Three problems with an agreement of interest are as follows. Randomized consensus algorithms can bypass the result of FLP impossibility by achieving security and vitality with overwhelming probability, even in the most pessimistic planning scenarios such as a smart denial of service attacker on the network.  Another known approach is called MSR-type algorithms widely used by computers to control theory.    Consensus algorithms traditionally assume that the set of nodes participating in nods is fixed and indicated at the beginning: that is, a pre-configuration process (manual or automatic) has authorized a well-known group of participants who can authenticate each other as members of the group. In the absence of such a well-defined and closed group with authenticated members, a Sybil attack on an open consensus group can even defeat a Byzantine consensus algorithm by simply creating enough virtual participants to exceed the error tolerance threshold. To solve the problem of consensus in a shared storage system, simultaneous objects must be introduced. A simultaneous or shared object is a data structure that helps simultaneous processes reach an agreement. The problem of consensus requires agreement between a number of processes (or agents) for a data value. Some of the processes (agents) may fail or not be reliable in another way, so consensual protocols must be tolerant or resilient. Processes must, in one way or another, set out their candidate values, communicate with each other and agree on a single consensual value. Some cryptocurrencies, such as Ripple, use a node validation system to validate the Ledger. This system used by Ripple, called Ripple Protocol Consensus Algorithm (RPCA), works in rounds: Step 1: Each server establishes a list of valid transactions; Step 2: Each server brings together all candidates from its single nodes list (UNL) and votes on their accuracy; Step 3: Transactions above the minimum threshold will move on to the next round; Step 4: The last round requires 80% agreement  A fundamental problem with distributed and multi-agent computing systems is to achieve the overall reliability of the system in the presence of a number of faulty processes. This often requires coordination of processes to reach consensus or to agree on a data value needed during the calculation.
Examples of consensual applications include agreement on which transactions to transfer to a database, on the order and order in which the computer must be copied and sent to the atom. Practical applications that often require consensus are cloud computing, measurement synchronization, PageRank, opinion formation, smart grids, state estimation, drone control (and multiple robots/agents in general), load compensation, blockchain and others.