Autocruise

1. 《Site Reliability Engineering: How Google Runs Production Systems》 作者 : Niall Richard Murphy, Betsy Beyer, Chris Jones, Jennifer Petoff 出版時間 : 2016年10月 內容簡介 ：這本書是 SRE 的基礎指南，介紹了 Google 如何運營大規模生產系統。書中詳細闡述了 SRE 的原則、方法及其對可靠性、可擴展性和效率的重視。主要概念包括 SLA（服務等級協議）、SLO（服務等級目標）、SLI（服務等級指標）以及故障管理、容量規劃等實踐。 強調可靠性與創新之間的平衡，推崇「運維即開發」的理念，倡導運維與開發團隊的緊密協作。 強調量化管理服務可靠性，建立 SLO 和 SLI 來衡量系統表現，並透過數據驅動的方式進行改進。 引導 DevOps/SRE 團隊採取自動化和精益化方式來提升效率，降低運維風險。 2. 《The Site Reliability Workbook: Practical Ways to Implement SRE》 作者 : Betsy Beyer, Niall Richard Murphy, David K. Rensin, Kent Kawahara, Stephen Thorne 出版時間 : 2018年3月 內容簡介 ：這本書是《Site Reliability Engineering》的實踐擴展，提供了具體的操作範例和建議，幫助讀者將 SRE 的理念應用到日常工作中。書中涵蓋了如何設計和運營可靠系統、如何進行容量管理、故障預防、測試及事件管理等內容。 提供具體的實踐步驟，讓 DevOps/SRE 團隊能夠在實際工作中應用 SRE 方法。 鼓勵團隊建立自動化流程，提高系統運營的效率和可擴展性，並減少人工干預。 3. 《Seeking SRE: Conversations About Running Production Systems at Scale》 作者 : David N. Blank-Edelman 出版時間 : 2019年6月 內容簡介 ：這本書以對話的形式，探索了 SRE 的哲學、策略和挑戰，並介紹了 Google 和其他大公司的真實案例。書中討論了如何實現可靠性、如何應對文化挑戰以及如...

Site Reliability Engineering (SRE) is a discipline that’s grown rapidly over the past two decades, originally pioneered by Google to ensure large-scale systems could operate efficiently and reliably. As the field evolved, it has seen an integration with DevOps, creating a hybrid model focused on both cultural collaboration and engineering-driven reliability. In this post, we’ll dive into the evolution of SRE, from its Google roots to the current DevOps-SRE hybrid, and explore the key differences between these two approaches. 1. The Birth of Google SRE (2003) SRE was born at Google in 2003 when Ben Treynor and his team were tasked with solving scalability and reliability issues that come with managing massive infrastructures. Back then, Google’s systems were growing rapidly, and the traditional IT operations models were no longer sufficient. Google needed to address the reliability of its growing platform while also keeping up with the pace of innovation. The core idea behind SRE is tha...

There are several tools and platforms designed to facilitate AIOps practices within IT environments. These tools often combine data collection, analytics, and automation to help IT teams manage complex infrastructures more efficiently. Here's a list of some popular AIOps tools and platforms: 1. Splunk : Known for its powerful log management and analytics capabilities, Splunk offers AIOps features such as anomaly detection, predictive analytics, and automated incident response. It enables organizations to collect and analyze data from various sources to gain insights into IT operations. 2. Dynatrace : Dynatrace provides full-stack monitoring, AI-powered root cause analysis, and automatic anomaly detection. It supports cloud-native and hybrid environments, offering real-time visibility into applications, infrastructure, and user experience. 3. New Relic : New Relic combines observability and AIOps capabilities, providing insights across applications, infrastructure, and customer expe...