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Contrôle de Congestion dans les Systèmes Distribués à l'Échelle du Cloud

Contrôle de Congestion dans les Systèmes Distribués à l’Échelle du Cloud

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Le contrôle de congestion dans les systèmes distribués à l’échelle du cloud est un sujet important pour assurer des performances optimales. Nous allons examiner comment le contrôle de congestion peut être mis en œuvre dans ce type de système.

Systèmes distribués composés de plusieurs systèmes reliés pour fournir une fonctionnalité spécifique

Testing is a key part of distributed system development. It is used to measure the performance of the system under various conditions. The tests should be designed to simulate the expected traffic surges and should be run frequently to ensure that the system is performing as expected. The results of the tests should be analyzed to identify any potential issues and to ensure that the system is able to handle the expected traffic surges. 

Les systèmes distribués sont composés de plusieurs systèmes reliés entre eux pour fournir une fonctionnalité spécifique. Les systèmes qui fonctionnent à l’échelle du cloud peuvent recevoir des pics de trafic attendus ou inattendus d’un ou de plusieurs appelants et sont censés fonctionner de manière prévisible. 

Cet article analyse les effets des pics de trafic sur un système distribué. Il présente une analyse détaillée de la façon dont chaque couche est affectée et fournit des mécanismes pour obtenir une performance prévisible pendant les pics de trafic. 

Le test est une partie essentielle du développement des systèmes distribués. Il est utilisé pour mesurer les performances du système dans différentes conditions. Les tests doivent être conçus pour simuler les pics de trafic attendus et doivent être exécutés fréquemment pour s’assurer que le système fonctionne comme prévu. Les résultats des tests doivent être analysés pour identifier tout problème potentiel et pour s’assurer que le système est capable de gérer les pics de trafic attendus. 

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Systèmes distribués: le split-brain

Systèmes distribués: le split-brain

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Les systèmes distribués sont une technologie complexe qui peut présenter des risques, tels que le split-brain. Apprenons à mieux comprendre ce phénomène et à le gérer.

Le problème du Split-Brain

Split-brain can be caused by a variety of factors, including network partitions, hardware failures, or software bugs. It can also be triggered by intentional actions, such as when an administrator deliberately isolates a node from the cluster. In any case, the result is the same: two or more isolated groups of nodes, each with its own view of the data.

Real-World Example

A real-world example of split-brain occurred in 2017 when a major outage affected Amazon Web Services’ S3 storage service. The outage was caused by a network partition that split the S3 cluster into two isolated groups. As a result, some requests to the S3 service were routed to one group, while others were routed to the other group. This caused data inconsistency and led to widespread disruption.

The S3 outage serves as a reminder of the importance of testing distributed systems for split-brain scenarios. While it is impossible to completely eliminate the risk of split-brain, it is possible to reduce the impact by designing systems that are resilient to network partitions and other forms of failure.

Best Practices

When designing distributed systems, it is important to consider how the system will handle split-brain scenarios. In some cases, it may be possible to use techniques such as quorum or leader election to minimize the impact of split-brain. However, these techniques should be used with caution, as they can introduce additional complexity and overhead.

In general, the best approach is to design systems that are resilient to network partitions and other forms of failure. This can be achieved by using techniques such as replication, redundancy, and fault tolerance. It is also important to test distributed systems for split-brain scenarios before they are deployed in production.

Le problème du Split-Brain

Dans les systèmes distribués, il est essentiel de maintenir une vue cohérente des données sur tous les nœuds pour un fonctionnement correct. Lorsqu’un scénario de split-brain se produit, chaque groupe partitionné peut recevoir des mises à jour différentes, ce qui entraîne une incohérence des données et rend difficile la résolution des conflits lorsque les partitions se reconnectent finalement.

Le split-brain peut être causé par une variété de facteurs, notamment des partitions réseau, des pannes matérielles ou des bogues logiciels. Il peut également être déclenché par des actions intentionnelles, telles que lorsqu’un administrateur isole délibérément un nœud du cluster. Dans tous les cas, le résultat est le même : deux ou plusieurs groupes isolés de nœuds, chacun ayant sa propre vue des données.

