Graph Based Liability Analysis for the Microservice Architecture

Kalinagac, Onur; Soussi, Wissem; Gûr, Gûrkan

doi:10.23919/cnsm55787.2022.9964681

Cited by 2 publications

(1 citation statement)

References 8 publications

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“…Additionally, we deploy Chaos Mesh, an open-source platform that simulate various failure scenarios. The LAS receives service metrics from the GRALAF (Graph-Based Liability Analysis Framework) described in [6]. The provider of core service committed to three SLAs namely availability (𝑆𝐿𝐴 0 ), latency (𝑆𝐿𝐴 1 ), and error rate (𝑆𝐿𝐴 2 ).…”

Section: Introductionmentioning

confidence: 99%

Demonstrating Liability and Trust Metrics for Multi-Actor, Dynamic Edge and Cloud Microservices

Anser,

Gaber,

Cajeat

et al. 2023

Proceedings of the 29th Annual International Conference on Mobile Computing and Networking

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Transitioning 5G, edge and cloud computing towards servicebased architecture increases their complexity as they become even more dynamic and intertwine more actors or delegation levels. In this paper, we demonstrate the Liability-Aware security manager Analysis Service (LAS), a framework that uses machine learning techniques to compute liability and trust indicators for service-based architectures such cloud microservices. Based on the commitments of Service Providers and real-time observations collected by a Root Cause Analysis (RCA) tool GRALAF, the LAS computes three categories of liability and trust indicators, specifically, a Commitment Trust Score, Financial Exposure, and Commitment Trends. CCS CONCEPTS• Networks → Cloud computing; • Social and professional topics → Quality assurance.

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Section: Introductionmentioning

confidence: 99%

Demonstrating Liability and Trust Metrics for Multi-Actor, Dynamic Edge and Cloud Microservices

Anser,

Gaber,

Cajeat

et al. 2023

Proceedings of the 29th Annual International Conference on Mobile Computing and Networking

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Root Cause and Liability Analysis in the Microservices Architecture for Edge IoT Services

Kalinagac,

Soussi,

Anser

et al. 2023

ICC 2023 - IEEE International Conference on Communications

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In this work, we present a liability analysis framework for root cause analysis (RCA) in the microservices architecture with IoT-oriented containerized network services. We keep track of the performance metrics of microservices, such as service response time, memory usage and availability, to detect anomalies. By injecting faults in the services, we construct a Causal Bayesian Network (CBN) which represents the relation between service faults and metrics. Service Level Agreement (SLA) data obtained from a descriptor named TRAILS (sTakeholder Responsibility, Accountability and Liability deScriptor) is also used to flag service providers which have failed their commitments. In the case of SLA violation, the constructed CBN is used to predict the fault probability of services under given metric readings and to identify the root cause.

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Graph Based Liability Analysis for the Microservice Architecture

Cited by 2 publications

References 8 publications

Demonstrating Liability and Trust Metrics for Multi-Actor, Dynamic Edge and Cloud Microservices

Demonstrating Liability and Trust Metrics for Multi-Actor, Dynamic Edge and Cloud Microservices

Root Cause and Liability Analysis in the Microservices Architecture for Edge IoT Services

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