Nowadays distributed computing environments, large amounts of data are
generated from different resources with a high velocity, rendering the data
difficult to capture, manage, and process within existing relational databases.
Hadoop is a tool to store and process large datasets in a parallel manner
across a cluster of machines in a distributed environment. Hadoop brings many
benefits like flexibility, scalability, and high fault tolerance; however, it
faces some challenges in terms of data access time, I/O operation, and
duplicate computations resulting in extra overhead, resource wastage, and poor
performance. Many researchers have utilized caching mechanisms to tackle these
challenges. For example, they have presented approaches to improve data access
time, enhance data locality rate, remove repetitive calculations, reduce the
number of I/O operations, decrease the job execution time, and increase
resource efficiency. In the current study, we provide a comprehensive overview
of caching strategies to improve Hadoop performance. Additionally, a novel
classification is introduced based on cache utilization. Using this
classification, we analyze the impact on Hadoop performance and discuss the
advantages and disadvantages of each group. Finally, a novel hybrid approach
called Hybrid Intelligent Cache (HIC) that combines the benefits of two methods
from different groups, H-SVM-LRU and CLQLMRS, is presented. Experimental
results show that our hybrid method achieves an average improvement of 31.2% in
job execution time