Microservice Orchestration Strategies for Containerized Cloud Environments

Abstract

The explosion in the popularity of the Internet paralleled with the impetuous evolution of computing and storage technologies has brought about a revolutionary shift in the way computational resources are provisioned. The Cloud computing paradigm facilitates the lease of computational resources as services on a pay-per-use basis. Cloud developers have rapidly embraced the Microservice Architecture to accelerate the development and deployment of Cloud applications. However, the dynamism, agility and distributed characteristics of microservices pose significant challenges in the resource orchestration of microservice-based Cloud environments. Effectively utilizing the distributed resources of the Cloud to obtain performance gains is an issue of paramount importance. Hence, this work focusses on the orchestrational challenges in microservicebased Cloud environments. In order to achieve the desired level of scalability and elasticity, microservice-based Cloud applications are typically packaged in containers. Therefore, microservice orchestration strategies for Cloud environments must effectively manage container clusters to automate processes such as resource allocation, autoscaling and load balancing. In terms of system performance, a key concern is the initial placement of the microservice applications. Placing microservice applications without considering the interactions among the microservices forming an application results in a performance penalty. Accordingly, an interaction-aware microservice placement strategy, called Interactionaware Microservice Allocation (IntMA) that preserves the Quality of Service and maintains resource efficiency, is devised in this research. The interaction pattern is modeled using a doubly weighted interaction graph, which is then used to assign the incoming microservice applications to appropriate nodes in the Cloud datacenter. Experiments on the Google Cloud Platform substantiated that our proposed approach attains better objective values than the existing placement policies. The dynamism of microservice-based Cloud environments renders it essential to revisit the initial placement decisions and perform rescheduling. Rescheduling strategies must strive to resolve degradations in the performance due to fluctuations in the workload. Existing rescheduling strategies, tailored for hypervisor-based virtualization environments, do not consider the features specific to containers. Therefore, this research work also explores the impact of container configuration parameters on microservice application performance. The experiments revealed that larger values for container CPU throttling led to higher response times. In order to circumvent this, a Throttling and Interaction-aware Anticorrelated Rescheduling Framework (TIARM) for microservices, is proposed. Experimental results elucidate the efficacy of the proposed approach in enhancing the performance of containerized Cloud environments.