A Genetic Algorithm-Based Energy-Efficient Container Placement Strategy in CaaS

Container placement (CP) is a nontrivial problem in Container as a Service (CaaS). Many works in the literature solve it by using linear server energy-consumption models. However, the solutions of using a linear model makes different CPs indistinguishable with regard to energy consumption in a homogeneous host environment that has a same amount of active hosts. As such, these solutions are energy inefficient. In this paper, we demonstrate that an energy-saving gain can be achieved by optimizing the placement of containers under a nonlinear energy consumption model. Specifically, we leverage a strategy based on genetic algorithm (GA) to search the optimal solution. Unfortunately, the conventional GA incurs performance degradation when the virtual machine (VM) resource utilization is high. In order to solve this problem, we propose an improved genetic algorithm called IGA for efficiently searching the optimal CP solution by introducing two different exchange mutation operations and constructing a function as the control parameter to selectively control the usage of the two operations. Extensive experiments are carried out under different settings, and their results show that our strategy is better than the existing CP strategies, i.e., spread and binpack, on energy efficiency target. In addition, the introduced IGA is experimentally proved to be more effective compared with the First Fit, Particle Swarm Optimization (PSO) algorithm and conventional GA. Moreover, the results validate that our proposed strategy can search new CP solutions with better fitness and alleviate the performance degradation caused by the conventional GA when the VM resource utilization is high.