An Analysis of Throughput and Latency Behaviours Under Microservice Decomposition

Jayasinghe, Malith; Chathurangani, Jayathma; Kuruppu, Gayal; Tennage, Pasindu; Perera, Srinath

doi:10.1007/978-3-030-50578-3_5

Cited by 5 publications

(3 citation statements)

References 13 publications

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“…• orchestration of complex services (the complexity of microservices applications is pushed from the components to the integration level [15], [23]); • maintaining data consistency and transaction management across microservices [15], [19], [32], [33]; • the difficulty in understanding the system holistically [7], [27]; • increased consumption of computing resources [7], [11], [17]. For small scale systems, these challenges may outweigh the benefits [27].…”

Section: A Motivation and Problem Statementmentioning

confidence: 99%

“…We narrowed the performance assessment to measuring the throughput. Although throughput is the predominant performance metric, other metrics, such as response time (latency) and CPU utilization, have also been used in prior studies [4], [14], [17], [18], [42], [70]. Our motivation was that throughput had also been commonly used as the scalability measure [18], while in this study, we were also interested in comparing the scalability of both architectural styles.…”

Section: A Construct Validitymentioning

confidence: 99%

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Monolithic vs. Microservice Architecture: A Performance and Scalability Evaluation

2022

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Context. Since its proclamation in 2012, microservices-based architecture has gained widespread popularity due to its advantages, such as improved availability, fault tolerance, and horizontal scalability, as well as greater software development agility. Motivation. Yet, refactoring a monolith to microservices by smaller businesses and expecting that the migration will bring benefits similar to those reported by top global companies, such as Netflix, Amazon, eBay, and Uber, might be an illusion. Indeed, for systems that do not have thousands of concurrent users and can be scaled vertically, the benefits of such migration have not been sufficiently investigated, while the existing evidence is inconsistent. Objective. The purpose of this paper is to compare the performance and scalability of monolithic and microservice architectures on a reference web application. Method. The application was implemented in four different versions, covering not only two different architectural styles (monolith vs. microservices) but also two different implementation technologies (Java vs. C# .NET). Next, we conducted a series of controlled experiments in three different deployment environments (local, Azure Spring Cloud, and Azure App Service). Findings. The key lessons learned are as follows: (1) on a single machine, a monolith performs better than its microservice-based counterpart; (2) The Java platform makes better use of powerful machines in case of computation-intensive services when compared to .NET; the technology platform effect is reversed when non-computationally intensive services are run on machines with low computational capacity; (3) vertical scaling is more cost-effective than horizontal scaling in the Azure cloud; (4) scaling out beyond a certain number of instances degrades the application performance; (5) implementation technology (either Java or C# .NET) does not have a noticeable impact on the scalability performance.

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Section: A Motivation and Problem Statementmentioning

confidence: 99%

Section: A Construct Validitymentioning

confidence: 99%

Monolithic vs. Microservice Architecture: A Performance and Scalability Evaluation

2022

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“…μ Tune 6 designs auto‐tuned threading method to interact with RPC interfaces. As pointed by Jayasinghe's analysis, 27 the number of service calls that impact the performance has not been considered, which also need the support of workflow characterization.…”

Section: Related Workmentioning

confidence: 99%

Characterizing and synthesizing the workflow structure of microservices in ByteDance Cloud

Wen

Cheng

Yin

2022

J Software Evolu Process

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Modern Cloud applications have evolved from monolithic systems to numerous distributed microservices whose workflows interact via Remote Procedure Calls (RPCs). The benchmarks focusing on individual microservice components are insufficient because the reference relationships between components, which form the microservice workflow structure, are another critical aspect of microservice applications. Unfortunately, understanding the characteristics of microservice workflow in the production Cloud remains a missing piece in the literature, which prevents the representative of microservice benchmarks. In this paper, we fill this gap by characterizing and synthesizing the microservice workflows based on the trace data of the Toutiao application that is running on ByteDance Cloud. We examine the microservice workflows starting from DAG graphs, introduce observed properties that are easy to ignore but important, show the artificiality of the workflow by statistic description, and explore the high cost of network overhead. We further synthesize the workflow following the characteristics observed. The extensive evaluations show that the synthesized microservice workflows have consistent statistical characteristics as the production ones. A case study applying the synthesized workflows proves its usability.

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Microservice Workflow Modeling for Affinity Scheduling to Improve the QoS

Wen

Cheng

Deng

et al. 2023

Lecture Notes in Computer Science

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An Analysis of Throughput and Latency Behaviours Under Microservice Decomposition

Cited by 5 publications

References 13 publications

Monolithic vs. Microservice Architecture: A Performance and Scalability Evaluation

Monolithic vs. Microservice Architecture: A Performance and Scalability Evaluation

Characterizing and synthesizing the workflow structure of microservices in ByteDance Cloud

Microservice Workflow Modeling for Affinity Scheduling to Improve the QoS

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