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Network-Aware Reliability Modeling and Optimization for Microservice Placement

Fangyu Zhang, Yuang Chen, Hancheng Lu, Yongsheng Huang

TL;DR

This paper tackles reliability in microservice placement for MSA-driven 5G/IoT networks by introducing a network-aware reliability model that ties dynamic network load and routing to service success. It formulates the placement problem to maximize service reliability $r_{G^i}$ and proposes two heuristics: SRP for fully protected paths and SRP-S for shared backup paths via SPRC that accounts for path contention. Empirical results show SRP can reduce service failures by up to 29% over benchmarks, while SRP-S can cut bandwidth use by up to 62% and still cut failures by up to 21% compared with shared-backup baselines. The approach offers practical gains in reliability and bandwidth efficiency for edge/fog deployments in 5G and IoT contexts, with future work on broader backup mechanisms and dynamic resource management.

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 29% compared to the benchmark algorithms. By introducing the shared backup path mechanism, the SRP-S algorithm reduces bandwidth consumption by up to 62% compared to the SRP algorithm with the fully protected path mechanism. It also reduces service failures by up to 21% compared to the SRP algorithm with the shared backup mechanism.

Network-Aware Reliability Modeling and Optimization for Microservice Placement

TL;DR

This paper tackles reliability in microservice placement for MSA-driven 5G/IoT networks by introducing a network-aware reliability model that ties dynamic network load and routing to service success. It formulates the placement problem to maximize service reliability and proposes two heuristics: SRP for fully protected paths and SRP-S for shared backup paths via SPRC that accounts for path contention. Empirical results show SRP can reduce service failures by up to 29% over benchmarks, while SRP-S can cut bandwidth use by up to 62% and still cut failures by up to 21% compared with shared-backup baselines. The approach offers practical gains in reliability and bandwidth efficiency for edge/fog deployments in 5G and IoT contexts, with future work on broader backup mechanisms and dynamic resource management.

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 29% compared to the benchmark algorithms. By introducing the shared backup path mechanism, the SRP-S algorithm reduces bandwidth consumption by up to 62% compared to the SRP algorithm with the fully protected path mechanism. It also reduces service failures by up to 21% compared to the SRP algorithm with the shared backup mechanism.
Paper Structure (23 sections, 42 equations, 8 figures, 1 table, 4 algorithms)

This paper contains 23 sections, 42 equations, 8 figures, 1 table, 4 algorithms.

Table of Contents

  1. Introduction
  2. Related Work
  3. Reliability Model
  4. Microservice Placement
  5. System Model
  6. Infrastructure Network Model
  7. Service Request Model
  8. Hardware And Software Reliability Model
  9. Network-Aware Service Reliability Model
  10. Problem Formulation
  11. Proposed SRP Algorithm
  12. Algorithm Description
  13. Complexity Analysis
  14. Proposed SPRC Algorithm
  15. Algorithm Description
  16. ...and 8 more sections

Figures (8)

  • Figure 1: The process of placing microservices into the infrastructure network.
  • Figure 2: Reliability performance with the fully protected path mechanism.
  • Figure 3: Reliability performance with the shared backup path mechanism.
  • Figure 4: Reliability performance with different topologies.
  • Figure 5: Reliability performance with different resource requirements.
  • ...and 3 more figures