MAMS: Multi-Agent MicroServices✱

Collier, Rem W.; O’Neill, Eoin; Lillis, David; O’Hare, Gregory M. P.

doi:10.1145/3308560.3316509

Cited by 24 publications

(14 citation statements)

References 27 publications

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“…Recently, researchers have proposed other interaction methods based on concepts like using a shared volume [19]. Scanflow leverages microservice-based agents [23] which could also interact with each other transparently with a service discovery through RESTful APIs. In practice, a single approach of social ability is often insufficient, and thus Scanflow agents apply both shared artifacts and RESTful APIs communication approaches to support social ability of agents.…”

Section: B Agent Social Abilitymentioning

confidence: 99%

Scanflow-K8s: Agent-based Framework for Autonomic Management and Supervision of ML Workflows in Kubernetes Clusters

Liu

Bravo-Rocca

Guitart

et al. 2022

2022 22nd IEEE International Symposium on Cluster, Cloud and Internet Computing (CCGrid)

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Machine Learning (ML) projects are currently heavily based on workflows composed of some reproducible steps and executed as containerized pipelines to build or deploy ML models efficiently because of the flexibility, portability, and fast delivery they provide to the ML life-cycle. However, deployed models need to be watched and constantly managed, supervised, and debugged to guarantee their availability, validity, and robustness in unexpected situations. Therefore, containerized ML workflows would benefit from leveraging flexible and diverse autonomic capabilities. This work presents an architecture for autonomic ML workflows with abilities for multi-layered control, based on an agent-based approach that enables autonomic management and supervision of ML workflows at the application layer and the infrastructure layer (by collaborating with the orchestrator). We redesign the Scanflow ML framework to support such multiagent approach by using triggers, primitives, and strategies. We also implement a practical platform, so-called Scanflow-K8s, that enables autonomic ML workflows on Kubernetes clusters based on the Scanflow agents. MNIST image classification and MLPerf ImageNet classification benchmarks are used as case studies to show the capabilities of Scanflow-K8s under different scenarios. The experimental results demonstrate the feasibility and effectiveness of our proposed agent approach and the Scanflow-K8s platform for the autonomic management of ML workflows in Kubernetes clusters at multiple layers.

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Section: B Agent Social Abilitymentioning

confidence: 99%

Scanflow-K8s: Agent-based Framework for Autonomic Management and Supervision of ML Workflows in Kubernetes Clusters

Liu

Bravo-Rocca

Guitart

et al. 2022

2022 22nd IEEE International Symposium on Cluster, Cloud and Internet Computing (CCGrid)

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“…Basically, they provide an isolated state, easy distribution, elasticity to best fit a scenario, automated management, and loose coupling (weak associations) [21]. These features share many commonalities with the characteristics of agents [22], and allow agents to have a live supervision over the workflow because they can perform actions by themselves all the time or when required.…”

Section: Agents In Production Systemsmentioning

confidence: 99%

Human-in-the-loop online multi-agent approach to increase trustworthiness in ML models through trust scores and data augmentation

Bravo-Rocca¹,

Liu²,

Guitart³

et al. 2022

Preprint

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Increasing a ML model accuracy is not enough, we must also increase its trustworthiness. This is an important step for building resilient AI systems for safety-critical applications such as automotive, finance, and healthcare. For that purpose, we propose a multi-agent system that combines both machine and human agents. In this system, a checker agent calculates a trust score of each instance (which penalizes overconfidence and overcautiousness in predictions) using an agreement-based method and ranks it; then an improver agent filters the anomalous instances based on a human rule-based procedure (which is considered safe), gets the human labels, applies geometric data augmentation, and retrains with the augmented data using transfer learning. We evaluate the system on corrupted versions of the MNIST and FashionMNIST datasets. We get an improvement in accuracy and trust score with just few additional labels compared to a baseline approach.

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“…In the context of intelligent agents, several solutions have been developed for microservice architectures. Rem et al [23] considered each agent as a microservice. It developed a template of the multi-agent system for handling microservices.…”

Section: Related Workmentioning

confidence: 99%

Reinforcement learning multi-agent system for faults diagnosis of mircoservices in industrial settings

Belhadi

Djenouri

Srivastava

et al. 2021

Computer Communications

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MAMS: Multi-Agent MicroServices✱

Cited by 24 publications

References 27 publications

Scanflow-K8s: Agent-based Framework for Autonomic Management and Supervision of ML Workflows in Kubernetes Clusters

Scanflow-K8s: Agent-based Framework for Autonomic Management and Supervision of ML Workflows in Kubernetes Clusters

Human-in-the-loop online multi-agent approach to increase trustworthiness in ML models through trust scores and data augmentation

Reinforcement learning multi-agent system for faults diagnosis of mircoservices in industrial settings

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