Deadlock is a situation that occurs in a multi-process system when two or more processes are blocked and unable to continue executing because each process is waiting for the other to release a resource. Deadlock can severely impact the performance of the system and lead to a complete system failure. In this tutorial, we will discuss the deadlock avoidance technique used in operating systems to prevent deadlock situations.
📗 What is Deadlock Avoidance?
Deadlock avoidance is a technique used in operating systems to prevent deadlock situations from occurring. In this technique, the system checks whether a particular resource allocation will lead to a deadlock or not before granting the resource. The system uses various algorithms to determine whether a resource allocation is safe or not.
📗 Deadlock Avoidance Algorithms
✔ Banker's Algorithm
The Banker's algorithm is a deadlock avoidance algorithm that works on the principle of ensuring that the system will not enter a deadlock state. In this algorithm, the system keeps track of the available resources, the resources currently allocated to each process, and the maximum resources required by each process. Based on this information, the system decides whether a resource request by a process can be granted or not.
Consider the following example to understand the Banker's algorithm:
Suppose P1 requests 1 unit of A, 1 unit of B, and 0 units of C. The system checks whether granting this request will lead to a deadlock or not. The available resources are (1, 3, 1), and the resources currently allocated to P1 are (2, 0, 1). Therefore, the maximum resources that can be allocated to P1 are (3, 2, 1). Since the system can still allocate the requested resources to P1 without entering a deadlock state, the request is granted.
✔ Resource Allocation Graph Algorithm
The Resource Allocation Graph (RAG) algorithm is a graphical representation of the resource allocation in a system. In this algorithm, the system checks for the presence of a cycle in the RAG to determine whether a deadlock exists or not. If a cycle is present, then a deadlock exists. The system can prevent deadlock by avoiding the formation of a cycle in the RAG.
Suppose we have a Resource Allocation Graph (RAG) as follows:
Processes: P1, P2, P3 Resources: R1, R2
P1 is currently holding resource R1 and is waiting for resource R2 to complete its execution. P2 is currently holding resource R2 and is waiting for resource R1 to complete its execution. P3 is waiting for resource R1 to become available.
P2
/ \
R1 R2
| |
P1 P3
We can use the Banker's algorithm to determine if the current system state is safe or not.
Assuming the current available resources are R1=1 and R2=1, we can create a table to calculate the maximum needs, allocation, and remaining needs for each process:
Now, we can calculate the available resources by summing up the allocation column:
Available Resources: R1=1, R2=1
Next, we can apply the Banker's algorithm to determine if the system is in a safe state or not. To do this, we start by assuming that all processes are requesting their maximum needs, and we calculate the remaining needs.
We can then create a work array, which represents the current available resources:
Work = Available Resources = 1 1
Now, we can iterate over the processes and check if their remaining needs can be satisfied with the current available resources. If a process can be completed, we add its allocated resources to the work array and mark it as finished. We repeat this process until either all processes are finished or we cannot find a process that can be completed.
Iteration 1: Process P1: Need = 0 1, Work = 1 1, Need <= Work, can finish New Work = Work + Allocation(P1) = 1 2 P1 finished
Iteration 2: Process P2: Need = 1 0, Work = 1 2, Need <= Work, can finish New Work = Work + Allocation(P2) = 2 2 P2 finished
Iteration 3: Process P3: Need = 1 1, Work = 2 2, Need <= Work, can finish New Work = Work + Allocation(P3) = 2 2 P3 finished
Since all processes have been finished, the system is in a safe state.
In this example, deadlock avoidance has been successful in preventing a deadlock from occurring. By using the Banker's algorithm, we were able to determine if the system is in a safe state or not and avoid a potential deadlock.
📗 Advantages and Disadvantages of Deadlock Avoidance:
✔ Advantages
Deadlock avoidance ensures that the system will never enter into a deadlock state. This leads to the efficient use of system resources and improved system performance.
Deadlock avoidance algorithms are easy to implement and can be integrated into most operating systems.
✔ Disadvantages
Deadlock avoidance can be very expensive in terms of computational resources and memory. The algorithms require the system to keep track of all the resources, processes, and requests, which can be time-consuming and resource-intensive.
Deadlock avoidance can be complex, and it may not always be possible to determine whether a resource allocation will lead to a deadlock or not. In such cases, the system may have to resort to other techniques such as deadlock detection and recovery.
Deadlock avoidance is an effective technique used in operating systems to prevent deadlock situations. The Banker's algorithm and the Resource Allocation Graph algorithm are two popular deadlock avoidance algorithms. Deadlock avoidance ensures that the system never enters into a deadlock state, leading to improved system performance and efficient use of resources. However, deadlock avoidance can be complex and resource-intensive, and it may not always be possible to determine whether a resource allocation will lead to a deadlock or not.
📗 Multi-Processor Scheduling
Multi-processor scheduling is the process of scheduling tasks and allocating resources on multiple processors in a computer system. The goal of multi-processor scheduling is to maximize system performance by efficiently utilizing all available resources. In this tutorial, we will cover the basics of multi-processor scheduling, types of scheduling algorithms, and their advantages and disadvantages.
