Banker’s Algorithm in Kotlin

The Banker’s Algorithm is a resource allocation and deadlock avoidance algorithm used in operating systems. It was developed by Edsger W. Dijkstra and named after the banking industry, as it deals with the allocation of scarce resources.

The Banker’s Algorithm is used to prevent deadlock in a system where there are multiple processes that compete for a finite set of resources. It works by simulating the allocation of resources to processes and checking whether the system will enter into a safe state or not. A safe state is one in which the system can allocate resources to all processes in some order without causing a deadlock.

The Banker’s Algorithm works by maintaining a set of data structures that represent the current state of the system. These data structures include the maximum demand matrix, the allocation matrix, and the need matrix. The maximum demand matrix specifies the maximum number of resources that a process may need to complete its execution. The allocation matrix specifies the number of resources that have been allocated to each process, and the need matrix specifies the number of resources that are still needed by each process.

To check whether a state is safe, the Banker’s Algorithm simulates the allocation of resources to processes and checks if the system can reach a safe state. If a safe state is reachable, then the system can allocate resources to the processes in that state. If not, the system must wait until a safe state is reachable before allocating resources.

Overall, the Banker’s Algorithm is an important tool for preventing deadlock in operating systems and ensuring the efficient allocation of resources to processes.

class BankerAlgorithm(private val available: IntArray, private val maximum: Array<IntArray>, private val allocation: Array<IntArray>) {
    private val need: Array<IntArray> = Array(maximum.size) { IntArray(maximum[0].size) }

    init {
        for (i in maximum.indices) {
            for (j in maximum[0].indices) {
                need[i][j] = maximum[i][j] - allocation[i][j]
            }
        }
    }

    fun isSafe(): Boolean {
        val work = available.clone()
        val finish = BooleanArray(maximum.size)
        var i = 0

        while (i < maximum.size) {
            if (!finish[i] && hasEnoughResources(i, work)) {
                finish[i] = true
                for (j in work.indices) {
                    work[j] += allocation[i][j]
                }
                i = 0
            } else {
                i++
            }
        }

        return finish.all { it }
    }

    private fun hasEnoughResources(processId: Int, work: IntArray): Boolean {
        for (i in work.indices) {
            if (need[processId][i] > work[i]) {
                return false
            }
        }

        return true
    }
}

fun main() {
    val available = intArrayOf(3, 3, 2)
    val maximum = arrayOf(
        intArrayOf(7, 5, 3),
        intArrayOf(3, 2, 2),
        intArrayOf(9, 0, 2),
        intArrayOf(2, 2, 2),
        intArrayOf(4, 3, 3)
    )
    val allocation = arrayOf(
        intArrayOf(0, 1, 0),
        intArrayOf(2, 0, 0),
        intArrayOf(3, 0, 2),
        intArrayOf(2, 1, 1),
        intArrayOf(0, 0, 2)
    )

    val bankerAlgorithm = BankerAlgorithm(available, maximum, allocation)

    if (bankerAlgorithm.isSafe()) {
        println("The system is in a safe state")
    } else {
        println("The system is not in a safe state")
    }
}

1. The code defines a class BankerAlgorithm which implements the Banker’s Algorithm for resource allocation.
2. The BankerAlgorithm class has three instance variables available, maximum, and allocation. The available variable stores the number of available instances of each resource. The maximum variable is a 2D array that stores the maximum number of instances of each resource that each process can request. The allocation variable is a 2D array that stores the number of instances of each resource that each process is currently holding.
3. The BankerAlgorithm class also defines an instance variable need which is a 2D array that stores the remaining need of each process for each resource. The need array is calculated in the init block of the class by subtracting the allocation array from the maximum array for each process.
4. The isSafe function of the BankerAlgorithm class checks whether the current state of the system is safe or not. It returns a boolean value indicating whether the system is safe or not.
5. The isSafe function uses a loop that iterates over all the processes. For each process, it checks if it has enough resources to complete its execution. If it does, it marks the process as finished and releases its resources. If not, it moves to the next process. This loop is repeated until all processes are either finished or there are no more processes that can finish.
6. The hasEnoughResources function of the BankerAlgorithm class checks if a given process has enough remaining resources to complete its execution. It takes two arguments, the processId and the work array, which stores the number of available instances of each resource at the current state of the system.
7. The main function of the code initializes the available, maximum, and allocation arrays, creates an instance of the BankerAlgorithm class, and calls the isSafe function to check if the current state of the system is safe. Finally, it prints a message indicating whether the system is safe or not.

The time complexity of the Banker’s Algorithm is O(P x R x R), where P is the number of processes and R is the number of resources. In the above implementation, we iterate over the processes multiple times until all processes have finished execution. Within each iteration, we iterate over all resources to check whether a process has enough remaining resources to complete its execution. Therefore, the time complexity is proportional to P x R x R.

The space complexity of the Banker’s Algorithm is O(P x R), as we store the maximum, allocation, and need arrays, each of size P x R. We also store the work and finish arrays, each of size P.

In the above implementation, the space complexity is proportional to P x R. However, it is worth noting that in practice, the number of resources and processes is typically much smaller than the memory capacity of modern computers. Therefore, the space complexity of the Banker’s Algorithm is not usually a limiting factor in its practical application.