Self-Adaptive Trade-off Decision Making for Autoscaling Cloud-Based Services

TL;DR

This paper introduces a self-adaptive autoscaling decision-making approach for cloud services that optimizes QoS and cost trade-offs using ant colony inspired multi-objective optimization, outperforming existing methods.

Contribution

It proposes a novel self-adaptive decision-making method leveraging ant colony optimization and compromise-dominance for balanced trade-offs in cloud autoscaling.

Findings

Outperforms rule, heuristic, randomized, and genetic algorithm approaches.

Achieves better trade-off quality with smaller requirement violations.

Effectively balances QoS and cost objectives in cloud autoscaling.

Abstract

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.