Performance Evaluation of Microservices Architectures Using Containers

Abstract

Abstract-Microservices architecture has started a new trend for application development for a number of reasons: (1) to reduce complexity by using tiny services; (2) to scale, remove and deploy parts of the system easily; (3) to improve flexibility to use different frameworks and tools; (4) to increase the overall scalability; and (5) to improve the resilience of the system. Containers have empowered the usage of microservices architectures by being lightweight, providing fast start-up times, and having a low overhead. Containers can be used to develop applications based on monolithic architectures where the whole system runs inside a single container or inside a microservices architecture where one or few processes run inside the containers. Two models can be used to implement a microservices architecture using containers: master-slave, or nested-container. The goal of this work is to compare the performance of CPU and network running benchmarks in the two aforementioned models of microservices architecture hence provide a benchmark analysis guidance for system designers.

Toward a New Massively Distributed Virtual Machine based Cloud Micro-Services Team Model for HPC: SPMD Applications

“…For example in [25] the authors presented a performance evaluation of microservices architectures using containers: master-slave and nested-container, and in [26] they discussed the benefit of implementing micro-services architecture for emerging the telecom application. Also, the micro-services approach is implemented to digital curation infrastructure by devolving function into a set of micro-services which grants the deployment flexibility and simplify of the development and the maintenance [27].…”

Open-source RANs in Practice: an Over-The-Air Deployment for 5G MEC

“…To realize virtualization on general-purpose hardware, containers have been reported to produce less operational overhead and better performance [6], [7] than virtual machines that are based on virtualized operating system layer instead of hardware layer [8].…”

“…DevOps utilizes continuous deployment (CD), continuous integration (CI), and continuous monitoring (CM). CD and CI enable on-demand deployment of software through automated mechanisms [6]. When the number of deployments increases, as with microservices, CD and CI become essential for frequent software releases.…”

High-Performance Elastic Management for Cloud Containers Based on Predictive Message Scheduling

“…It weakens the randomness of the original monitored data. The Grey model is established for transforming X (1) . The GM (1,1) model represents first-order, one-variable differential equation model to predict elastic expansion of container.…”

“…Containers can be started quickly, with little resource consumption. The combination of container and micro-service architectures provides an application-oriented lightweight infrastructure that can be extended by creating replicas of bottleneck components [1]. In container-based micro-service architectures, there are a great deal of log data, such as user behavior and system logs, which default to real-time consumption, and are not suitable for asynchronous processing; additionally, there exists the problem of message coupling and response delay [2].…”