Prerequisite: Deadlock detection in Distributed systems In hierarchical deadlock detection algorithm, sites are arranged in a hierarchical fashion and a site detects deadlocks involving only its descendant sites. Distributed deadlock algorithms delegate the responsibility of deadlock detection to individual sites while in hierarchical there are local detectors at each site which communicate their local wait for graphs(WFG) with one another. Approach: Deadlocks that are local to a single site are detected at that site using their local WFG. Each site also sends its local WFG to deadlock detector at the next level. Thus, distributed deadlocks involving 2 or more sites would be detected by a deadlock detector in lowest level that has control over these sites. In this approach, there are 2 methods to detect: 1. Ho-Ramamoorthy algorithm:
- Uses only two levels i.e. Master control nodes and Cluster control nodes.
- Cluster control nodes are used for detecting deadlock among their members and reporting dependencies outside their cluster to Master control node.
- The master control node is responsible for detecting inter-cluster deadlocks.
2. Menasce-Muntz algorithm:
- Leaf controllers are responsible for allocating resources whereas branch controllers find deadlock among the resources that their children span in the tree.
- Network congestion can be managed and node failure is less critical than in fully centralized.
- Detection can be done ways such as Continuous allocation reporting or periodically allocation reporting.
Advantages:
- If the hierarchy coincides with resource access pattern local to cluster of sites, this approach can provide efficient deadlock detection as compared to both centralized and distributed methods.
- It reduces the dependence on central sites thus, reducing the communication cost.
- It allows each cluster to make its own deadlock detection decisions, reducing the need for centralized coordination and improving fault tolerance. This can help to ensure that the system
- It can be designed to take advantage of the local resources available in each cluster, which can help to improve the overall performance and efficiency of the system. This can be particularly beneficial in resource-constrained environments.
- It allows each cluster to have greater control over its own resources, reducing the risk of conflicts and other issues that can arise when multiple clusters compete for the same resources. This can help to ensure that the system operates smoothly and reliably over time.
Disadvantages:
- If deadlocks are span over several clusters, this approach will be inefficient.
- It is more complicated to implement and would involve nontrivial modification to lock and transaction manager algorithms.
- It is limited in its ability to detect deadlocks that span multiple clusters or resource types. This can result in false negatives or missed deadlocks, which can impact the overall reliability of the distributed system.
- It can be more complex to implement and maintain than other deadlock detection approaches, due to the need to manage the hierarchy and coordinate deadlock detection decisions across multiple clusters. This can increase the risk of errors and require specialized expertise to manage effectively.
- It can introduce additional latency into the system, as deadlock detection decisions may need to be coordinated across multiple clusters before a resolution can be reached. This can impact the overall performance and responsiveness of the system, particularly in time-critical applications.