Software Design Pattern Model and Data Structure Algorithm Abilities on Microservices Architecture Design in High-tech Enterprises

• URL: http://arxiv.org/abs/2411.04143v1
• Date: Tue, 05 Nov 2024 07:26:53 GMT
• Title: Software Design Pattern Model and Data Structure Algorithm Abilities on Microservices Architecture Design in High-tech Enterprises
• Authors: Jun Cui,
• Abstract summary: This study investigates the impact of software design model capabilities and data structure algorithm abilities on architecture design within enterprises. The findings reveal that organizations emphasizing robust design models and efficient algorithms achieve superior scalability, performance, and flexibility in their architecture.
• Score: 0.4532517021515834
• License:
• Abstract: This study investigates the impact of software design model capabilities and data structure algorithm abilities on microservices architecture design within enterprises. Utilizing a qualitative methodology, the research involved in-depth interviews with software architects and developers who possess extensive experience in microservices implementation. The findings reveal that organizations emphasizing robust design models and efficient algorithms achieve superior scalability, performance, and flexibility in their microservices architecture. Notably, participants highlighted that a strong foundation in these areas facilitates better service decomposition, optimizes data processing, and enhances system responsiveness. Despite these insights, gaps remain regarding the integration of emerging technologies and the evolving nature of software design practices. This paper contributes to the existing literature by underscoring the critical role of these competencies in fostering effective microservices architectures and suggests avenues for future research to address identified gaps

Related papers

• Cliqueformer: Model-Based Optimization with Structured Transformers [102.55764949282906]
  We develop a model that learns the structure of an MBO task and empirically leads to improved designs. We evaluate Cliqueformer on various tasks, ranging from high-dimensional black-box functions to real-world tasks of chemical and genetic design.
  arXiv  Detail & Related papers  (2024-10-17T00:35:47Z)
• Experimental evaluation of architectural software performance design patterns in microservices [5.662788913145226]
  This research aims to quantify the effect of design patterns on system performance metrics. Real performance measurements are collected and compared with model-based predictions. Results demonstrate that, despite the difficulty to parameterize our benchmark system, model-based predictions are in line with real experimentation.
  arXiv  Detail & Related papers  (2024-08-20T12:21:40Z)
• An Infrastructure Cost Optimised Algorithm for Partitioning of Microservices [20.638612359627952]
  As migrating applications into the cloud is universally adopted by the software industry, have proven to be the most suitable and widely accepted architecture pattern for applications deployed on distributed cloud. Their efficacy is enabled by both technical benefits like reliability, fault isolation, scalability and productivity benefits like ease of asset maintenance and clear ownership boundaries. In some cases, the complexity of migrating an existing application into the architecture becomes overwhelmingly complex and expensive.
  arXiv  Detail & Related papers  (2024-08-13T02:08:59Z)
• Systematic Mapping of Monolithic Applications to Microservices Architecture [2.608935407927351]
  It discusses the advantages of and the challenges that organizations face when transitioning from a monolithic system. It presents a case study of a financial application and proposed techniques for identifying on monolithic systems using domain-driven development concepts.
  arXiv  Detail & Related papers  (2023-09-07T15:47:11Z)
• Computation-efficient Deep Learning for Computer Vision: A Survey [121.84121397440337]
  Deep learning models have reached or even exceeded human-level performance in a range of visual perception tasks. Deep learning models usually demand significant computational resources, leading to impractical power consumption, latency, or carbon emissions in real-world scenarios. New research focus is computationally efficient deep learning, which strives to achieve satisfactory performance while minimizing the computational cost during inference.
  arXiv  Detail & Related papers  (2023-08-27T03:55:28Z)
• Cloud-Native Architectural Characteristics and their Impacts on Software Quality: A Validation Survey [1.4502611532302039]
  We aim to investigate relationships between architectural characteristics of cloud-native applications, and quality aspects. The architectural characteristics in consideration are based on our recently proposed quality model for cloud-native software architectures. We present an updated version of the quality model incorporating the survey results.
  arXiv  Detail & Related papers  (2023-06-21T19:35:03Z)
• AI Techniques in the Microservices Life-Cycle: A Survey [10.06596283248616]
  In microservice systems, functionalities are provided by loosely coupled, small services, each focusing on a specific business capability. Building a system according to the architectural style brings a number of challenges, mainly related to how different are deployed and coordinated. In this paper, we provide a survey about how techniques in the area of Artificial Intelligence have been used to tackle these challenges.
  arXiv  Detail & Related papers  (2023-05-25T14:24:37Z)
• CateCom: a practical data-centric approach to categorization of computational models [77.34726150561087]
  We present an effort aimed at organizing the landscape of physics-based and data-driven computational models. We apply object-oriented design concepts and outline the foundations of an open-source collaborative framework.
  arXiv  Detail & Related papers  (2021-09-28T02:59:40Z)
• A Field Guide to Federated Optimization [161.3779046812383]
  Federated learning and analytics are a distributed approach for collaboratively learning models (or statistics) from decentralized data. This paper provides recommendations and guidelines on formulating, designing, evaluating and analyzing federated optimization algorithms.
  arXiv  Detail & Related papers  (2021-07-14T18:09:08Z)
• Stage-Wise Neural Architecture Search [65.03109178056937]
  Modern convolutional networks such as ResNet and NASNet have achieved state-of-the-art results in many computer vision applications. These networks consist of stages, which are sets of layers that operate on representations in the same resolution. It has been demonstrated that increasing the number of layers in each stage improves the prediction ability of the network. However, the resulting architecture becomes computationally expensive in terms of floating point operations, memory requirements and inference time.
  arXiv  Detail & Related papers  (2020-04-23T14:16:39Z)
• A Privacy-Preserving Distributed Architecture for Deep-Learning-as-a-Service [68.84245063902908]
  This paper introduces a novel distributed architecture for deep-learning-as-a-service. It is able to preserve the user sensitive data while providing Cloud-based machine and deep learning services.
  arXiv  Detail & Related papers  (2020-03-30T15:12:03Z)

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.