Microkernel-Based Web Architecture: Design & Implementation Considerations
- URL: http://arxiv.org/abs/2502.08802v1
- Date: Wed, 12 Feb 2025 21:29:18 GMT
- Title: Microkernel-Based Web Architecture: Design & Implementation Considerations
- Authors: Vick Dini,
- Abstract summary: I propose a middle-ground alternative between monolithic and microservice web architectures.
I revised the design of a microkernel-based web architecture, considering these challenges as well as recent architectural advancements.
- Score: 0.0
- License:
- Abstract: In this vision paper I propose a middle-ground alternative between monolithic and microservice web architectures. After identifying the key challenges associated with microservice architectures, I revised the design of a microkernel-based web architecture, considering these challenges as well as recent architectural advancements. Next, I examined contemporary approaches to various self-* properties and explored how this new architecture could enhance them, including a modified version of the MAPE-K loop. Once the high-level design of the microkernel architecture was finalized, I evaluated its potential to address the identified challenges. Lastly, I reflected on several implementation aspects of the proposed work.
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) - Software Design Pattern Model and Data Structure Algorithm Abilities on Microservices Architecture Design in High-tech Enterprises [0.4532517021515834]
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.
arXiv Detail & Related papers (2024-11-05T07:26:53Z) - Investigating Benefits and Limitations of Migrating to a Micro-Frontends Architecture [3.8206629823137597]
This study investigates the benefits and limitations of migrating a real-world application to a micro-frontends architecture.
Key benefits included enhanced flexibility in technology choices, scalability of development teams, and gradual migration of technologies.
However, the increased complexity of the architecture raised concerns among developers.
arXiv Detail & Related papers (2024-07-22T17:47:05Z) - 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) - Proposing a Dynamic Executive Microservices Architecture Model for AI
Systems [0.0]
Microservices architecture is one of the new architectural styles that has improved in recent years.
Orchestration of the components in the architecture is one of the main challenges in distributed systems.
The presented model, as a pattern, can be used at the both design and development level of the system.
arXiv Detail & Related papers (2023-08-10T19:31:02Z) - Heterogeneous Continual Learning [88.53038822561197]
We propose a novel framework to tackle the continual learning (CL) problem with changing network architectures.
We build on top of the distillation family of techniques and modify it to a new setting where a weaker model takes the role of a teacher.
We also propose Quick Deep Inversion (QDI) to recover prior task visual features to support knowledge transfer.
arXiv Detail & Related papers (2023-06-14T15:54:42Z) - Towards efficient feature sharing in MIMO architectures [102.40140369542755]
Multi-input multi-output architectures propose to train multipleworks within one base network and then average the subnetwork predictions to benefit from ensembling for free.
Despite some relative success, these architectures are wasteful in their use of parameters.
We highlight in this paper that the learned subnetwork fail to share even generic features which limits their applicability on smaller mobile and AR/VR devices.
arXiv Detail & Related papers (2022-05-20T12:33:34Z) - Analyze and Design Network Architectures by Recursion Formulas [4.085771561472743]
This work attempts to find an effective way to design new network architectures.
It is discovered that the main difference between network architectures can be reflected in their formulas.
A case study is provided to generate an improved architecture based on ResNet.
Massive experiments are conducted on CIFAR and ImageNet, which witness the significant performance improvements.
arXiv Detail & Related papers (2021-08-18T06:53:30Z) - Does Form Follow Function? An Empirical Exploration of the Impact of
Deep Neural Network Architecture Design on Hardware-Specific Acceleration [76.35307867016336]
This study investigates the impact of deep neural network architecture design on the degree of inference speedup.
We show that while leveraging hardware-specific acceleration achieved an average inference speed-up of 380%, the degree of inference speed-up varied drastically depending on the macro-architecture design pattern.
arXiv Detail & Related papers (2021-07-08T23:05:39Z) - Twins: Revisiting Spatial Attention Design in Vision Transformers [81.02454258677714]
In this work, we demonstrate that a carefully-devised yet simple spatial attention mechanism performs favourably against the state-of-the-art schemes.
We propose two vision transformer architectures, namely, Twins-PCPVT and Twins-SVT.
Our proposed architectures are highly-efficient and easy to implement, only involving matrix multiplications that are highly optimized in modern deep learning frameworks.
arXiv Detail & Related papers (2021-04-28T15:42:31Z) - 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)
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.