Contrastive Learning-Enhanced Large Language Models for Monolith-to-Microservice Decomposition

• URL: http://arxiv.org/abs/2502.04604v1
• Date: Fri, 07 Feb 2025 01:37:20 GMT
• Title: Contrastive Learning-Enhanced Large Language Models for Monolith-to-Microservice Decomposition
• Authors: Khaled Sellami, Mohamed Aymen Saied,
• Abstract summary: Monolithic applications become increasingly difficult to maintain and improve, leading to scaling and organizational issues. Despite its advantages, migrating from a monolithic to a monolithic architecture is often costly and complex. This research addresses this issue by introducing MonoEmbed, a Language Model based approach for automating the decomposition process.
• Score: 0.4297070083645049
• License:
• Abstract: As Monolithic applications evolve, they become increasingly difficult to maintain and improve, leading to scaling and organizational issues. The Microservices architecture, known for its modularity, flexibility and scalability, offers a solution for large-scale applications allowing them to adapt and meet the demand on an ever increasing user base. Despite its advantages, migrating from a monolithic to a microservices architecture is often costly and complex, with the decomposition step being a significant challenge. This research addresses this issue by introducing MonoEmbed, a Language Model based approach for automating the decomposition process. MonoEmbed leverages state-of-the-art Large Language Models (LLMs) and representation learning techniques to generate representation vectors for monolithic components, which are then clustered to form microservices. By evaluating various pre-trained models and applying fine-tuning techniques such as Contrastive Learning and Low Rank Adaptation (LoRA), MonoEmbed aims to optimize these representations for microservice partitioning. The evaluation of the fine-tuned models showcases that they were able to significantly improve the quality of the representation vectors when compared with pre-trained models and traditional representations. The proposed approach was benchmarked against existing decomposition methods, demonstrating superior performance in generating cohesive and balanced microservices for monolithic applications with varying scales.

Related papers

• JanusFlow: Harmonizing Autoregression and Rectified Flow for Unified Multimodal Understanding and Generation [36.93638123812204]
  We present JanusFlow, a powerful framework that unifies image understanding and generation in a single model. JanusFlow integrates autoregressive language models with rectified flow, a state-of-the-art method in generative modeling.
  arXiv  Detail & Related papers  (2024-11-12T17:55:10Z)
• Revisiting SMoE Language Models by Evaluating Inefficiencies with Task Specific Expert Pruning [78.72226641279863]
  Sparse Mixture of Expert (SMoE) models have emerged as a scalable alternative to dense models in language modeling. Our research explores task-specific model pruning to inform decisions about designing SMoE architectures. We introduce an adaptive task-aware pruning technique UNCURL to reduce the number of experts per MoE layer in an offline manner post-training.
  arXiv  Detail & Related papers  (2024-09-02T22:35:03Z)
• Robust Latent Representation Tuning for Image-text Classification [9.789498730131607]
  We propose a robust latent representation tuning method for large models. Our approach introduces a modality latent translation module to maximize the correlation between modalities, resulting in a robust representation. Within this framework, common semantics are refined during training, and robust performance is achieved even in the absence of one modality.
  arXiv  Detail & Related papers  (2024-06-10T06:29:00Z)
• Process Modeling With Large Language Models [42.0652924091318]
  This paper explores the integration of Large Language Models (LLMs) into process modeling. We propose a framework that leverages LLMs for the automated generation and iterative refinement of process models. Preliminary results demonstrate the framework's ability to streamline process modeling tasks.
  arXiv  Detail & Related papers  (2024-03-12T11:27:47Z)
• Model Composition for Multimodal Large Language Models [71.5729418523411]
  We propose a new paradigm through the model composition of existing MLLMs to create a new model that retains the modal understanding capabilities of each original model. Our basic implementation, NaiveMC, demonstrates the effectiveness of this paradigm by reusing modality encoders and merging LLM parameters.
  arXiv  Detail & Related papers  (2024-02-20T06:38:10Z)
• Majority Kernels: An Approach to Leverage Big Model Dynamics for Efficient Small Model Training [32.154166415680066]
  Methods like distillation, compression or quantization help leverage the highly performant large models to induce smaller performant ones. This paper explores the hypothesis that a single training run can simultaneously train a larger model for performance and derive a smaller model for deployment.
  arXiv  Detail & Related papers  (2024-02-07T17:07:41Z)
• Adapting Large Language Models for Content Moderation: Pitfalls in Data Engineering and Supervised Fine-tuning [79.53130089003986]
  Large Language Models (LLMs) have become a feasible solution for handling tasks in various domains. In this paper, we introduce how to fine-tune a LLM model that can be privately deployed for content moderation.
  arXiv  Detail & Related papers  (2023-10-05T09:09:44Z)
• Scaling Vision-Language Models with Sparse Mixture of Experts [128.0882767889029]
  We show that mixture-of-experts (MoE) techniques can achieve state-of-the-art performance on a range of benchmarks over dense models of equivalent computational cost. Our research offers valuable insights into stabilizing the training of MoE models, understanding the impact of MoE on model interpretability, and balancing the trade-offs between compute performance when scaling vision-language models.
  arXiv  Detail & Related papers  (2023-03-13T16:00:31Z)
• Slimmable Domain Adaptation [112.19652651687402]
  We introduce a simple framework, Slimmable Domain Adaptation, to improve cross-domain generalization with a weight-sharing model bank. Our framework surpasses other competing approaches by a very large margin on multiple benchmarks.
  arXiv  Detail & Related papers  (2022-06-14T06:28:04Z)
• Model-Agnostic Multitask Fine-tuning for Few-shot Vision-Language Transfer Learning [59.38343286807997]
  We propose Model-Agnostic Multitask Fine-tuning (MAMF) for vision-language models on unseen tasks. Compared with model-agnostic meta-learning (MAML), MAMF discards the bi-level optimization and uses only first-order gradients. We show that MAMF consistently outperforms the classical fine-tuning method for few-shot transfer learning on five benchmark datasets.
  arXiv  Detail & Related papers  (2022-03-09T17:26:53Z)

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.