✔ Types of Multi-processor Scheduling Algorithms:
Load balancing scheduling: Load balancing is the process of distributing tasks evenly across all processors to ensure that each processor is equally loaded. Load balancing algorithms dynamically adjust the workload of each processor based on the current state of the system. One example of a load balancing algorithm is the Central Queue algorithm, in which a single queue is used to schedule all processes across all processors.
Partitioned scheduling: Partitioned scheduling involves dividing the available resources into fixed-size partitions and allocating these partitions to different processes. Each partition can be assigned to a different processor, and the scheduler decides which partition to allocate to each process. One example of a partitioned scheduling algorithm is the Rate-Monotonic algorithm, which assigns priorities to processes based on their execution time requirements.
Global scheduling: In global scheduling, all processors in the system share a common queue of tasks. The scheduler selects the most appropriate processor to run a task based on its priority and the current state of the system. One example of a global scheduling algorithm is the Earliest Deadline First algorithm, which selects the task with the earliest deadline for execution.
✔ Advantages and Disadvantages of Multi-processor Scheduling:
Advantages:
Improved system performance: Multi-processor scheduling can help to utilize all available resources efficiently, resulting in better system performance.
Reduced response time: By distributing tasks across multiple processors, response time can be reduced, allowing for more efficient task execution.
Increased reliability: In the event of a failure of one processor, other processors can continue to handle tasks, ensuring system reliability.
Disadvantages:
Increased complexity: Multi-processor scheduling is more complex than single-processor scheduling due to the need to coordinate task execution across multiple processors.
Increased overhead: Communication and synchronization overheads are higher in multi-processor systems, which can affect overall performance.
Difficulty in load balancing: Load balancing can be difficult in multi-processor systems, as it can be challenging to distribute tasks evenly across all processors.
✔ Example of Multi-processor Scheduling
Let's consider an example where we have four processors and six tasks to schedule. Each task has a different execution time and priority, and we need to allocate the tasks to the processors in a way that maximizes system performance.
Task Execution Time Priority
T1 5 2
T2 2 3
T3 3 1
T4 6 2
T5 4 1
T6 1 3
One possible way to allocate the tasks to the processors is as follows:
Processor 1: T3, T5
Processor 2: T1, T4
Processor 3: T2, T6
Processor 4: idle
In this example, we have used partitioned scheduling to divide the available resources into four partitions and assign each partition to a different processor. The scheduler has allocated the tasks based on their priority and execution time, ensuring that each processor is equally loaded.
📗 Master Slave Multi-Processor
Master-Slave Multiprocessor refers to a type of multiprocessor system architecture where a single master processor or node controls the overall system while multiple slave processors or nodes carry out the processing tasks assigned by the master. The master node handles the overall coordination, scheduling, and communication among the processors in the system.
In a Master-Slave Multiprocessor architecture, the master processor is responsible for dividing the processing workload among the slave processors and ensuring that each processor receives an equal share of the workload. The master processor also handles the synchronization of data and communication among the processors to ensure the proper execution of tasks.
There are several advantages of using a Master-Slave Multiprocessor architecture, some of which include:
Improved Performance: By distributing processing tasks among multiple processors, the overall system performance can be significantly improved. This is because each processor can work on a separate task simultaneously, reducing the overall processing time.
Scalability: The Master-Slave Multiprocessor architecture is highly scalable, which means that additional processors can be added to the system as the workload increases, allowing the system to handle more processing tasks without any significant decrease in performance.
Fault Tolerance: In the event of a processor failure, the master processor can reassign the workload to other available processors, ensuring that the processing tasks are completed without any interruption.
Flexibility: The Master-Slave Multiprocessor architecture allows for a high degree of flexibility in terms of task allocation and scheduling. The master processor can dynamically allocate processing tasks based on the workload, ensuring that each processor is utilized to its full potential.
✔ Example of Master-Slave Multiprocessor
Let us consider an example of a Master-Slave Multiprocessor architecture in the context of a data processing application. The master node in the system receives the data from the input source and distributes it among the slave nodes for processing. Each slave node performs a specific processing task, such as data sorting or filtering, and returns the processed data to the master node. The master node then combines the results from all the slave nodes to generate the final output.
✔ Advantages and Disadvantages of Master-Slave Multiprocessor
Advantages:
Improved Performance
Scalability
Fault Tolerance
Flexibility
Disadvantages:
Complexity: The Master-Slave Multiprocessor architecture can be complex to implement and manage, requiring specialized hardware and software components.
Synchronization Overhead: The synchronization and communication between the master and slave nodes can add overhead and reduce the overall system performance.
Single Point of Failure: The master node can be a single point of failure, and any failure in the master node can lead to the failure of the entire system.
Multi-processor scheduling is an essential aspect of modern computer systems, allowing for the efficient utilization of all available resources. By understanding the different types of scheduling algorithms and their advantages and disadvantages, we can design efficient and reliable multi-processor scheduling systems that meet our performance and reliability requirements.
Thanks for reading, and happy coding!
Exploring Deadlock Avoidance and Multi-Processor Scheduling in Operating Systems -> Understanding Deadlocks and Threads in Operating Systems: Causes, Prevention, and Solutions