are enqueued into their respective queues. The code then checks the queues in priority order and executes the first available process. In this example, the high-priority process "Process A" is selected from the high-priority queue and executed, as it is the highest-priority queue with a process present.

from queue import Queue

# Create multiple queues for different priority levels
high_priority_queue = Queue()
medium_priority_queue = Queue()
low_priority_queue = Queue()

# Enqueue processes into their respective queues
high_priority_queue.put("Process A")
medium_priority_queue.put("Process B")
low_priority_queue.put("Process C")

# Process scheduling
if not high_priority_queue.empty():
   process = high_priority_queue.get()
   print("Executing high-priority process:", process)
elif not medium_priority_queue.empty():
   process = medium_priority_queue.get()
   print("Executing medium-priority process:", process)
elif not low_priority_queue.empty():
   process = low_priority_queue.get()
   print("Executing low-priority process:", process)
   print("No processes in the queues.")


Executing high-priority process: Process A


Applying the Multilevel Queue (MLQ) scheduling CPU method has multiple benefits, such as −

  • Improved system responsiveness − The MLQ method makes sure higher-priority procedures get carried out quicker by establishing objectives with numerous kinds of processes, resulting in an increasingly accommodating system.

  • Better resource utilization − The MLQ technique may assist in making sure that assets are used with greater effectiveness by permitting distinct queues to use distinctive scheduling methods.

  • Support for different types of processes − The MLQ algorithm might be tailored to accommodate a variety of procedures with varying demands on scheduling.

  • Flexibility − By modifying the number of queues, the scheduling procedures used for every queue, as well as the parameters applied when deciding which of the procedures must be assigned, the MLQ algorithm can be tailored to the needs of various systems.


Applying the Multilevel Queue (MLQ) scheduling CPU method has multiple downfalls as well, such as−

  • Complexity − The MLQ algorithm can be more complicated compared to easier scheduling algorithms such as First-Come, First-Served, or Round-Robin.

  • Overhead − Upholding numerous queues can cause framework overhead. The greater the number of queues, the greater amount of CPU time needs to be spent handling them, potentially reducing the system's overall efficiency.

  • Inefficiency − The MLQ algorithm might be more inefficient compared to alternative scheduling computer programs, which include Shortest Job First (SJF) or Priority Scheduling, in particular instances.

  • Complex implementation − The total number of queues, the scheduling methods employed for every queue, as well as the parameters utilized when deciding which of them an operation needs to be allocated to must all be carefully considered when carrying out the MLQ algorithm.


A scheduling algorithm that can boost the system's efficiency and make sure that assets are used more effectively is the Multilevel Queue (MLQ) CPU planning algorithm. The MLQ algorithm can improve structure flexibility, hinder starvation of low-priority procedures, and support various types of procedures with various arranging necessities by splitting procedures into numerous queues with various priorities and scheduling algorithms.

The MLQ algorithm can add expenditure to the framework, be less effective in some circumstances, and have problems with implementation because it is more complicated than less difficult scheduling algorithms. Thus, it is crucial that one carefully considers the particular demands of the system as well as the trade-offs involved while determining whether or not to employ the MLQ algorithm or a different scheduling algorithm.

Updated on: 14-Jul-2023


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