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A container-based cloud is a new trend in cloud computing that introduces more granular management of cloud resources. Compared with VM-based clouds, container-based clouds can further improve energy efficiency with a finer granularity resource allocation in data centers. The current allocation approaches for VM-based clouds cannot be used in container-based clouds. The first reason is that existing research lacks appropriate models that can represent the interaction of allocation features. Many critical features, such as VM overhead, are also not considered in the current models. The second reason is that current allocation approaches do not perform well to the three frequently encountered allocation scenarios, offline allocation, on-line allocation, and multi-objective allocation. Current approaches for these scenarios are mostly based on greedy heuristics that can be easily stuck at local optimum, or meta-heuristics that consider a simplified one-level allocation problem. Evolutionary Computation (EC) is particularly good at solving combinatorial optimization problems for both off-line and on-line scenarios. The overall goal of this thesis is to propose an EC approach to the three allocation scenarios in order to improve the performance of container-based clouds. Specifically, we aim to optimize energy consumption in all the scenarios. An additional objective, availability of the application, is considered for the multi-objective scenario. First, this thesis investigates two promising representations, vector-based and group-based. We propose two novel vector-based (e.g., Single- chromosome Genetic Algorithm (SGA) and Dual-chromosome GA (DGA)) and a group-based GA approaches for the off-line allocation scenario. Cor- responding genetic operators and decoding processes are also developed and evaluated. Two contributions have been made. Firstly, a novel offline model has been proposed based on current models with additional features. It can be used to evaluate allocation algorithms. Secondly, two types of problem representation, vector-based and group-based, are investigated and three novel approaches are proposed. The three approaches are compared with state-of-the-art approaches. The results show that all solutions produced by these approaches are better than the state-of-the-art approaches and group-based GA is the best approach. Second, this thesis proposes a novel genetic programming hyper-heuristic (GPHH) and a cooperative coevolution (CCGP)-based approach for the on-line allocation scenario. These hyper-heuristic methods can automatically generate allocation rules. For the GPHH-based approach, we develop a novel training procedure to generate reservation-based rules for allocating containers to VM instances. For the CCGP-based approach, we introduce a new terminal set and develop a training procedure to generate allocation rules for two-level allocations. We analyze both human-designed rules and generated rules to provide insights for algorithm designers. Two contributions have been proposed for the on-line problem. First, the on-line model for the on-line allocation scenario is developed. Second, a novel terminal set and training procedures are developed. The automatically generated heuristics perform significantly better than the manually designed heuristics. Third, this thesis proposes a multi-objective approach that generates a set of trade-off solutions for the cloud providers to choose from. Our novel approach, namely Nondominated Sorting-Group GA (NS-GGA), combines the group-based representation and the NSGA-II framework. The experimental results are compared with existing approaches. The results show that our proposed NS-GGA approach outperforms all other approaches. We propose two novelties. The first novelty is the multi-objective model including objectives of energy consumption and availability. The second novelty is the NS-GGA approach that combines the group-based representation with NSGA-II. The allocation solutions found by NS-GGA dominate the solutions found by other existing approaches.

An Evolutionary Computation Approach to Resource Allocation in Container-based Clouds