Banker's Algorithm

Banker's Algorithm

Definition

The Banker's Algorithm is a resource allocation and deadlock avoidance algorithm used in operating systems. It works by simulating the allocation of requested resources to determine if a system can remain in a safe state to avoid deadlocks. It is named after a banking system, where the bank ensures that it never allocates cash in such a way that it can no longer satisfy the needs of its customers.

Why use Banker's Algorithm?

Operating systems must manage resources efficiently and prevent deadlocks. A deadlock occurs when two or more processes are indefinitely waiting for each other to release a resource. The Banker's Algorithm ensures that the system remains in a safe state, meaning there is an order in which all processes can complete their execution without causing a deadlock.

Key Concepts

1. Safe State: A state is considered safe if the system can allocate resources to each process (up to its maximum) in some order and still avoid a deadlock. This means there exists a safe sequence of processes.

2. Unsafe State: If no such safe sequence exists, the system is in an unsafe state. An unsafe state does not necessarily mean a deadlock will occur, but it indicates a possibility of a deadlock.

3. Safe Sequence: A sequence of processes {1, P2, …, Pn>} is a safe sequence if, for each ${P</em>i}$, the resources that ${P<em>i}$ still needs can be satisfied by the currently available resources plus the resources held by all ${P</em>j}$ where {j < i}. If ${P<em>i}$'s needs are satisfied, it can run to completion, release its resources, and then ${P</em>{i+1}}$ can use those resources, and so on.

Data Structures Needed

To implement the Banker's Algorithm, the following data structures are required:

• Available: A vector of length m (number of resource types) indicating the number of available resources of each type. If ${Available[j] = k}$, then k instances of resource type j are available.

• Max: An $n \times m$ matrix defining the maximum demand of each process. If ${Max[i][j] = k}$, then process ${P_i}$ may request at most k instances of resource type *j}.

• Allocation: An $n \times m$ matrix defining the number of resources of each type currently allocated to each process. If ${Allocation[i][j] = k}$, then process ${P_i}$ is currently allocated k instances of resource type *j$.

• Need: An $n \times m$ matrix indicating the remaining resource need of each process. If ${Need[i][j] = k}$, then process ${P_i}$ still needs k instances of resource type *j$. This can be calculated as ${Need[i][j] = Max[i][j] - Allocation[i][j]}$.

Where:

• $n$ = number of processes

• $m$ = number of resource types

Algorithm Steps (Safety Algorithm)

This algorithm checks if the current state is safe (i.e., if a safe sequence exists).

1. Initialization:

   • Let Work be a vector of length m, initialized to Available. (Work tracks available resources during simulation).

   • Let Finish be a boolean vector of length n, initialized to false for all processes. (Finish[i] = true if ${P_i}$ can complete).

2. Find a Process:

   • Find an index ${i}$ such that Finish[i] == false and ${Need<em>i \le Work}$ (where ${Need</em>i}$ is the vector for process ${P_i}$'s needs).

   • If no such ${i}$ exists, go to Step 4.

3. Allocate and Release (Simulate):

   • If such an ${i}$ is found:

     • Work = Work + Allocation_i (resources ${P_i}$ held are now 'available').

     • Finish[i] = true (process ${P_i}$ can complete).

     • Go back to Step 2.

4. Check for Safety:

   • If Finish[i] == true for all ${i}$, then the system is in a safe state.

   • Otherwise, the system is in an unsafe state.