%0 Journal Article %A Chen, Tao %A Bahsoon, Rami %D 2019 %T Self-adaptive trade-off decision making for autoscaling cloud-based services %U https://repository.lboro.ac.uk/articles/journal_contribution/Self-adaptive_trade-off_decision_making_for_autoscaling_cloud-based_services/9876311 %2 https://repository.lboro.ac.uk/ndownloader/files/18113105 %K Uncategorised value %K Search-based optimization %K Multi-objective trade-offs %K QoS interference %K Cloud computing %K Information Systems %K Computer Software %K Distributed Computing %X Elasticity in the cloud is often achieved by on-demand autoscaling. In such context, the goal is to optimize the Quality of Service (QoS) and cost objectives for the cloud-based services. However, the difficulty lies in the facts that these objectives, e.g., throughput and cost, can be naturally conflicted; and the QoS of cloud-based services often interfere due to the shared infrastructure in cloud. Consequently, dynamic and effective trade-off decision making of autoscaling in the cloud is necessary, yet challenging. In particular, it is even harder to achieve well-compromised trade-offs, where the decision largely improves the majority of the objectives; while causing relatively small degradations to others. In this paper, we present a self-adaptive decision making approach for autoscaling in the cloud. It is capable to adaptively produce autoscaling decisions that lead to well-compromised trade-offs without heavy human intervention. We leverage on ant colony inspired multi-objective optimization for searching and optimizing the trade-offs decisions, the result is then filtered by compromise-dominance, a mechanism that extracts the decisions with balanced improvements in the trade-offs. We experimentally compare our approach to four state-of-the-arts autoscaling approaches: rule, heuristic, randomized and multi-objective genetic algorithm based solutions. The results reveal the effectiveness of our approach over the others, including better quality of trade-offs and significantly smaller violation of the requirements. %I Loughborough University