The Microservice Dependency Matrix
- URL: http://arxiv.org/abs/2309.02804v1
- Date: Wed, 6 Sep 2023 07:41:00 GMT
- Title: The Microservice Dependency Matrix
- Authors: Amr S. Abdelfattah, Tomas Cerny
- Abstract summary: This paper introduces the Dependency Matrix (EDM) and Data Dependency Matrix (DDM) as tools to address this challenge.
We present an automated approach for tracking these dependencies and demonstrate their extraction through a case study.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Microservices have been recognized for over a decade. They reshaped system
design enabling decentralization and independence of development teams working
on particular microservices. While loosely coupled microservices are desired,
it is inevitable for dependencies to arise. However, these dependencies often
go unnoticed by development teams. As the system evolves, making changes to one
microservice may trigger a ripple effect, necessitating adjustments in
dependent microservices and increasing maintenance and operational efforts.
Tracking different types of dependencies across microservices becomes crucial
in anticipating the consequences of development team changes. This paper
introduces the Endpoint Dependency Matrix (EDM) and Data Dependency Matrix
(DDM) as tools to address this challenge. We present an automated approach for
tracking these dependencies and demonstrate their extraction through a case
study.
Related papers
- Benchmarking Data Management Systems for Microservices [1.9948490148513414]
Microservice architectures are a popular choice for deploying large-scale data-intensive applications.
Existing microservice benchmarks lack essential data management challenges.
Online Marketplace is a novel benchmark that embraces core data management requirements.
arXiv Detail & Related papers (2024-05-19T11:55:45Z) - A Feature Dataset of Microservices-based Systems [2.3734388579113275]
Poor practices in the design and development of datasets are called microservice bad smells.
There is a lack of an appropriate open-source microservice feature dataset.
arXiv Detail & Related papers (2024-04-02T09:52:18Z) - Decentralized Online Learning in Task Assignment Games for Mobile
Crowdsensing [55.07662765269297]
A mobile crowdsensing platform (MCSP) sequentially publishes sensing tasks to the available mobile units (MUs) that signal their willingness to participate in a task by sending sensing offers back to the MCSP.
A stable task assignment must address two challenges: the MCSP's and MUs' conflicting goals, and the uncertainty about the MUs' required efforts and preferences.
To overcome these challenges a novel decentralized approach combining matching theory and online learning, called collision-avoidance multi-armed bandit with strategic free sensing (CA-MAB-SFS) is proposed.
arXiv Detail & Related papers (2023-09-19T13:07:15Z) - Evaluating the Risk of Changes in a Microservices Architecture [0.0]
In a-based system, reliability and availability are key components to guarantee the best-in-class experience for the consumers.
One of the key advantages of architecture is the ability to independently deploy services, providing maximum change flexibility.
arXiv Detail & Related papers (2023-09-12T13:54:28Z) - DeepScaler: Holistic Autoscaling for Microservices Based on
Spatiotemporal GNN with Adaptive Graph Learning [4.128665560397244]
This paper presents DeepScaler, a deep learning-based holistic autoscaling approach.
It focuses on coping with service dependencies to optimize service-level agreements (SLA) assurance and cost efficiency.
Experimental results demonstrate that our method implements a more effective autoscaling mechanism for microservice.
arXiv Detail & Related papers (2023-09-02T08:22:21Z) - Handling Communication via APIs for Microservices [6.5499625417846685]
We discuss the challenges with conventional techniques of communication using and propose an alternative way of ID-passing via APIs.
We also devise an algorithm to reduce the number of APIs.
arXiv Detail & Related papers (2023-08-02T17:40:34Z) - Automatic Data Augmentation via Invariance-Constrained Learning [94.27081585149836]
Underlying data structures are often exploited to improve the solution of learning tasks.
Data augmentation induces these symmetries during training by applying multiple transformations to the input data.
This work tackles these issues by automatically adapting the data augmentation while solving the learning task.
arXiv Detail & Related papers (2022-09-29T18:11:01Z) - Bilateral Dependency Optimization: Defending Against Model-inversion
Attacks [61.78426165008083]
We propose a bilateral dependency optimization (BiDO) strategy to defend against model-inversion attacks.
BiDO achieves the state-of-the-art defense performance for a variety of datasets, classifiers, and MI attacks.
arXiv Detail & Related papers (2022-06-11T10:07:03Z) - Causal Scene BERT: Improving object detection by searching for
challenging groups of data [125.40669814080047]
Computer vision applications rely on learning-based perception modules parameterized with neural networks for tasks like object detection.
These modules frequently have low expected error overall but high error on atypical groups of data due to biases inherent in the training process.
Our main contribution is a pseudo-automatic method to discover such groups in foresight by performing causal interventions on simulated scenes.
arXiv Detail & Related papers (2022-02-08T05:14:16Z) - SOLIS -- The MLOps journey from data acquisition to actionable insights [62.997667081978825]
In this paper we present a unified deployment pipeline and freedom-to-operate approach that supports all requirements while using basic cross-platform tensor framework and script language engines.
This approach however does not supply the needed procedures and pipelines for the actual deployment of machine learning capabilities in real production grade systems.
arXiv Detail & Related papers (2021-12-22T14:45:37Z) - Reducing Catastrophic Forgetting in Self Organizing Maps with
Internally-Induced Generative Replay [67.50637511633212]
A lifelong learning agent is able to continually learn from potentially infinite streams of pattern sensory data.
One major historic difficulty in building agents that adapt is that neural systems struggle to retain previously-acquired knowledge when learning from new samples.
This problem is known as catastrophic forgetting (interference) and remains an unsolved problem in the domain of machine learning to this day.
arXiv Detail & Related papers (2021-12-09T07:11:14Z)
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.