Banker's algorithm
The Banker's algorithm is a resource allocation and deadlock avoidance algorithm developed by Edsger W. Dijkstra in 1965 to manage finite resources in multiprogramming systems, ensuring that allocations keep the system in a "safe state" where all processes can complete without deadlock.[1] It models the operating system as a banker granting loans (resources) to multiple customers (processes), each with predefined maximum needs, and only approves requests if a simulation confirms no risk of an unsafe state leading to circular waiting.[1]
The algorithm operates on key data structures representing the system's resource state: Available, a vector of free resources of each type; Max, the maximum resources each process might ever request; Allocation, the resources currently held by each process; and Need, calculated as Max minus Allocation for each process and resource type.[2] When a process requests additional resources, the system first pretends to allocate them and then runs the safety algorithm: it initializes a working copy of Available and a Finish vector for processes, then iteratively searches for an uncompleted process whose Need does not exceed the current Available; if found, it simulates the process finishing by adding its Allocation back to Available and marks it complete, repeating until all processes are finished or no such process exists.[2] If a complete sequence is found, the state is safe, and the request is granted; otherwise, it is postponed or denied to prevent deadlock.[1]
Originally proposed for Dijkstra's THE multiprogramming system at Eindhoven University of Technology, the algorithm provides a dynamic avoidance strategy that is less restrictive than deadlock prevention techniques like resource ordering or denial, but it requires processes to declare maximum needs upfront—a practical limitation in real-world systems where needs may be unpredictable.[3] Its computational complexity arises from repeated safety checks, which can be O(n²m) in the worst case for n processes and m resource types, making it more suitable for theoretical analysis and education than high-throughput production operating systems.[2] Despite limited direct implementation, it remains a foundational concept in operating systems curricula, illustrating principles of safe resource management and influencing subsequent deadlock-handling mechanisms.[3]
Overview
History and Motivation
The Banker's algorithm emerged in the mid-1960s amid the challenges of early multiprogrammed operating systems, where resource contention frequently led to system halts. Systems like the Compatible Time-Sharing System (CTSS), implemented in 1961 at MIT, and Multics, begun in 1965 as a collaborative project by MIT, Bell Labs, and General Electric, introduced concurrent process execution to maximize CPU utilization but struggled with unpredictable resource demands that caused deadlocks—situations where processes indefinitely wait for resources held by each other. In CTSS, for instance, deadlocks arose due to incomplete understanding of concurrency issues during design, resulting in programmed-in vulnerabilities that disrupted operations.[4]
Edsger W. Dijkstra developed the Banker's algorithm in 1965 while leading the design of the THE multiprogramming system at Eindhoven University of Technology, aiming to provide a structured approach to resource management in this experimental OS. The THE system divided activities into layered sequential processes to handle multiprogramming on an Electrologica X8 computer, and the algorithm was proposed as a mechanism to avoid deadlocks without overly restricting resource access. Although the full THE system description appeared in 1968, the algorithm's core ideas were outlined in Dijkstra's 1965 manuscript EWD 108, "An algorithm to prevent the deadly embrace."[1]
The algorithm draws its name and motivation from a banking analogy, where a banker must ensure that loans to customers do not exceed the total available funds to prevent collective insolvency, mirroring how an operating system should allocate resources to processes without risking a system-wide deadlock, or "deadly embrace" as Dijkstra termed it. This approach addresses deadlock avoidance by checking allocations against a safe state, where resources can be satisfied in some order. Deadlocks require four necessary conditions, later formalized as the Coffman conditions: mutual exclusion (resources cannot be shared), hold and wait (processes hold resources while waiting for others), no preemption (resources cannot be forcibly taken), and circular wait (a cycle of processes each waiting for the next's resources). By proactively preventing these conditions through cautious allocation, the algorithm sought to maintain system reliability in resource-scarce environments.
