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GA-Par: Dependable Microservice Orchestration Framework for Geo-Distributed Clouds

Lookup NU author(s): Dr Zhenyu Wen, Professor Raj Ranjan, Professor Alexander RomanovskyORCiD, Dr Jiawei Xu



This is the authors' accepted manuscript of an article that has been published in its final definitive form by IEEE, 2020.

For re-use rights please refer to the publisher's terms and conditions.


Recent advances in composing Cloud applications have been driven by deployments of inter-networking heterogeneous microservices across multiple Cloud datacenters. System dependability has been of the upmost importance and criticality to bot hservice vendors and customers. Security, a measurable attribute, is increasingly regarded as the representative example of dependability. Literally, with the increment of microservice types and dynamicity, applications are exposed to aggravated internal security threats and externally environmental uncertainties. Existing work mainly focuses on the QoS-aware composition of native VM-based Cloud application components, while ignoring uncertainties and security risks among interactive and interdependent container-based microservices. Still, orchestrating a set of microservices across datacenters under those constraints remains computationally intractable. This paper describes a new dependable microservice orchestration framework GA-Par to effectively select and deploy microservices whilst reducing the discrepancy between user security requirements and actual service provision. We adopt a hybrid (both whitebox and blackbox based) approach to measure the satisfaction of security requirement and the environmental impact of network QoS on system dependability. Due to the exponential grow of solution space, we develop a parallel GeneticAlgorithm framework based on Spark to accelerate the operations for calculating the optimal or near-optimal solution. Large-scale realworld datasets are utilized to validate models and orchestration approach. Experiments show that our solution outperforms the greedy-based security aware method with 42.34% improvement. GA-Par is roughly 4x faster than a Hadoop-based genetic algorithm solver and the effectiveness can be constantly guaranteed under different application scales.

Publication metadata

Author(s): Wen Z, Lin T, Yang R, Ji S, Ranjan R, Romanovsky A, Lin C, Xu J

Publication type: Article

Publication status: Published

Journal: IEEE Transactions on Parallel and Distributed Systems

Year: 2020

Volume: 31

Issue: 1

Pages: 29-143

Print publication date: 01/01/2020

Online publication date: 19/07/2019

Acceptance date: 15/07/2019

Date deposited: 16/07/2019

ISSN (print): 1045-9219

ISSN (electronic): 1558-2183

Publisher: IEEE


DOI: 10.1109/TPDS.2019.2929389


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