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Mert Toslali is a Ph.D. student in the Electrical and Computer Engineering Department at Boston University. His thesis seeks to design automated analytics methods and systems for cloud applications that minimize dependency on expert knowledge, reduce time-to-solution, and help make applications more resilient. The thesis is divided into two research venues that cover two significant aspects of overcoming cloud performance unpredictability. In the first venue, they aim to demonstrate that dynamically adjusting instrumentation (i.e., automatically enabling dis- tributed traces) using statistically driven techniques help localize performance problems and provide the discriminate context (i.e., request workflows) needed for effective diagnosis. Second, they aim to show that learning-based methods considering both SLOs and business concerns, unlike existing solutions, improve the accuracy in online experimentation, hence the application performance beyond current limits.