An Energy and Data Locality Aware Bi-level Multiobjective Task Scheduling Model Based on MapReduce for Cloud Computing

Soaring power consumption of data centers has drawn increasing attentions. Reducing energy consumption will not only cut down the operational cost of data centers, but also reduce the amount of greenhouse gases emissions. From the perspective of optimizing energy efficiency of servers in a data center, and by taking data layout policies and the requirement of data locality for task execution, as well as the relationship between servers´ performance and energy consumption into consideration, a new bi-level multiobjective task scheduling model based on MapReduce is proposed first. To sole the problem efficiently, a tailor-made encoding and decoding methods are designed, then, two explicit objective functions, energy efficiency function and localized ratio function are defined. Based on all these, an improved bi-level multiobjective evolutionary algorithm based on MOEA/D is proposed to solve this model, in which a local search operator is introduced to accelerate its convergent speed and enhance its searching ability. Finally, simulation results show that the proposed algorithm is effective and efficient.