Hybrid cloud provides an attractive solution to microservices for better resource elasticity. A subset of application components can be offloaded from the on-premises cluster to the cloud, where they can readily access additional resources. However, the selection of this subset is challenging because of the large number of possible combinations. A poor choice degrades the application performance, disrupts the critical services, and increases the cost to the extent of making the use of hybrid cloud unviable. This paper presents Atlas, a hybrid cloud migration advisor. Atlas uses a data-driven approach to learn how each user-facing API utilizes different components and their network footprints to drive the migration decision. It learns to accelerate the discovery of high-quality migration plans from millions and offers recommendations with customizable trade-offs among three quality indicators: end-to-end latency of user-facing APIs representing application performance, service availability, and cloud hosting costs. Atlas continuously monitors the application even after the migration for proactive recommendations. Our evaluation shows that Atlas can achieve 21% better API performance (latency) and 11% cheaper cost with less service disruption than widely used solutions.

AIOps can provide essential value for data lake-houses as lakehouses pose complex operational challenges for Site Reliability Engineers (SRE). This paper proposes that the unified approach of data lakehouses creates a unique opportunity for unified data resiliency management. We focus on AIOps applied to disaster recovery and backup/restore. In particular, we focus on managing data lakehouse hardware resources to ensure that lakehouse data Recovery Point Objectives (RPO) are met with a high degree of accuracy. The goal is to warn an SRE about an impending RPO violation and to suggest adding given amounts of hardware resources before a given time to avoid violation of the lakehouse data’s RPO. We claim AIOps can achieve this goal with an ensemble of machine learning and time series analysis.

Despite the reliability of modern disks, recent studies have made it clear that a new class of faults, UndetectedDisk Errors (UDEs) also known as silent data corruption events, become a real challenge as storage capacity scales. While RAID systems have proven effective in protecting data from traditional disk failures, silent data corruption events remain a significant problem unaddressed by RAID. We present a fault model for UDEs, and a hybrid framework for simulating UDEs in large-scale systems. The framework combines a multi-resolution discrete event simulator with numerical solvers. Our implementation enables us to model arbitrary storage systems and workloads and estimate the rate of undetected data corruptions. We present results for several systems and workloads, from gigascale to petascale. These results indicate that corruption from UDEs is a significant problem in the absence of protection schemes and that such schemes dramatically decrease the rate of undetected data corruption.