Evaluation of API Request Bundling and its Impact on Performance of Microservice Architectures

Malki, Amine El; Zdun, Uwe

doi:10.1109/scc53864.2021.00060

Cited by 5 publications

(9 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Request Bundle and Rate Limit have been defined as best practices or patterns [27], [28], [29], [30], and particularly in the context of Service-Level Agreements [31], [32]. Although these API patterns have been studied experimentally [5], [33], their combinations with other API patterns and techniques have not been the subject of any experimental study in the scientific literature. This represents a significant gap in understanding the impact of widely used API practices such as Rate Limit and Load Balancing and their combinations.…”

Section: Related Workmentioning

confidence: 99%

“…• Request Bundle [5] is a technique used on both client and server sides to reduce communication overhead caused by exchanging too many messages between both sides. This is usually achieved programmatically on both the API client and backend by adding up the data in those messages to form one block of data to be sent simultaneously.…”

Section: Introductionmentioning

confidence: 99%

“…We believe this is a realistic, large-scale experimental study (designed to fulfill the first methodological principle for reproducible performance evaluation in Cloud Computing [13]). Our benchmark is provided as open access 5 and can be used to run similar tests for other API patterns and combinations of API patterns and reproduce our study.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Combining API Patterns in Microservice Architectures: Performance and Reliability Analysis

Malki,

Zdun

2023

2023 IEEE International Conference on Web Services (ICWS)

Self Cite

View full text Add to dashboard Cite

There are many challenges in maintaining the desired quality of service levels in modern microservice and cloud applications. Numerous techniques and patterns, such as API Rate Limit, Load Balancing, and Request Bundle, have been suggested for API services and clients to improve quality properties related to performance and reliability. However, no study has measured the impact of these techniques and their combinations in a specific configuration, especially using a large distributed system workload setting. This paper experimentally studies the effects of combining the API Rate Limit, Load Balancing, and Request Bundle patterns based on a realistic, third-party microservice-based application deployed in a private cloud and on the Amazon Web Services cloud (AWS) using 130 different configurations. We have run each configuration 500 times in the private cloud, totaling more than 4500 hours of runtime, and 200 times on AWS, totaling more than 3900 hours of runtime. We developed regression models from the collected data to predict the performance and reliability impacts of combining such techniques and patterns. We found that the models provide acceptable prediction errors below 30% on the private cloud and AWS. Further, we found that the models work best in highly reliable environments like AWS. In addition to the concrete analyses provided in our work, we propose a general and largely automated method that can be followed iteratively to evaluate similar techniques and patterns for their quality properties.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Combining API Patterns in Microservice Architectures: Performance and Reliability Analysis

Malki,

Zdun

2023

2023 IEEE International Conference on Web Services (ICWS)

Self Cite

View full text Add to dashboard Cite

show abstract

“…Both projects were chosen because they provide a well-documented architecture and use many current technological standards and best practices. Despite not being real production systems, their maturity makes them a good alternative, also frequently used in other research studies [25][26][27].…”

Section: Case Studiesmentioning

confidence: 99%

Avoiding Excessive Data Exposure Through Microservice APIs

Genfer

Zdun

2022

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

Data transfer and exchange of information through APIs are essential for each microservice architecture. Since these transfers often include private or sensitive data, potential data leaks, either accidentally or through malicious attacks, provide a high-security risk. While there are different techniques, like using data encryption or authentication protocols to secure the data exchange, only a few strategies are known to reduce the damage when an actual data breach happens. Our work presents a novel approach to identifying the optimal amount of data attributes that need to be exchanged between APIs and minimizes the damage in case of a potential breach. Our method relies only on static source code analysis and easy-to-calculate architectural metrics, making it well suited to be used in continuous integration and deployment processes. We further verified and validated the feasibility of our approach by conducting two case studies on open-source microservice systems.

show abstract

“…Rate Limit and similar techniques have been introduced as best practices or patterns [2], [3], [4], [5], [20] and in the context of Service-Level Agreements [21], [22]. However, the effects of Rate Limit have not yet been studied empirically in the scientific literature.…”

Section: Related Workmentioning

confidence: 99%

Impact of API Rate Limit on Reliability of Microservices-Based Architectures

Malki

Zdun

Pautasso

2022

2022 IEEE International Conference on Service-Oriented System Engineering (SOSE)

View full text Add to dashboard Cite

Many API patterns and best practices have been developed around microservices-based architectures, such as Rate Limiting and Circuit Breaking, to increase quality properties such as reliability, availability, scalability, and performance. Even though estimates on such properties would be beneficial, especially during the early design of such architectures, the real impact of the patterns on these properties has not been rigorously studied yet. This paper focuses on API Rate Limit and its impact on reliability properties from the perspective of API clients. We present an analytical model that considers specific workload configurations and predefined rate limits and then accurately predicts the success and failure rates of the back-end services. The model also presents a method for adaptively fine-tuning rate limits. We performed two extensive data experiments to validate the model and measured Rate Limiting impacts, firstly on a private cloud to minimize latency and other biases, and secondly on the Google Cloud Platform to test our model in a realistic cloud environment. In both experiments, we observed a low percentage of prediction errors. Thus, we conclude that our model can provide distributed system engineers and architects with insights into an acceptable value for the rate limits to choose for a given workload. Very few works empirically studied the impact of Rate Limit or similar API-related patterns on reliability.

show abstract

Evaluation of API Request Bundling and its Impact on Performance of Microservice Architectures

Cited by 5 publications

References 9 publications

Combining API Patterns in Microservice Architectures: Performance and Reliability Analysis

Combining API Patterns in Microservice Architectures: Performance and Reliability Analysis

Avoiding Excessive Data Exposure Through Microservice APIs

Impact of API Rate Limit on Reliability of Microservices-Based Architectures

Contact Info

Product

Resources

About