A Cooperative Coevolution Genetic Programming Hyper-Heuristics Approach for On-Line Resource Allocation in Container-Based Clouds
Boxiong Tan, Hui Ma, Yi Mei, Mengjie Zhang
Abstract
Containers are lightweight and provide the potential to reduce more energy consumption of data centers than Virtual Machines (VMs) in container-based clouds. On-line resource allocation is the most common operation in clouds. However, the on-line <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Resource Allocation in Container-based clouds (RAC)</i> is new and challenging because of its two-level architecture, i.e., the allocations of containers to VMs and the allocation of VMs to physical machines. These two allocations interact with each other, and hence cannot be made separately. Since on-line container allocation requires a real-time response, most current allocation techniques rely on heuristics (e.g., First Fit and Best Fit), which do not consider the comprehensive information such as workload patterns and VM types. As a result, resources are not used efficiently and the energy consumption is not sufficiently optimized. We first propose a novel model of the on-line <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RAC</i> problem with the consideration of VM overheads, VM types and an affinity constraint. Then, we design a Cooperative Coevolution Genetic Programming (CCGP) hyper-heuristic approach to solve the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RAC</i> problem, named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CCGP-RAC</i> . <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CCGP-RAC</i> can learn the workload patterns and VM types from historical workload traces and generate allocation rules. The experiments show significant improvement in energy consumption compared to the state-of-the-art algorithms.