Multi-Objective Query Optimization for Mobile- Cloud Database Environment

Abstract

Mobile-cloud computing is one of the most prominent infrastructures of mobile technologies of
the future, as it accumulates the advantages of both mobile computing and cloud computing,
providing optimized services to users. In a database system distributed in the cloud, connections
necessary for a query plan may be stored on multiple sites, which exponentially increases the
number of possible equivalent plans in the search for an optimum query execution plan.
However, a thorough search of all possible plans is not computationally logical in such a large
search space. In this study, we aim to identify a cost model including a multi-objective function
with diverse (and possibly contradictory) QoS parameters to solve the query optimization
problem in heterogeneous (in terms of pricing models) and mobile cloud databases. Then, we
propose a novel strategy to optimize queries in such environments using the Teaching-
Learning-Based Optimization (TLBO) algorithm. Finally, the obtained results are evaluated in the
CloudSim environment and compared with genetic optimization and Ant Colony Optimization
(ACO).