Network Centrality as a New Perspective on Microservice Architecture
- URL: http://arxiv.org/abs/2501.13520v1
- Date: Thu, 23 Jan 2025 10:13:57 GMT
- Title: Network Centrality as a New Perspective on Microservice Architecture
- Authors: Alexander Bakhtin, Matteo Esposito, Valentina Lenarduzzi, Davide Taibi,
- Abstract summary: The adoption of Microservice Architecture has led to the identification of various patterns and anti-patterns, such as Nano/Mega/Hub services.
This study investigates whether centrality metrics (CMs) can provide new insights into MSA quality and facilitate the detection of architectural anti-patterns.
- Score: 48.55946052680251
- License:
- Abstract: Context: Over the past decade, the adoption of Microservice Architecture (MSA) has led to the identification of various patterns and anti-patterns, such as Nano/Mega/Hub services. Detecting these anti-patterns often involves modeling the system as a Service Dependency Graph (SDG) and applying graph-theoretic approaches. Aim: While previous research has explored software metrics (SMs) such as size, complexity, and quality for assessing MSAs, the potential of graph-specific metrics like network centrality remains largely unexplored. This study investigates whether centrality metrics (CMs) can provide new insights into MSA quality and facilitate the detection of architectural anti-patterns, complementing or extending traditional SMs. Method: We analyzed 24 open-source MSA projects, reconstructing their architectures to study 53 microservices. We measured SMs and CMs for each microservice and tested their correlation to determine the relationship between these metric types. Results and Conclusion: Among 902 computed metric correlations, we found weak to moderate correlation in 282 cases. These findings suggest that centrality metrics offer a novel perspective for understanding MSA properties. Specifically, ratio-based centrality metrics show promise for detecting specific anti-patterns, while subgraph centrality needs further investigation for its applicability in architectural assessments.
Related papers
- A Survey of Model Architectures in Information Retrieval [64.75808744228067]
We focus on two key aspects: backbone models for feature extraction and end-to-end system architectures for relevance estimation.
We trace the development from traditional term-based methods to modern neural approaches, particularly highlighting the impact of transformer-based models and subsequent large language models (LLMs)
We conclude by discussing emerging challenges and future directions, including architectural optimizations for performance and scalability, handling of multimodal, multilingual data, and adaptation to novel application domains beyond traditional search paradigms.
arXiv Detail & Related papers (2025-02-20T18:42:58Z) - System States Forecasting of Microservices with Dynamic Spatio-Temporal Data [9.519440926598524]
Current forecasting methods are insufficient in environments where relationships are critical.
In both short-term and long-term forecasting tasks, our model consistently achieved a 8.6% reduction in MAE(Mean Absolute Error) and a 2.2% reduction in MSE (Mean Squared Error)
arXiv Detail & Related papers (2024-08-15T02:52:02Z) - Benchmarks as Microscopes: A Call for Model Metrology [76.64402390208576]
Modern language models (LMs) pose a new challenge in capability assessment.
To be confident in our metrics, we need a new discipline of model metrology.
arXiv Detail & Related papers (2024-07-22T17:52:12Z) - CHASE: A Causal Heterogeneous Graph based Framework for Root Cause Analysis in Multimodal Microservice Systems [22.00860661894853]
We propose a Causal Heterogeneous grAph baSed framEwork for root cause analysis, namely CHASE, for microservice systems with multimodal data.
CHASE learns from the constructed hypergraph with hyperedges representing the flow of causality and performs root cause localization.
arXiv Detail & Related papers (2024-06-28T07:46:51Z) - A Microservices Identification Method Based on Spectral Clustering for
Industrial Legacy Systems [5.255685751491305]
We propose an automated microservice decomposition method for extracting microservice candidates based on spectral graph theory.
We show that our method can yield favorable results even without the involvement of domain experts.
arXiv Detail & Related papers (2023-12-20T07:47:01Z) - A Novel Energy based Model Mechanism for Multi-modal Aspect-Based
Sentiment Analysis [85.77557381023617]
We propose a novel framework called DQPSA for multi-modal sentiment analysis.
PDQ module uses the prompt as both a visual query and a language query to extract prompt-aware visual information.
EPE module models the boundaries pairing of the analysis target from the perspective of an Energy-based Model.
arXiv Detail & Related papers (2023-12-13T12:00:46Z) - Twin Graph-based Anomaly Detection via Attentive Multi-Modal Learning
for Microservice System [24.2074235652359]
We propose MSTGAD, which seamlessly integrates all available data modalities via attentive multi-modal learning.
We construct a transformer-based neural network with both spatial and temporal attention mechanisms to model the inter-correlations between different modalities.
This enables us to detect anomalies automatically and accurately in real-time.
arXiv Detail & Related papers (2023-10-07T06:28:41Z) - On the Empirical Evidence of Microservice Logical Coupling. A Registered
Report [15.438443553618896]
We propose the design of a study aimed at empirically validating the Microservice Logical Coupling (MLC) metric presented in our previous study.
In particular, we plan to empirically study Open Source Systems (OSS) built using a microservice architecture.
arXiv Detail & Related papers (2023-06-03T07:29:54Z) - Leveraging a Probabilistic PCA Model to Understand the Multivariate
Statistical Network Monitoring Framework for Network Security Anomaly
Detection [64.1680666036655]
We revisit anomaly detection techniques based on PCA from a probabilistic generative model point of view.
We have evaluated the mathematical model using two different datasets.
arXiv Detail & Related papers (2023-02-02T13:41:18Z) - Metrics reloaded: Recommendations for image analysis validation [59.60445111432934]
Metrics Reloaded is a comprehensive framework guiding researchers in the problem-aware selection of metrics.
The framework was developed in a multi-stage Delphi process and is based on the novel concept of a problem fingerprint.
Based on the problem fingerprint, users are guided through the process of choosing and applying appropriate validation metrics.
arXiv Detail & Related papers (2022-06-03T15:56:51Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.