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
