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

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 percent improvement. GA-Par is roughly 4× faster than a Hadoop-based genetic algorithm solver and the effectiveness can be constantly guaranteed under different application scales

Using Microservices to Customize Multi-Tenant SaaS: From Intrusive to Non-Intrusive

Customization is a widely adopted practice on enterprise software applications such as Enterprise resource planning (ERP) or Customer relation management (CRM). Software vendors deploy their enterprise software product on the premises of a customer, which is then often customized for different specific needs of the customer. When enterprise applications are moving to the cloud as mutli-tenant Software-as-a-Service (SaaS), the traditional way of on-premises customization faces new challenges because a customer no longer has an exclusive control to the application. To empower businesses with specific requirements on top of the shared standard SaaS, vendors need a novel approach to support the customization on the multi-tenant SaaS. In this paper, we summarize our two approaches for customizing multi-tenant SaaS using microservices: intrusive and non-intrusive. The paper clarifies the key concepts related to the problem of multi-tenant customization, and describes a design with a reference architecture and high-level principles. We also discuss the key technical challenges and the feasible solutions to implement this architecture. Our microservice-based customization solution is promising to meet the general customization requirements, and achieves a balance between isolation, assimilation and economy of scale

Microservices Validation: Methodology and Implementation

Due to the wide spread of cloud computing, arises actual question about architecture, design and implementation of cloud applications. The microservice model describes the design and development of loosely coupled cloud applications when computing resources are provided on the basis of automated IaaS and PaaS cloud platforms. Such applications consist of hundreds and thousands of service instances, so automated validation and testing of cloud applications developed on the basis of microservice model is a pressing issue. There are constantly developing new methods of testing both individual microservices and cloud applications at a whole. This article presents our vision of a framework for the validation of the microservice cloud applications, providing an integrated approach for the implementation of various testing methods of such applications, from basic unit tests to continuous stability testing