If the payment amount is an overpayment, then a new detailed ledger entry is posted to the payment entry so that no remaining amount is left on the payment entry. A detailed ledger entry is posted to the payment entry so that no remaining amount is left on the applied invoice entry. If the payment amount is an underpayment, then the outstanding amount is fully closed by the underpayment. If the payment tolerance is met, then the payment amount is analyzed. You can use payment tolerances so that every outstanding amount has a set maximum allowed payment tolerance. You can set up a payment discount tolerance to grant a payment discount after the payment discount date has passed. For example, payment tolerances are typically for small amounts that would cost more to correct than to just accept. The paper also presents a perceptive discussion of the findings derived from previous studies and proposes a list of future directions to address the fault tolerance challenges.You can set up a payment tolerance to close an invoice when the payment does not fully cover the amount on the invoice. The fault tolerance solutions applied by previous studies intended to address the identified challenges are reviewed. This study aims to provide a consistent understanding of fault tolerance in big data systems and highlights common challenges that hinder the improvement in fault tolerance efficiency.
Achieving an efficient fault tolerance solution in a big data system is challenging because fault tolerance must meet some constraints related to the system performance and resource consumption. Fault tolerance is the main property of such systems because it maintains availability, reliability, and constant performance during faults.
However, big data systems are composed of large-scale hardware resources that make their subspecies easily fail. Big data systems are sufficiently stable to store and process a massive volume of rapidly changing data.
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