Core Concepts
The Banker's algorithm serves as a deadlock avoidance strategy in operating systems by simulating resource allocations to verify that granting a process's request maintains a safe system state, defined as one in which there exists an ordering of processes that allows all to complete without deadlock. Developed by Edsger W. Dijkstra, the algorithm models resource management akin to a banker lending loans, ensuring that allocations do not exceed the system's capacity to satisfy all outstanding claims.[1] This simulation checks whether, after a hypothetical allocation, sufficient resources remain to sequentially fulfill the maximum needs of all processes through their eventual releases.[1]
At its core, the algorithm enforces pre-allocation of maximum resource claims by each process upon initiation, enabling the system to anticipate and validate future demands against current availability and projected returns from completing processes. This proactive claim declaration distinguishes the approach by treating resources as commitments that must be honored without leading to circular waits, thereby avoiding the resource contention that precipitates deadlocks.[1] By denying requests that would render the system unsafe, it preserves a viable execution path for all active processes.[5]
In contrast to deadlock detection methods, which periodically scan for existing deadlocks and resolve them through process termination or resource preemption after occurrence, the Banker's algorithm prevents deadlocks entirely by rejecting allocations that could lead to an unsafe state, thus minimizing system disruptions and overhead from recovery.[6] This avoidance paradigm prioritizes prevention over cure, though at the cost of potentially conservative resource utilization.[7]
The algorithm operates under specific assumptions, including a fixed total number of identical, reusable resource instances available in the system, the upfront declaration of each process's maximum resource needs, and the hold-and-wait behavior where processes retain allocated resources until completion without preemption.[1] It further presumes that processes will not request more than their declared maximum and that no new processes or resources are introduced during execution, ensuring bounded and predictable demand.[8] These conditions allow the use of structures like allocation and need matrices to model the state concisely.[1]
Data Structures
Resource Allocation Matrices
The Banker's algorithm, developed by Edsger W. Dijkstra, utilizes a set of vectors and matrices to model the resource allocation state in a multiprogramming environment with multiple resource types. These data structures capture the current availability of resources, ongoing allocations to processes, maximum resource demands, and residual needs, enabling the system to assess potential deadlock risks without simulating every possible allocation sequence. The structures assume n processes and m distinct resource types, typically denoted as R1 through Rm.[1][9]
Central to these is the available vector, Available, a one-dimensional array of length m. The entry Available denotes the number of free instances of resource type Rj at any given time. It is dynamically maintained as Available = Total - \sum_{i=1}^{n} Allocation, where Total is the fixed total instances of Rj in the system, reflecting resources not yet allocated to any process. This vector provides a snapshot of immediately usable resources for fulfilling process requests.[9]
The allocation matrix, Allocation, is an n × m matrix that records current resource holdings. Specifically, Allocation equals the number of instances of resource type Rj assigned to process Pi. Each row i corresponds to a process's current resource portfolio, ensuring that allocations do not exceed declared maximums and contribute to computing available resources. Complementing this is the maximum matrix, Max, another n × m matrix where Max specifies the upper limit on instances of Rj that process Pi may request over its lifetime. Processes declare their Max values a priori upon initiation, bounding potential demands to facilitate safe allocation decisions.[9]
Derived from the above, the need matrix, Need, is an n × m matrix representing outstanding resource requirements. It is computed element-wise as:
\text{Need} = \text{Max} - \text{Allocation}
for each process i and resource type j, indicating how many more instances of Rj process Pi requires to finish. Non-negative entries ensure feasibility, as negative needs would violate the maximum claim. Finally, the total resources vector, Total, is a static one-dimensional array of length m, with Total giving the system's overall instances of resource type Rj, unchanged throughout execution and serving as the baseline for all other computations. These matrices and vectors collectively form the foundation for state evaluation in the algorithm.[9]
Process Needs and Claims
In the Banker's algorithm, each process P_i must declare its maximum resource claim, denoted as \text{Max}, upfront upon entering the system, specifying the maximum number of instances it may require for each resource type over its lifetime.[1] This declaration assumes that all resource needs are known in advance, allowing the operating system to assess potential resource demands proactively.[10]
The current need for process P_i, represented as \text{Need}, is computed as the difference between its maximum claim and the resources currently allocated to it:
\text{Need} = \text{Max} - \text{Allocation}
This matrix is updated dynamically whenever resources are allocated or released, reflecting the remaining resources required to complete the process.[1][11]
These declarations and computations play a crucial role in deadlock avoidance by enabling the simulation of resource fulfillment for remaining needs, ensuring that allocations do not lead to a state where the system cannot satisfy all processes without deadlock.[1] Through this mechanism, the algorithm maintains awareness of future demands alongside current allocations.
