  Cache replacement algorithms are used to optimize the time taken by processor
to process the information by storing the information needed by processor at
that time and possibly in future so that if processor needs that information,
it can be provided immediately. There are a number of techniques (LIFO, FIFO,
LRU, MRU, Hybrid) used to organize information in such a way that processor
remains busy almost all the time. But there are some limitations of every
technique. We tried to overcome those limitations. We used Probabilistic
Graphical Model(PGM), which gives conditional dependency between random
variables using directed or undirected graph. In our research, we exploited the
Bayesian network technique to predict the future request by processor. The main
goal of the research was to increase the cache hit rate but not by increasing
the size of cache and also reducing or maintaining the overhead. We achieved 7%
more cache hits in best case scenario than those classical algorithms by using
PGM technique. This proves the success of our technique as far as cache hits
are concerned. Also, pre-eviction proves to be a better technique to get more
cache hits. Combining both pre-eviction and pre-fetching using PGM gives us the
results which were intended to achieve as the sole purpose of this research.
