Fault-tolerance is the ability of an algorithm to handle unexpected errors or failures without crashing or producing incorrect results. It is a desirable quality for any software system, especially for mission-critical or distributed applications. But how can you design your algorithm to be fault-tolerant? Here are some tips and best practices to follow.
One of the most common ways to achieve fault-tolerance is to use redundancy, which means having multiple copies or backups of the same data or function. For example, you can store your data in multiple locations, use replication or mirroring techniques, or run multiple instances of your algorithm on different servers or nodes. This way, if one copy fails, you can switch to another one without losing any information or functionality.
To ensure your algorithm design pattern is fault-tolerant, focus on building resilience into the system. Implement error-handling mechanisms that gracefully handle unexpected situations. Incorporate redundancy and failover mechanisms to maintain functionality in the face of failures. Conduct thorough testing, including edge cases and stress testing, to identify potential vulnerabilities. Employ monitoring and logging to detect issues early and enable prompt response. Foster a culture of continuous improvement, encouraging the team to learn from failures and iteratively enhance the design for robustness. Regularly review and update the fault-tolerance strategies based on evolving system requirements and potential risks.
Best way for fault-tolerant algorithm is resolve edge cases as many as you can. Other benefits will coming with it automaticaly.
Another way to handle errors or failures is to use exceptions, which are special events that interrupt the normal flow of your algorithm and trigger a predefined response. For example, you can use try-catch blocks to catch and handle exceptions, such as input errors, network failures, or memory issues. You can also use custom exceptions to define your own error conditions and responses. Exceptions allow you to gracefully recover from errors or failures, or at least log them and notify the user.
Sometimes, errors or failures are not detected or reported by your algorithm, but rather by the lack of response or feedback. For example, your algorithm may be stuck in an infinite loop, waiting for a resource that is unavailable, or communicating with a service that is down. In these cases, you can use timeouts, which are limits on how long your algorithm can wait for something to happen. For example, you can set a timeout for a network request, a database query, or a function call. If the timeout expires, you can abort the operation and try a different approach or report an error.
FAST RESPONSE ARE A MUST At an industry level algorithms need to be fast, if the algorithm takes too much time to train or run it might not be ideal, — everything is correct but the time required to converge is too long, and generally this indicates a poor problem definition where the steepest gradient descent cannot find the steepest direction — the algorithm has some error in the coding and it gets stuck in infinite loops (such as never entering a break condition, a while that is never false, etc). By setting a time limit you avoid both these issues and you can explore which error you are hitting and solve it accordingly.
Another technique to deal with errors or failures is to use retries, which means attempting the same operation again after a failure. For example, you can retry a network request, a database query, or a function call if it fails due to a transient error, such as a network congestion, a server overload, or a temporary outage. However, you should be careful not to retry indefinitely or too frequently, as this may cause more problems or waste resources. You should use a backoff strategy, which means increasing the delay between retries, or a circuit breaker pattern, which means stopping the retries after a certain threshold.
ALWAYS USE RETRY EVENTUALLY WITH A LOG Sometimes you need to use retry, this happens for example on very congested Database, and it is important to retry before investing any possible issue. However there should be some good practices such as, — keep a log on how many failures you have to investigate the phenomenon — if the issue persist automatically activate the failure log so to understand what the issue could be and keep track on failure issues — periodically review failure and issues to optimize critical point (that might indicate overload of systems and report it)
Finally, one of the best ways to ensure your algorithm is fault-tolerant is to use testing, which means verifying the correctness and performance of your algorithm under different scenarios and conditions. For example, you can use unit testing, integration testing, or end-to-end testing to check the functionality and logic of your algorithm. You can also use stress testing, load testing, or chaos testing to simulate errors, failures, or extreme situations and observe how your algorithm reacts and recovers. Testing allows you to identify and fix bugs, improve the quality and reliability of your algorithm, and increase your confidence and trust in your algorithm.
TESTING and VALIDATION, most of the time are confused terms but are one of the essential pillars to check algorithm quality and robustness. We call the full data the Total Dataset, we extract the training (70%), the remaining 30% is divided into 20% test and 10% validation. While you can verify and check the test, eventually even inject test data into the training, the validation is kept as for a blind test. In case of very complex test and sensitive algorithms the best way is to ask a third party to create a validation test so it is kept blind but you are sure it is representative and well balanced. Keep this rule as rigid as possible.
A good way to guard against unexpected failures of your algorithm is to insist that every possible state that the algorithm may take is explicitly defined. A trivial example of this might be: never leave an element of a complex if-else statement unspecified. Thinking in this way -- about every possible eventuality of your algorithm *explicitly* as you are writing it -- is a good practice that can help you avoid unexpected faults that are not mitigated by the above considerations. It sounds trivial but not adhering to this approach causes problems all too often.