  Variant Stochastic cracking is a significantly more resilient approach to
adaptive indexing. It showed [1]that Stochastic cracking uses each query as a
hint on how to reorganize data, but not blindly so; it gains resilience and
avoids performance bottlenecks by deliberately applying certain arbitrary
choices in its decision making. Therefore bring, adaptive indexing forward to a
mature formulation that confers the workload-robustness that previous
approaches lacked. Original cracking relies on the randomness of the workloads
to converge well. [2][3] However, where the workload is non-random, cracking
needs to introduce randomness on its own. Stochastic Cracking clearly improves
over original cracking by being robust in workload changes while maintaining
all original cracking features when it comes to adaptation. But looking at both
types of cracking, it conveyed an incomplete picture as at some point of time
it is must to know whether the workload is random or sequential. In this paper
our focus is on optimization of variant stochastic cracking, that could be
achieved in two ways either by reducing the initialization cost to make
stochastic cracking even more transparent to the user, especially for queries
that initiate a workload change and hence incur a higher cost or by combining
the strengths of the various stochastic cracking algorithms via a dynamic
component that decides which algorithm to choose for a query on the fly. The
efforts have been put in to make an algorithm that reduces the initialization
cost by using the main notion of both cracking, while considering the
requirements of adaptive indexing [2].

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