Research On Models And Algorithms For Energy-Aware Optimization Problems Under Cloud Computing Environment

With the constant expansion of data centers, huge energy consumption and limited band-width have become one of the main factors restricting the development of cloud computing. Forthe former, improving servers’ energy efficiency is one of the effective ways to reduce energyconsumption of data centers. Thus one of the main issue of this paper is to study how to im-prove servers’ energy efficiency through appropriate scheduling strategies. For the latter, sincebandwidth is a very limited and valuable resource in a data center, how to save bandwidth byreasonable scheduling strategies is another main issue of this paper.The main objective of this thesis is to design appropriate scheduling strategies to solve twoof the most prominent issues of cloud computing—huge energy consumption and limited band-width. That is to say, adjusting data deployment and task allocation among servers by effectivescheduling strategies, so as to maximize the energy efficiency of servers and the data localityratio of tasks. Thereby, the total energy consumption and bandwidth usage could be reduced.Based on these, we built the three sets of optimization models and design their correspondingalgorithms for the energy consumption and bandwidth problems under cloud computing. Themain contributions are as follows:1. Build an energy-aware large-scale task-scheduling model and design a global optimiza-tion genetic algorithm to solve the model. The proposed model adjust the CPU utilizationof servers through reasonable task-scheduling strategies, so as to improve the energy effi-ciency of servers. Meanwhile, by ensuring100%data locality ratio of tasks, it improvesthe execution efficiency of tasks and saves the bandwidth usage of data centers.2. Build a data deployment and task allocation combined energy-aware large-scale modeland design a bi-level genetic algorithm to solve the model. The proposed model adjustthe resource utilizations of servers, including the CPU and Disk utilization, by combiningdata deployment policies and task-scheduling strategies, so as to improve the energy effi-ciency of servers. Meanwhile, by ensuring100%data locality ratio of tasks, it improvesthe execution efficiency of tasks and saves the bandwidth usage of data centers.3. Build a data deployment and task allocation combined multi-objective model and designa multi-objective genetic algorithm based on MOEA/D to solve the model. The proposedmodel provide a set of optional data deployment policies and task-scheduling strategiesto the decision makers, so as to get the maximum energy efficiency of servers under theprerequisite of meeting the current demand for bandwidth.4. As tasks involved in cloud computing are usually tens of thousands, the proposed mod-els are all large-scale optimization ones. In order to speed up the solving process of the proposed models and accelerate convergent speed of the proposed algorithm, local searchoperators is introduced in genetic algorithms. Finally, numerical experiments are madeand the results indicate that the proposed models are reasonable and their correspondingalgorithms can effectively improve the energy efficiency of servers and save the band-width usage of data centers.