Algorithms
Safety Algorithm
The safety algorithm in the Banker's algorithm determines whether the current allocation of resources to processes leaves the system in a safe state, meaning there exists at least one sequence of process executions that avoids deadlock by ensuring all processes can complete.[12] It achieves this by simulating a hypothetical execution order where processes release their allocated resources upon completion, allowing subsequent processes to acquire what they need.[12]
The algorithm operates iteratively: it begins by setting the available resources (Work) equal to the current Available vector and initializing a Finish array to false for all m processes. It then repeatedly searches for an unfinished process i where its remaining Need (maximum claim minus current Allocation) is less than or equal to the current Work. If such a process is found, it is assumed to complete, adding its Allocation back to Work and marking Finish as true; this step repeats until no more such processes can be found.[12]
The following pseudocode outlines the procedure:
Work ← Available
for i = 1 to m do
Finish[i] ← false
end for
find an index i such that Finish[i] = false and Need[i] ≤ Work
if no such i exists, go to step 4
Work ← Work + Allocation[i]
Finish[i] ← true
goto step 3
if Finish[j] = true for all j = 1, 2, ..., m, then the system is in a safe state
else the system is in an unsafe state
Work ← Available
for i = 1 to m do
Finish[i] ← false
end for
find an index i such that Finish[i] = false and Need[i] ≤ Work
if no such i exists, go to step 4
Work ← Work + Allocation[i]
Finish[i] ← true
goto step 3
if Finish[j] = true for all j = 1, 2, ..., m, then the system is in a safe state
else the system is in an unsafe state
The order in which processes are selected during the iterations forms a safe sequence if all processes can finish.[12]
The time complexity of this algorithm is O(m² n), where m is the number of processes and n is the number of resource types, arising from up to m iterations, each scanning up to m processes with O(n) comparisons per check. If a safe sequence is found, the output is that sequence of processes; otherwise, the state is declared unsafe, indicating potential deadlock risk.[12]
Resource-Request Algorithm
The Resource-Request Algorithm in the Banker's algorithm manages incoming resource requests from a process P_i to ensure deadlock avoidance by validating and simulating the impact of the allocation. When process P_i submits a request vector \text{Request}_i, the system first verifies that \text{Request}_i \leq \text{Need}_i for each resource type R_j; if this condition fails, an error condition is raised, indicating the process has attempted to exceed its declared maximum claim.[9][1]
If the request passes the need check, the algorithm then examines whether \text{Request}_i \leq \text{Available}; if the available resources are insufficient, the requesting process must wait until resources become available.[9] Upon sufficient availability, the system performs a temporary allocation by updating the state vectors as follows: \text{Available} = \text{Available} - \text{Request}_i, \text{Allocation}_i = \text{Allocation}_i + \text{Request}_i, and \text{Need}_i = \text{Need}_i - \text{Request}_i.[9][13]
With the temporary state established, the Safety Algorithm is invoked to determine if this new configuration remains safe, meaning there exists a sequence of processes that can complete without deadlock.[9] If the safety check confirms a safe state, the temporary changes are committed, and the resources are allocated to P_i; otherwise, the state is restored to its prior values, the request is denied, and P_i waits.[9][1] This simulation-based approach ensures that allocations only proceed when the overall system integrity is preserved.[13]
Special handling applies to trivial cases, such as when \text{Request}_i is a zero vector, in which no allocation or safety check is performed, as the state remains unchanged.[9] The algorithm inherently avoids preemption, as granted resources remain allocated to the process until it completes or releases them voluntarily, aligning with the non-preemptive nature of the underlying resource management model.[13][1]
System States
Safe States
In the Banker's algorithm, a system state is defined as safe if there exists at least one sequence of processes, denoted as \langle P_1, P_2, \dots, P_m \rangle, such that for each process P_i in the sequence, the resources required to satisfy its maximum claim (its Need) can be allocated using the currently available resources plus the resources that would be released by all previously executed processes in the sequence.[1] This definition ensures that the system can complete all processes without entering a deadlock, assuming each process requests resources up to its declared maximum and releases them upon completion.[14]
A key property of safe states is that they are guaranteed to be deadlock-free, as the existence of the sequence provides a scheduling order that avoids resource contention leading to circular waits.[1] However, the Banker's algorithm adopts a conservative approach: not all deadlock-free states are classified as safe, because the safety check assumes the worst-case scenario where processes demand their full maximum claims, potentially rejecting allocations that would otherwise avoid deadlock in practice.[15]
In the state space of possible resource allocations, safe states form a connected region where transitions between them maintain the absence of potential circular waits, often visualized as a subset of the allocation graph excluding paths that could lead to resource exhaustion without completion.[16] The Need matrix, representing each process's remaining resource requirements, is used to verify the feasibility of such sequences in this space.[11]
When a process requests additional resources, the algorithm simulates the allocation and checks if the resulting state remains safe; if so, the request is granted, transitioning the system to another safe state while preserving deadlock avoidance.[17]
Unsafe States
In the Banker's algorithm, an unsafe state is defined as a system configuration in which no safe sequence of process executions exists that allows all processes to complete their resource requests without leading to indefinite postponement.[1] This occurs when the Safety Algorithm, upon evaluation, fails to identify any ordering of processes that can satisfy their maximum claims using the available resources and those reclaimable from completing processes.[1]
The primary risk associated with an unsafe state is the potential for deadlock, where processes enter a circular wait that prevents progress, although an unsafe state does not guarantee deadlock—some unsafe configurations may still resolve if processes request resources conservatively.[18] For instance, if resources are allocated greedily without prior safety checks, a initially safe state can transition to an unsafe one, such as when multiple processes hold partial resources and simultaneously request amounts that exceed the remaining availability, partitioning processes into groups unable to proceed independently.[1] This vulnerability underscores the algorithm's conservative approach, as unsafe states can arise from seemingly reasonable allocations that overlook future needs.
