"Fast, Elastic Storage for the Cloud"
Presented By: Ana Klimovic, Stanford University
Cloud computing promises high performance, cost-efficiency, and elasticity, three essential goals when processing exponentially growing datasets. To meet these goals, cloud platforms must provide each application with the right amount and balance of compute and fast storage resources (e.g., NVMe Flash). This is challenging today because server machines have a fixed ratio of compute to storage resources, remote access to fast storage leads to significant performance and cost overheads, and storage requirements vary significantly over time and across applications.
This talk will focus on how to build high performance, cost-effective, and easy-to-use cloud storage systems. First, I will discuss how to provide fast access to remote Flash storage so that the balance of compute and storage allocation is not limited by the physical characteristics of server hardware. I will present ReFlex, a custom network-storage operating system that provides fast access to modern Flash storage over commodity cloud networks. ReFlex enables storage devices to be shared among multiple tenants with predictable performance. Second, I will discuss how to implement intelligent allocation of storage resources. I will present Pocket, a distributed storage service that combines the fast remote data access in ReFlex with automatic resource allocation for serverless analytics workloads. I will also briefly discuss the potential of using machine learning in resource allocation.
Ana Klimovic is a Ph.D. candidate in the Electrical Engineering Department at Stanford University. Her research interests are in computer systems and computer architecture. Ana is particularly interested in designing and implementing high performance, resource-efficient computer systems for cloud computing. As part of her research, she has collaborated with companies such as Facebook, Microsoft, and IBM. Before coming to Stanford, Ana earned her Bachelor’s degree in Engineering Science at the University of Toronto. She is a Microsoft Research Ph.D. Fellow, Stanford Graduate Fellow, and Accel Innovation Scholar.
Joint with the Dept of Electrical and Computer Engineering