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Synchronizing back with the database Because RowSets are just an extended form of ResultSet, you can make all the same changes to the underlying data source. How to get them back to the underlying database is an interesting problem, as it depends on what your RowSet represented in the first place was it just some offline version of the ResultSet, or was it used as a live JavaBean representation of the data, or was it used in some other fashion What you did in the first place determines how information gets back to the database. When acting as a JavaBean, the RowSet typically represents a live view of the underlying database just as the ResultSet does. Therefore, all the methods act in the same way. A call to updateRow() or deleteRow() will make those changes immediately. Note The definition of immediately is also influenced by the transaction handling of the connection. We look at this in more detail later in 23, but the actual results may not make it to the database until you call commit() on the Connection that this RowSet used to fill its information.



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OLLER = INACTIVE, engineon -> clearspeed -> ACTIVE), engineoff -> INACTIVE ~on->recordSpeed->enableControl->CR~ISING

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1871 Overheads Table 181 summarizes overheads in the FT-CORBA platform (columns three to ve) and the FA-CORBA platform (column six) Each row of the table corresponds to one of the six method calls Column two contains average roundtrip times for the different method calls when the client was calling them on the non-replicated server The results in the next columns are presented as average percentages Each average is computed as follows Consider the highlighted cell in Table 181 that contains the value 62% Here, the average roundtrip time value for method 3 on the non-replicated server was 65 ms The % means that the average roundtrip time in the replicated experiments was 65 + 062 65 = 105 ms This was the lowest roundtrip time measured within the 12 experiments.





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It is evident that explicit differencing arises when l = 0 and an implicit scheme, requiring an evaluation of variables at a future time step, arises when l = 1. The implicit scheme requires an iterative procedure for solution of the resulting partially discretised equations with a corresponding increase in computational cost. The advantage, however, is that this scheme is unconditionally stable. The above time-differenced forms of the vapour pressure variable can be expressed in derivative form as follows: Dpv (n - 1) = Dt Dpv (n) = Dt d p (n - 1) = Dtpv (n - 1) dt v (3.5)

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The highlighted cell shows that the average overhead when calling Method 3 on the server, while using cold passive replication style, ranged between of 62 and 163% for group sizes of two, three, four, or eight replicas and checkpointing intervals 1 s, 5 s, or 10 s In particular, 62% corresponds to a group size of three and checkpointing interval of 10 s, while 163% corresponds to a group size of eight and checkpointing interval of 1 s We observed during our experiments that the group size slightly in uenced the overheads in warm passive replication In cold passive replication, the group size had almost no effect on the overheads The variation in overhead for this replication style is mainly given by the different checkpointing intervals For lower values of the checkpointing interval, the overhead was slightly higher, due to higher degree of interference between method calls and state checkpointing.

The BCH bound is the proof that the constructiveprocedure described aboveproduces codes with at least the specified minimum distance. We begin by constructing a parity check matrix for the code. Let c ( x ) = rn(x)g(x) be a code polynomial. Then, for i = b , b 1 , . . . , b 2t - 1 ,

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Another interesting observation is that the overhead percentages are not much different between cold and warm For active replication, on the other hand, the overheads are large, and highly affected by group size The large variations present in Table 181 are due to variation over the number of replicas The least value in the range corresponds to a group size of two replicas, while the largest to a group size of eight replicas The FA-CORBA overheads were also in uenced by the group size, but also by the chosen pruning interval Both parameters in uence the average time taken for execution of the consensus primitive which is part of the overhead, in the following ways:.

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