Energy-Aware Dynamic Resource Allocation in Container-Based Clouds via Cooperative Coevolution Genetic Programming

Chen Wang, Hui Ma, Gang Chen, Victoria Huang, Yongbo Yu, Kameron Christopher

Abstract: As a scalable and lightweight infrastructure technology, containers are quickly gaining popularity in cloud data centers. However, dynamic Resource-Allocation in Container-based clouds (RAC) is challenging due to two interdependent allocation sub-problems, allocating dynamic arriving containers to appropriate Virtual Machines (VMs) and allocating VMs to multiple Physical Machines (PMs). Most of existing research works assume homogeneous PMs and rely on simple and manually designed heuristics such as Best Fit and First Fit, which can only capture limited information, affecting their effectiveness of reducing energy consumption in data centers. In this work, we propose a novel hybrid Cooperative Coevolution Genetic Programming (CCGP) hyper-heuristic approach to automatically generate heuristics that are effective in solving the dynamic RAC problem. Different from existing works, our approach hybridizes Best Fit to automatically designed heuristics to coherently solve the two interdependent sub-problems. Moreover, we introduce a new energy model that accurately captures the energy consumption in a more realistic setting than that in the literature, e.g., real-world workload patterns and heterogeneous PMs. The experiment results show that our approach can significantly reduce energy consumption, in comparison to two state-of-the-art methods.