Today, even after more than a decade of the cloud computing revolution, users still do not have predictable performance for their applications, and the providers continue to suffer due to poorly utilized resources. Moreover, the environmental implications of these inefficiencies are dire: Cloud-hosted datacenters consume as much power as a city of a million people and emit roughly as much CO2 as the airline industry. Fighting these implications, especially in the post Moore's law era, is crucial.
My work points out that the root of these inefficiencies is the gap between the users and the providers: user economy and performance vs. resource utilization for providers to achieve revenue. To overcome this divide, my research brings out two key insights for building systems that render the cloud smart, a cloud that is easy-to-use, adaptive, and efficient. First, we must build systems with interfaces that are intuitive and expressive for users. Such interfaces should open a dialog between users and providers, allowing users to specify high-level application goals, and transfer the responsibility of making low-level resource management decisions to the providers. This opens an opportunity for providers to optimize the use of their resources while still best aligning with user goals. Second, to make the resource management decisions in an adaptive manner in increasingly complex cloud systems, we must leverage Data-Driven or Machine Learning (ML) models. In doing so, my work uses and develops ML algorithms and studies the challenges that such data-driven models raise in the context of systems: modeling uncertainty, cost of training, and generalizability. In this talk, I will present two systems, INFaaS and PARIS, designed to demonstrate the efficacy of these two key insights. These systems represent key steps towards building a smart cloud: they significantly simplify the use of cloud, improve resource efficiency while meeting user goals.
Neeraja Yadwadkar is a post-doctoral research fellow in the Computer Science Department at Stanford University, working with Christos Kozyrakis. She is a Cloud Computing Systems researcher, with a strong background in Machine Learning (ML). Neeraja's research focuses on using and developing ML techniques for systems, and building systems for ML. Neeraja graduated with a PhD in Computer Science from the RISE Lab at University of California, Berkeley, where she was advised by Randy Katz and Joseph Gonzalez. Before starting her PhD, she received her masters in Computer Science from the Indian Institute of Science, Bangalore, India, and her bachelors from the Government College of Engineering, Pune, India.
Faculty Host: Robin Kravets