To mitigate these risks, the Banker's algorithm enforces a policy of denying any resource request that would transition the system from a safe to an unsafe state, thereby maintaining deadlock avoidance by restricting allocations to those preserving at least one viable execution sequence.[1] This implication ensures system stability but may introduce inefficiencies, as valid requests are occasionally postponed to avoid hypothetical deadlocks.[18]
Examples
Basic Allocation Example
Consider a system with three processes, P1, P2, and P3, and two types of resources, A and B, with total instances of 10 for A and 5 for B, respectively. The maximum resource claims (Max matrix) and current allocations (Allocation matrix) are as follows, leading to the Need matrix computed as Max minus Allocation. The initial available resources are determined by subtracting the total allocated resources from the overall totals: 3 instances of A and 4 instances of B.[19]
| Process | Max (A, B) | Allocation (A, B) | Need (A, B) |
|---|
| P1 | (3, 2) | (2, 0) | (1, 2) |
| P2 | (9, 0) | (3, 0) | (6, 0) |
| P3 | (2, 2) | (2, 1) | (0, 1) |
- Check P1: Need (1, 2) ≤ Work (3, 4), so allocate to P1, set Finish[P1] = true, and update Work = (3 + 2, 4 + 0) = (5, 4).
- Check P3: Need (0, 1) ≤ Work (5, 4), so allocate to P3, set Finish[P3] = true, and update Work = (5 + 2, 4 + 1) = (7, 5).
- Check P2: Need (6, 0) ≤ Work (7, 5), so allocate to P2, set Finish[P2] = true, and update Work = (7 + 3, 5 + 0) = (10, 5).
All Finish flags are true, confirming the initial state is safe with sequence <P1, P3, P2>.[19]
If a process completes (e.g., P1 finishes first in the sequence), its allocated resources are released, updating the Available vector by adding the Allocation row for that process (Available becomes (3 + 2, 4 + 0) = (5, 4)), and its Need becomes (0, 0). This release enables subsequent processes in the safe sequence to proceed, ensuring no deadlock occurs as the system progresses toward full resource availability.[19]
Request Handling Example
To illustrate the Resource-Request Algorithm in action, consider a continuation from the basic allocation example above, with total resources 10 A and 5 B, and current available resources (3, 4). Suppose P1 submits a request vector of (1, 2), meaning it seeks one additional unit of A and two units of B. The algorithm first verifies that this request does not exceed P1's remaining Need (1, 2) or the Available resources (3, 4). These checks pass, so the system creates a temporary state by pretending to grant the request: P1's Allocation is incremented to (3, 2), Available is decremented to (2, 2), and P1's Need is reduced to (0, 0). The other processes remain unchanged.[20]
The full temporary matrices are as follows:
Temporary Allocation Matrix:
Temporary Need Matrix:
Temporary Available: (2, 2)
Running the Safety Algorithm starts with Work = (2, 2) and Finish flags false for all processes.
- Check P3: Need (0, 1) ≤ (2, 2), so finish P3, update Work = (2 + 2, 2 + 1) = (4, 3).
