When legendary computer scientist Jim Gray accepted the Turing Award in 1999, he laid out a dozen long-range information technology research goals. One of those goals called for the creation of trouble-free server systems or, in Gray’s words, to “build a system used by millions of people each day and yet administered and managed by a single part-time person.”
Gray envisioned a self-organizing “server in the sky” that would store massive amounts of data, and refresh or download data as needed. Today, with the emergence and rapid advancement of artificial intelligence (AI), machine learning (ML) and cloud computing, and Microsoft’s development of Cloud Intelligence/AIOps, we are closer than we have ever been to realizing that vision—and moving beyond it.
Over the past fifteen years, the most significant paradigm shift in the computing industry has been the migration to cloud computing, which has created unprecedented digital transformation opportunities and benefits for business, society, and human life.
The implication is profound: cloud computing platforms have become part of the world’s basic infrastructure. As a result, the non-functional properties of cloud computing platforms, including availability, reliability, performance, efficiency, security, and sustainability, have become immensely important. Yet the distributed nature, massive scale, and high complexity of cloud computing platforms—ranging from storage to networking, computing and beyond—present huge challenges to building and operating such systems.
What is Cloud Intelligence/AIOps?
Cloud Intelligence/AIOps (“AIOps” for brevity) aims to innovate AI/ML technologies to help design, build, and operate complex cloud platforms and services at scale—effectively and efficiently.
AIOps has three pillars, each with its own goal:
- AI for Systems to make intelligence a built-in capability to achieve high quality, high efficiency, self-control, and self-adaptation with less human intervention.
- AI for Customers to leverage AI/ML to create unparalleled user experiences and achieve exceptional user satisfaction using cloud services.
- AI for DevOps to infuse AI/ML into the entire software development lifecycle to achieve high productivity.
Where did the research on AIOps begin?
Gartner, a leading industry analyst firm, first coined the term AIOps (Artificial Intelligence for IT Operations) in 2017. According to Gartner, AIOps is the application of machine learning and data science to IT operation problems. While Gartner’s AIOps concept focuses only on DevOps, Microsoft’s Cloud Intelligence/AIOps research has a much broader scope, including AI for Systems and AI for Customers.
The broader scope of Microsoft’s Cloud Intelligence/AIOps stems from the Software Analytics research we proposed in 2009, which seeks to enable software practitioners to explore and analyze data to obtain insightful and actionable information for data-driven tasks related to software and services. We started to focus our Software Analytics research on cloud computing in 2014 and named this new topic Cloud Intelligence (Figure 1). In retrospect, Software Analytics is about the digital transformation of the software industry itself, such as empowering practitioners to use data-driven approaches and technologies to develop software, operate software systems, and engage with customers.
Figure 1: From Software Analytics to Cloud Intelligence/AIOps
What is the AIOps problem space?
There are many scenarios around each of the three pillars of AIOps. Some example scenarios include predictive capacity forecasting for efficient and sustainable services, monitoring service health status, and detecting health issues in a timely manner in AI for Systems; ensuring code quality and preventing defective build deployed into production in AI for DevOps; and …….