Exemple concret

Un exemple concret de split-brain s’est produit en 2017 lorsqu’une panne majeure a affecté le service de stockage S3 d’Amazon Web Services. La panne était causée par une partition réseau qui a divisé le cluster S3 en deux groupes isolés. En conséquence, certaines demandes au service S3 ont été acheminées vers un groupe, tandis

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Using Redis on Cloud? Here Are Ten Things You Should Know

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It’s hard to operate stateful distributed systems at scale and Redis is no exception. Managed databases make life easier by taking on much of the heavy lifting. But you still need a sound architecture and apply best practices both on the server (Redis) as well as the client (application).

This blog covers a range of Redis-related best practices, tips and tricks including cluster scalability, client-side configuration, integration, metrics etc. Although I will be citing Amazon MemoryDB and ElastiCache for Redis from time to time, most (if not all) will be applicable to Redis clusters in general.

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Elasticsearch Fundamentals and Setup Progress

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Elasticsearch is a full-text search engine and analysis tool developed using Java programming language on Apache Lucene infrastructure. 

Lucene, which was developed to perform searches on huge text files on a single machine, is Elasticsearch, which emerged because it was insufficient in searches on instant data and distributed systems; It has gained popularity in a short time with its flexible structure, ability to work with real-time data in distributed systems.

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Observability Maturity Model

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Modern systems and applications span numerous architectures and technologies — they are also becoming increasingly more dynamic, distributed, and modular in nature. In order to support the availability and performance of their systems, IT operations and SRE teams need advanced monitoring capabilities. This Refcard reviews the four distinct levels of observability maturity, key functionality at each stage, and next steps organizations should take to enhance their monitoring practices.
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Managing Secrets in Node.js With HashiCorp Vault

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As the number of services grows in an organization, the problem of secret management only gets worse. Between Zero Trust and the emergence of microservices, handling secrets such as tokens, credentials, and keys has become an increasingly challenging task. That’s where a solution like HashiCorp’s Vault can help organizations solve their secret management woes.

Although there are secret management tools native to each cloud provider, using these solutions locks you in with a specific cloud provider. Vault, on the other hand, is open source and portable.

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Deploying an Apache Kafka mock service with Microcks

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Developers are working with new applications every day using Apache Kafka as the backbone to implement an event-driven architecture (EDA) to support distributed systems. However, this adds new challenges when sharing across teams, even within the same organization. What endpoints are available? What is the structure of the message? That’s why payload examples became critical to speed up development. For this reason, having a reliable and enterprise-grade service to mock Apache Kafka should be an item in your EDA checklist. This post will do a quick review of the Microcks General Availability (GA) version and their support to Kafka.

What is Microcks?

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Creating Grafana Dashboards to Visualize Alluxio Metrics

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Creating Grafana Dashboards

Overview

Monitoring metrics is highly important to operate distributed systems in production. Alluxio collects metrics using the Codahale Metrics Library on I/O throughput, RPC throughput, and resource usage. Alluxio metrics are shown in its webUI but are also available through a REST endpoint or exportable to several third-party sinks in a time-series manner (see docs).

Grafana, a comprehensive metrics visualization software, ties into this process by pulling the metrics that systems like Alluxio collect through a sink and visualizes them in a more helpful fashion. This guide will cover how to set up Grafana and Graphite, a supported sink for Alluxio, which will put metrics in a time-series database, along with exploring some of the possibilities that the combination offers.

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29 Top Tools for Building Microservices on All Levels

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There are many great articles out there on microservices. For those who have been hiding under a rock about the controversial technique—or are new to the idea—this article simply aims to collate the top open source tools available in one handy place. Microservice architecture, or just microservices, is a highly scalable structural style for developing software systems. Such architecture can be used for enterprise applications for businesses, governments, schools, and charities, etc. It is quite the opposite of the legacy-style monolithic architecture that focuses on a single unit application.

Microservices are small, independent, and unique. And the architecture can be complex in both construction and maintenance. Microservices communicate with each other to serve business goals utilizing synchronous protocols, HTTP/REST or asynchronous protocols. HTTP/REST or AMQP are examples of collaborating services that implement functions related to one another to work as efficiently as possible.

Source de l’article sur DZONE

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