The document discusses recovery mechanisms in distributed operating systems (DOS), emphasizing their role in maintaining system reliability and data integrity after failures due to various disruptions. It outlines key concepts, types of failures, and strategies for error recovery, such as backward and forward error recovery, and addresses challenges in concurrent systems. Effective strategies are crucial for ensuring consistency and system performance in distributed environments.

1. NADAR SARASWATHI COLLEGE OF ARTS AND SCIENCE
DISTRIBUTED OPERATING SYSYTEM
RECOVERY
BY:
P. Roshini
I – M.sc (CS)

2. In distributed operating systems (DOS), recovery mechanisms are essential for maintaining
system reliability and data integrity, allowing the system to restore functionality after failures caused
by network disruptions, hardware malfunctions, or software errors. These mechanisms ensure
continuous availability of services, protect against data loss, and facilitate smooth operation despite
the inherent complexities of a distributed environment. Effective recovery strategies, such as
backward and forward error recovery, are crucial for addressing transient and permanent failures,
while also managing challenges like state synchronization and communication delays. Overall, a
robust recovery framework is fundamental for building resilient distributed applications that meet
user expectations for uptime and performance.
INTRODUCTION:

3. 1. BASIC CONCEPTS OF RECOVERY IN DISTRIBUTED OPERATING SYSTEMS:
In distributed operating systems (DOS), several key concepts are essential for effective
recovery
I) Error vs. Failure:
❖ Error: A deviation from expected behavior.
❖ Failure: A condition where a system cannot perform its intended function.
II) Types of Failures:
❖ Transient Failures: Temporary issues, like brief network outages.
❖ Permanent Failures: Serious faults requiring corrective action.
❖ Partial Failures: Some components fail while others continue to operate.

4. III) Recovery Techniques:
❖ Checkpointing: Saving system state at intervals to allow rollback.
❖ Logging: Keeping a record of operations to reconstruct state post-failure.
❖ Redundancy: Using duplicate components to provide alternatives during failures.
IV) Consistency Models:
❖ Eventual Consistency: Nodes may have temporary discrepancies but will converge over
time.
❖ Strong Consistency: All nodes see the same data simultaneously.
V) Scalability and Performance:
❖ Scalability: Handling growth in nodes or data without performance loss.
❖ Performance: Efficiency of recovery processes to minimize downtime.

5. 2. CLASSIFICATION OF FAILURES IN DISTRIBUTED OPERATING SYSTEMS
In distributed operating systems (DOS), understanding the various types of failures is essential
for effective recovery strategies. Here’s a classification that includes process failures, system
failures, secondary storage failures, and communication medium failures:
I) Process Failure:
Occurs when a specific process in the system terminates unexpectedly or behaves incorrectly due to
bugs, resource exhaustion, or other issues.
II) System Failure:
A failure that affects the overall system operation, rendering it unable to perform its intended functions.
This could be due to hardware malfunctions, software crashes, or power outages.
III) Secondary Storage Failure:
Occurs when data stored on non-volatile storage (like hard drives or SSDs) becomes inaccessible due to
hardware failure, corruption, or file system errors.
V) Communication Medium Failure
Refers to issues in the network or communication channels that prevent successful data transmission
between nodes in a distributed system.

6. 3. BACKWARD AND FORWARD ERROR RECOVERY IN DISTRIBUTED OPERATING SYSTEMS
In distributed operating systems (DOS), error recovery is critical for maintaining system integrity and
availability. Two primary strategies for error recovery are backward error recovery and forward error recovery.
BACKWARD ERROR RECOVERY:
Backward error recovery involves reverting the system to a previously saved state in response to an error or
failure. This method allows the system to recover from a known good state, effectively undoing the effects of the
error.
Advantages:
❖ Simple to implement, especially in systems with well-defined checkpoints.
❖ Effective for handling transient errors and system crashes.
Disadvantages:
❖ Potentially high overhead due to the need for frequent state saving.

7. 4. BACKWARD-ERROR RECOVERY: BASIC APPROACHES
Backward-error recovery is vital in distributed operating systems, allowing systems to revert to a previously
consistent state after a failure. The two primary approaches to implementing backward-error recovery are the
operation-based approach and the state-based approach.
1. The Operation-Based Approach
This approach focuses on recording individual operations that change the system state, enabling the
system to roll back to a known good state when necessary.
Key Features:
❖ Logging: Each operation is logged, capturing the sequence of changes made by processes. This log enables
the system to undo operations in the event of a failure.
❖ Transaction Management: Operations are often grouped into transactions, which ensure atomicity—either
all operations within a transaction are completed, or none are.

8. 2. The State-Based Approach
This approach involves periodically saving the entire state of the system, allowing for recovery from a
known good state after a failure.
Key Features:
❖ Checkpoints: The system takes snapshots of its entire state at regular intervals. These checkpoints serve
as recovery points.
❖ State Restoration: When a failure occurs, the system can revert to the most recent checkpoint,
discarding any changes made since that point.
Both the operation-based and state-based approaches to backward-error recovery provide valuable strategies
for managing failures in distributed operating systems.

9. 5. Recovery in Concurrent Systems in Distributed Operating Systems
In distributed operating systems (DOS), concurrent systems involve multiple processes executing
simultaneously, often interacting with shared resources. This concurrency presents unique challenges for
recovery after failures. Effective recovery mechanisms are crucial for ensuring data consistency and system
reliability in such environments.
1. Challenges of Recovery in Concurrent Systems
Race Conditions: Multiple processes accessing shared resources can lead to inconsistencies if not properly
managed during recovery.
State Dependencies: The state of one process may depend on the states of others, complicating the recovery
process.
Deadlocks: Failures can lead to deadlocks, where processes wait indefinitely for resources held by each other,
necessitating careful management during recovery.

10. 2. Recovery Strategies
A. Checkpointing and Rollback
❖ Coordinated Checkpointing: Processes coordinate to take checkpoints at the same time, ensuring that
they all reflect a consistent state. If a failure occurs, the system can roll back to the most recent coordinated
checkpoint.
❖ Uncoordinated Checkpointing: Processes take checkpoints independently. This can lead to
inconsistencies if one process rolls back while another does not. To address this, additional mechanisms,
such as communication logs, may be needed to reconstruct a consistent state.
B. Logging
❖ Distributed Logging: Each process logs its operations, allowing for recovery by replaying or undoing
operations based on the log. This approach helps to maintain consistency across processes.
❖ Recovery Protocols: Protocols such as Two-Phase Commit (2PC) can be employed to ensure that all
processes reach a consensus on committing or rolling back changes.

11. 3. Consistency Models:
To manage recovery in concurrent systems, different consistency models can be employed:
❖ Strong Consistency: Guarantees that all processes see the same data at the same time, which
simplifies recovery but can impact performance.
❖ Eventual Consistency: Allows temporary inconsistencies, ensuring that all processes will
eventually converge to the same state, which can make recovery more complex.
C. State-Based Recovery
❖ Snapshot Isolation: The system periodically takes snapshots of the entire state, allowing it to revert to
a known good state. This method can be resource-intensive but simplifies recovery.

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