Self-adaptive trade-off decision making for autoscaling cloud-based services

Chen, T. ORCID: 0000-0001-5025-5472 and Bahsoon, R., 2017. Self-adaptive trade-off decision making for autoscaling cloud-based services. IEEE Transactions on Services Computing, 10 (4), pp. 618-632. ISSN 1939-1374

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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.

Item Type: Journal article
Publication Title: IEEE Transactions on Services Computing
Creators: Chen, T. and Bahsoon, R.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: July 2017
Volume: 10
Number: 4
ISSN: 1939-1374
Divisions: Schools > School of Science and Technology
Record created by: Linda Sullivan
Date Added: 29 Jan 2018 08:58
Last Modified: 29 Jan 2018 09:45

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