An Adaptive Load-Balanced Partitioning Module in Cassandra Using Rendezvous Hashing

With the rapid growth and use of social networks, the appearance of Internet technology and the advent of the cloud computing a need for new tools and algorithms is appeared to handle the challenges of the big-data. One of the key advances in resolving the big-data challenges is to introduce scalable storage systems. NoSQL databases are considered as efficient big data storage management systems that provide horizontal scalability. To ensure scalability of the system, data partitioning strategies must be implemented in these databases. In this paper, an Adaptive Rendezvous Hashing Partitioning Module (ARHPM) is proposed for Cassandra NoSQL databases. The main goal of this module is to partition the data in Cassandra using rendezvous hashing with proposing a Load Balancing based Rendezvous Hashing (LBRH) algorithm for guaranteeing the load balancing in the partitioning process. To evaluate the proposed module, Cassandra is modified by embedding the APRHM partitioning module in it and a number of experiments are conducted to validate the load balancing of the proposed module by using the Yahoo Cloud Serving Benchmark.