Research on load balancing technology for microservice architecture

Wang, Hao; Wang, Yong; Liang, Guanying; Gao, Yunfan; Gao, Weizhen; Zhang, Wenping

doi:10.1051/matecconf/202133608002

Cited by 7 publications

(5 citation statements)

References 5 publications

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“…Then the corresponding service instances are accessed via the corrsponding address with the help of load balancing algorithm. The Ribbon load balancer utilizes techniques like polling, randomization and other methods to ensure the load balancing [15]. At present, it is one of the most efficient load balancing techniques existing and the proposed method is compared against this method.…”

Section: Implementation Detailsmentioning

confidence: 99%

Latency Minimization Using an Adaptive Load Balancing Technique in Microservices Applications

Selvakumar¹,

Jayashree²,

Arumugam³

2023

Computer Systems Science and Engineering

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Advancements in cloud computing and virtualization technologies have revolutionized Enterprise Application Development with innovative ways to design and develop complex systems. Microservices Architecture is one of the recent techniques in which Enterprise Systems can be developed as fine-grained smaller components and deployed independently. This methodology brings numerous benefits like scalability, resilience, flexibility in development, faster time to market, etc. and the advantages; Microservices bring some challenges too. Multiple microservices need to be invoked one by one as a chain. In most applications, more than one chain of microservices runs in parallel to complete a particular requirement To complete a user's request. It results in competition for resources and the need for more inter-service communication among the services, which increases the overall latency of the application. A new approach has been proposed in this paper to handle a complex chain of microservices and reduce the latency of user requests. A machine learning technique is followed to predict the weighting time of different types of requests. The communication time among services distributed among different physical machines are estimated based on that and obtained insights are applied to an algorithm to calculate their priorities dynamically and select suitable service instances to minimize the latency based on the shortest queue waiting time. Experiments were done for both interactive as well as non interactive workloads to test the effectiveness of the solution. The approach has been proved to be very effective in reducing latency in the case of long service chains.

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Section: Implementation Detailsmentioning

confidence: 99%

Latency Minimization Using an Adaptive Load Balancing Technique in Microservices Applications

Selvakumar¹,

Jayashree²,

Arumugam³

2023

Computer Systems Science and Engineering

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“…Many studies have proposed solutions to improve load balancing efficiency in microservices architecture and their future directions [19]. Yipei et al [20] argue that existing loadbalancing strategies lack optimization and suffer from network congestion and complexity.…”

Section: Related Workmentioning

confidence: 99%

“…We have realized data analysis using R language 19 . Tables III and IV list the combined models that we generated from the data collection for total time prediction on the private cloud and AWS, respectively.…”

Section: A Data Collection and Analysismentioning

confidence: 99%

Combining API Patterns in Microservice Architectures: Performance and Reliability Analysis

Malki,

Zdun

2023

2023 IEEE International Conference on Web Services (ICWS)

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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.

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“…Lastly, the NELB includes management and monitoring components that enable administrators to configure and manage the load balancer while providing insights into system health and performance. NELB effectively supports various protocols, including HTTP, UDP, and TCP [20].…”

Section: Management and Monitoringmentioning

confidence: 99%

Light Weight Native Edge Load Balancers for Edge Load Balancing

Kumar¹,

Rajagopalan

P.³

2023

Green Intell. Syst. Appl.

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Edge computing has become an essential aspect of modern computing systems. Edge computing involves processing data at the edge of the network, closer to where the data is generated. The ability to process data in real-time at the edge provides various benefits such as lower latency, improved response times, and reduced network congestion. Load balancing is a critical component of edge computing, which distributes the workload across multiple edge devices, ensuring that the workload is evenly distributed. This paper discusses current trends in edge computing load balancing techniques, including static, dynamic, and hybrid load balancing approaches.

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Research on load balancing technology for microservice architecture

Cited by 7 publications

References 5 publications

Latency Minimization Using an Adaptive Load Balancing Technique in Microservices Applications

Latency Minimization Using an Adaptive Load Balancing Technique in Microservices Applications

Combining API Patterns in Microservice Architectures: Performance and Reliability Analysis

Light Weight Native Edge Load Balancers for Edge Load Balancing

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