- Check P1: Need (0, 0) ≤ (4, 3), so finish P1, update Work = (4 + 3, 3 + 2) = (7, 5).
- Check P2: Need (6, 0) ≤ (7, 5), so finish P2.
A safe sequence <P3, P1, P2> exists, so the temporary state is safe, and the request is granted by making the temporary changes permanent. The final matrices after granting reflect the updated state, ensuring no deadlock risk.[20]
In contrast, suppose P1 instead requests (2, 0). This exceeds the Available A (2 > 3? Wait, no—request (2,0) <= need (1,2)? 2>1 for A, so the algorithm immediately denies the request without proceeding to the Safety Algorithm, as it violates the need limit, preventing potential over-allocation and unsafe states. P1 must wait until its need is adjusted or resources are released.[19]
Limitations and Extensions
Key Limitations
The Banker's algorithm requires processes to declare their maximum resource claims in advance, which is often unrealistic in practice because many processes have unpredictable or evolving resource needs that cannot be accurately forecasted at startup.[21][22] This upfront declaration can lead to conservative estimates, potentially causing processes to request excessive resources or face denial later due to inaccurate predictions.[21]
A significant drawback is the high computational overhead associated with frequent safety checks, which have a time complexity of O(m n²), where m is the number of resource types and n is the number of processes; this makes the algorithm inefficient for systems with large numbers of processes or resource types.[22] In resource-constrained environments, such as operating systems handling numerous concurrent tasks, these repeated simulations can introduce substantial delays during resource allocation requests.[22]
The algorithm's conservative approach, which denies any request that could lead to an unsafe state—even if no immediate deadlock occurs—can result in unnecessary blocking of processes and reduced overall resource utilization.[23] For instance, available resources may go unused because the algorithm prioritizes avoiding potential future deadlocks over maximizing current throughput, leading to lower system efficiency in dynamic workloads.[23]
Additionally, the Banker's algorithm assumes a static pool of resources with a fixed total quantity, which does not account for scenarios where resources can be dynamically added or removed, such as in modern distributed systems or cloud environments.[16] This limitation renders it less applicable to environments with variable resource availability, as the safety analysis relies on unchanging totals that may not reflect real-time changes.[16]
Modern Adaptations
One significant adaptation of the Banker's algorithm involves hierarchical structures to manage resource allocation in distributed and multi-level systems. In 1984, Madduri and Finkel proposed a hierarchical extension that organizes processors in a tree-like hierarchy, applying a modified version of the Banker's algorithm at each level to decentralize decision-making and reduce the overhead of centralized control.[24] This approach ensures deadlock avoidance by limiting inter-subtree allocations while allowing local safe states within subtrees, making it suitable for distributed operating systems with resource hierarchies.[24]
To address the original algorithm's assumption of static maximum claims, dynamic adaptations have emerged that permit updates to resource needs during execution. For instance, the Dynamic Banker's Deadlock Avoidance Algorithm (DBDAA), introduced in 2024 by Zohora et al., incorporates a ready queue to dynamically include processes in safety checks, reducing time complexity from O(n²d) to O(nd) on average and enabling real-time responsiveness in time-sensitive environments.[25] This variant simulates allocations with updated needs, preventing deadlocks while adapting to changing demands, and has shown improved efficiency in simulations compared to the classical method.[25]
In embedded systems, particularly system-on-chip (SoC) designs, parallel and hardware-optimized versions of the Banker's algorithm facilitate efficient deadlock avoidance under resource constraints. Lee and Mooney's 2006 work presents an O(n) parallel Banker's algorithm with a dedicated hardware unit (PBAU) that processes multiple resource types simultaneously, achieving up to 10 times faster safety checks than sequential implementations in FPGA prototypes for embedded multiprocessor SoCs.[26] This adaptation is particularly valuable in real-time operating systems (RTOS) where low-latency resource management is critical, as it integrates directly into hardware to monitor and allocate shared resources like memory or peripherals without software overhead.
Contemporary cloud resource allocation also draws on Banker's-inspired techniques for safe scaling. Dhenakaran and Priya's 2013 implementation adapts the algorithm for virtualized environments, using it to simulate resource requests in a pay-as-you-go model, ensuring that allocations across virtual machines maintain a safe state amid dynamic scaling.[27]
Post-2000 research has explored AI for predicting resource needs in deadlock avoidance, though direct integrations with Banker's algorithm remain limited as of 2024.