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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 both service 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 Genetic Algorithm framework based on Spark to accelerate the operations for calculating the optimal or near-optimal solution. Large-scale real world 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.

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The significant increase in the use of cloud computing, has led to an interest in partitioning applications over a set of public and private clouds in order to meet a range of non-functional requirements including performance (for example where private cloud resources alone are insufficient), dependability (e.g. to allow the application to continue to operate even if one cloud fails) and security (for example to ensure that sensitive data is restricted to sufficiently secure clouds and networks). This paper describes a novel deployment planning algorithm to partition complex workflow-based applications over federated clouds, while meeting security requirements. The security issues are based on our previous work which extends the Bell-LaPadula model to encompass cloud computing. Selecting the cheapest option for partitioning a workflow over a set of resources has been shown to be an NP-hard problem, which can take impractically long for partitioning large workflows over multiple clouds. We therefore introduce a novel adaptive partitioning algorithm to handle these large workflow applications, which significantly reduces the time required to choose a sufficientlygood partitioning option. This is based on generating an initial partitioning, and then adapting it to see if a better solution can be found by bringing together on the same node services with significant communication costs. The algorithm has been implemented and evaluated by using both randomly generated and real world scientific workflows. The experiment results show that our algorithm is thousands times quicker than the exhaustive algorithm presented in our previous work. Yet, on average it generates only 25% more costly solutions. We also compared this algorithm with two other methods commonly used to partition workflows over a set of clouds.