## 2 Related Research  
As noted in the introduction, real-time diagnostics of offered services remains a challenge. Assistive frameworks, such as Troubleshooting Workbench (TW) by Aria Operations [1], which provide intelligent event/alert consolidation methods of guessing the origin of issues, target this critical task from different angles. TW relies on discovery of important and relevant changes occurring within a delta time-and-topology scope of the cloud infrastructure hierarchy that might provide evidence about the source of the problem, while the alert grouping concepts rely on helping users to focus on the larger incidents to make inference of the root issues easier, as well as helping to look into the problem in relation to co-occurring events across time and infra/app topology axes. However, all these methods are too inherently limited in automation capacities and intelligence power to perform deep and targeted RCA for alerts.  
Other event management vendors, such as Big Panda (Redwood City, CA, USA) [9], Moogsoft (San Francisco, CA, USA) [10], and Pager Duty (San Francisco, CA, USA) [11], have adopted the consolidated insight and incident discovery strategy but also built a vision of human-driven guidance of alerts and incidents consisting of those atomic events for training supervised RCA models. An important related approach in the industry is represented by InfoSight (Miami Lakes, FL, USA) [12] by HPE, an AI-powered autonomous operations service applying analytics from global learning with a self-managing, self-healing, and self-optimizing vision for cloud applications. In this regard, ProbRCA was proposed to realize self-support for cloud management offerings with explainable features. It applies cross-customer user and developer feedback and trains accurate models over time to use them to recommend problem resolutions while interpreting/justifying those measures.  
In various research initiatives (see [13]), ML methods underly the automation of the management of complex data center applications (built upon many networked objects such as virtual machines (VMs), hosts, datastores, etc.) based on large volumes of data measured from those environments for complete monitoring and observability. At the same time, cloud services must be furnished with efficient self-diagnostics capabilities for business continuity and availability to avoid/eliminate time-consuming analysis of issues by product support specialists. This is a critical problem within self-driving data centers [14]. Moreover, interpretable models (see [15; 16]) in RCA are preferable compared to their black-box versions to produce justified recommendations to users and mitigate potential impacts and risks induced by those recommendations. There are specific use cases (e.g., [17; 18; 19; 20]) recently modeled by researchers in the domain of intelligent cloud applications relevant to our current study. In some related areas, such as cellular networks [21] and cloud databases [22], authors perform domain-specific modeling for similar problem solutions, which, in our use cases, are not readily achievable because of many factors, including a lack of labeled/annotated datasets, which are hard to obtain for cloud infrastructures. In this regard, we investigate more universal ways to build RCA models while adopting self-supervised strategies in training those models.