Network-Aware Reliability Modeling and Optimization for Microservice Placement

Optimizing microservice placement to enhance the reliability of services is crucial for improving the service level of microservice architecture-based mobile networks and Internet of Things (IoT) networks. Despite extensive research on service reliability, the impact of network load and routing on service reliability remains understudied, leading to suboptimal models and unsatisfactory performance. To address this issue, we propose a novel network-aware service reliability model that effectively captures the correlation between network state changes and reliability. Based on this model, we formulate the microservice placement problem as an integer nonlinear programming problem, aiming to maximize service reliability. Subsequently, a service reliability-aware placement (SRP) algorithm is proposed to solve the problem efficiently. To reduce bandwidth consumption, we further discuss the microservice placement problem with the shared backup path mechanism and propose a placement algorithm based on the SRP algorithm using shared path reliability calculation, known as the SRP-S algorithm. Extensive simulations demonstrate that the SRP algorithm reduces service failures by up to 22% compared to the benchmark algorithms. By introducing the shared backup path mechanism, the SRP-S algorithm reduces bandwidth consumption by up to 64% compared to the SRP algorithm with the fully protected path mechanism. It also reduces service failures by up to 11% compared to the SRP algorithm with the shared backup mechanism.

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Publication Details

Journal
IEEE Transactions on Network and Service Management
Published
2025-04-21
DOI
https://doi.org/10.1109/tnsm.2025.3562913
Primary Topic
Software System Performance and Reliability
Type
article
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article

Network-Aware Reliability Modeling and Optimization for Microservice Placement

Y. S. Huang, Fangyu Zhang, Hancheng Lu, Yuang Chen
1 links
IEEE Transactions on Network and Service Management
Software System Performance and Reliability
article

Network-Aware Reliability Modeling and Optimization for Microservice Placement

Y. S. Huang, Fangyu Zhang, Hancheng Lu, Yuang Chen
article en

Abstract

Optimizing microservice placement to enhance the reliability of services is crucial for improving the service level of microservice architecture-based mobile networks and Internet of Things (IoT) networks. Despite extensive research on service reliability, the impact of network load and routing on service reliability remains understudied, leading to suboptimal models and unsatisfactory performance. To address this issue, we propose a novel network-aware service reliability model that effectively captures the correlation between network state changes and reliability. Based on this model, we formulate the microservice placement problem as an integer nonlinear programming problem, aiming to maximize service reliability. Subsequently, a service reliability-aware placement (SRP) algorithm is proposed to solve the problem efficiently. To reduce bandwidth consumption, we further discuss the microservice placement problem with the shared backup path mechanism and propose a placement algorithm based on the SRP algorithm using shared path reliability calculation, known as the SRP-S algorithm. Extensive simulations demonstrate that the SRP algorithm reduces service failures by up to 22% compared to the benchmark algorithms. By introducing the shared backup path mechanism, the SRP-S algorithm reduces bandwidth consumption by up to 64% compared to the SRP algorithm with the fully protected path mechanism. It also reduces service failures by up to 11% compared to the SRP algorithm with the shared backup mechanism.

IEEE Transactions on Network and Service ManagementVol. 22(4)
University of Science and Technology of China (CN)
Openalex Percentile: Top 92%
Software System Performance and Reliability
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Network-Aware Reliability Modeling and Optimization for Microservice Placement — Y. S. Huang, Fangyu Zhang, et al. · IEEE Transactions on Network and Service Management (2025) | TGRS Research Map | TGRS