Fault Tolerance in Distributed System

Fault tolerance in distributed systems is the capability to continue operating smoothly despite failures or errors in one or more of its components. This resilience is crucial for maintaining system reliability, availability, and consistency. By implementing strategies like redundancy, replication, and error detection, distributed systems can handle various types of failures, ensuring uninterrupted service and data integrity.

In distributed systems, three types of problems occur. All these three types of problems are related.

• Fault: Fault is defined as a weakness or shortcoming in the system or any hardware and software component. The presence of fault can lead to error and failure.
• Errors: Errors are incorrect results due to the presence of faults.
• Failure: Failure is the outcome where the assigned goal is not achieved.

What is Fault Tolerance?

Fault Tolerance is defined as the ability of the system to function properly even in the presence of any failure. Distributed systems consist of multiple components due to which there is a high risk of faults occurring. Due to the presence of faults, the overall performance may degrade.

Types of Faults

• Transient Faults: Transient Faults are the type of faults that occur once and then disappear. These types of faults do not harm the system to a great extent but are very difficult to find or locate. Processor fault is an example of transient fault.
• Intermittent Faults: Intermittent Faults are the type of faults that come again and again. Such as once the fault occurs it vanishes upon itself and then reappears again. An example of intermittent fault is when the working computer hangs up.
• Permanent Faults: Permanent Faults are the type of faults that remain in the system until the component is replaced by another. These types of faults can cause very severe damage to the system but are easy to identify. A burnt-out chip is an example of a permanent Fault.  

Need for Fault Tolerance in Distributed Systems

Fault Tolerance is required in order to provide below four features.

1. Availability: Availability is defined as the property where the system is readily available for its use at any time.
2. Reliability: Reliability is defined as the property where the system can work continuously without any failure.
3. Safety: Safety is defined as the property where the system can remain safe from unauthorized access even if any failure occurs.
4. Maintainability: Maintainability is defined as the property states that how easily and fastly the failed node or system can be repaired.

Fault Tolerance in Distributed Systems

In order to implement the techniques for fault tolerance in distributed systems, the design, configuration and relevant applications need to be considered. Below are the phases carried out for fault tolerance in any distributed systems.

1. Fault Detection

Fault Detection is the first phase where the system is monitored continuously. The outcomes are being compared with the expected output. During monitoring if any faults are identified they are being notified. These faults can occur due to various reasons such as hardware failure, network failure, and software issues. The main aim of the first phase is to detect these faults as soon as they occur so that the work being assigned will not be delayed.

2. Fault Diagnosis

Fault diagnosis is the process where the fault that is identified in the first phase will be diagnosed properly in order to get the root cause and possible nature of the faults. Fault diagnosis can be done manually by the administrator or by using automated Techniques in order to solve the fault and perform the given task.

3. Evidence Generation

Evidence generation is defined as the process where the report of the fault is prepared based on the diagnosis done in an earlier phase. This report involves the details of the causes of the fault, the nature of faults, the solutions that can be used for fixing, and other alternatives and preventions that need to be considered.

4. Assessment

Assessment is the process where the damages caused by the faults are analyzed. It can be determined with the help of messages that are being passed from the component that has encountered the fault. Based on the assessment further decisions are made.

5. Recovery

Recovery is the process where the aim is to make the system fault free. It is the step to make the system fault free and restore it to state forward recovery and backup recovery. Some of the common recovery techniques such as reconfiguration and resynchronization can be used.

Types of Fault Tolerance in Distributed Systems

1. Hardware Fault Tolerance: Hardware Fault Tolerance involves keeping a backup plan for hardware devices such as memory, hard disk, CPU, and other hardware peripheral devices. Hardware Fault Tolerance is a type of fault tolerance that does not examine faults and runtime errors but can only provide hardware backup. The two different approaches that are used in Hardware Fault Tolerance are fault-masking and dynamic recovery.
2. Software Fault Tolerance: Software Fault Tolerance is a type of fault tolerance where dedicated software is used in order to detect invalid output, runtime, and programming errors. Software Fault Tolerance makes use of static and dynamic methods for detecting and providing the solution. Software Fault Tolerance also consists of additional data points such as recovery rollback and checkpoints.
3. System Fault Tolerance: System Fault Tolerance is a type of fault tolerance that consists of a whole system. It has the advantage that it not only stores the checkpoints but also the memory block, and program checkpoints and detects the errors in applications automatically. If the system encounters any type of fault or error it does provide the required mechanism for the solution. Thus system fault tolerance is reliable and efficient.

Fault Tolerance Strategies

Fault tolerance strategies are essential for ensuring that distributed systems continue to operate smoothly even when components fail. Here are the key strategies commonly used:

• Redundancy and Replication
  • Data Replication: Data is duplicated across multiple nodes or locations to ensure availability and durability. If one node fails, the system can still access the data from another node.
  • Component Redundancy: Critical system components are duplicated so that if one component fails, others can take over. This includes redundant servers, network paths, or services.
• Failover Mechanisms
  • Active-Passive Failover: One component (active) handles the workload while another component (passive) remains on standby. If the active component fails, the passive component takes over.
  • Active-Active Failover: Multiple components actively handle workloads and share the load. If one component fails, others continue to handle the workload.
• Error Detection Techniques
  • Heartbeat Mechanisms: Regular signals (heartbeats) are sent between components to detect failures. If a component stops sending heartbeats, it is considered failed.
  • Checkpointing: Periodic saving of the system's state so that if a failure occurs, the system can be restored to the last saved state.
• Error Recovery Methods
  • Rollback Recovery: The system reverts to a previous state after detecting an error, using saved checkpoints or logs.
  • Forward Recovery: The system attempts to correct or compensate for the failure to continue operating. This may involve reprocessing or reconstructing data.

Design Patterns for Fault Tolerance

Design patterns for fault tolerance help in creating systems that can handle failures gracefully and maintain reliable operations. Here are some key fault tolerance design patterns:

1. Circuit Breaker Pattern

This pattern prevents a system from making calls to a failing service by wrapping it in a "circuit breaker." When the service fails, the circuit breaker trips, causing further calls to fail fast instead of trying to connect to a failing service repeatedly.

Useful in scenarios where services might experience temporary outages. For example, a microservices architecture where a downstream service might be unreliable.

2. Bulkhead Pattern

This pattern isolates different components or services to prevent a failure in one part of the system from affecting others. It’s similar to the bulkheads in a ship that prevent flooding in one compartment from sinking the entire vessel.

Essential in systems where failures in one service should not impact others. For instance, an e-commerce platform might use bulkhead isolation to separate payment processing from inventory management.

3. Retry Pattern

This pattern involves automatically retrying an operation that has failed due to transient errors. The retries are typically done with exponential backoff to avoid overwhelming the system.

Suitable for scenarios where operations might fail intermittently due to temporary issues like network glitches or service overloads.

4. Rate Limiting Pattern

This pattern controls the number of requests a system or service can handle within a specific time window to prevent overload and ensure fair usage.

Essential for APIs and services that might be susceptible to abuse or excessive traffic. It helps in maintaining system stability and performance.

5. Failover Pattern

This pattern involves switching to a backup system or component when the primary one fails. It ensures continuity of service by having redundant systems ready to take over.

Ideal for systems requiring high availability, such as critical financial systems or cloud services.

Conclusion

Fault Tolerance in Distributed Systems is a major task that needs to be accomplished. Faults can lead to a reduction in the overall performance of the system. The faults that arise also differ from one another. Therefore these faults need to be identified and handled according to the working, architecture, and applications of the given distributed systems.