Recent demands for storing and querying big data have revealed various shortcomings of
traditional relational database systems. This, in turn, has led to the emergence of a new kind …

Resource management in a cloud environment is a hard problem, due to: the scale of
modern data centers; the heterogeneity of resource types and their interdependencies; the …

Modern resource management frameworks for largescale analytics leave unresolved the
problematic tension between high cluster utilization and job's performance predictability …

Recent years have seen the massive migration of enterprise applications to the cloud. One
of the challenges posed by cloud applications is Quality-of-Service (QoS) management …

Dynamically adjusting the number of virtual machines (VMs) assigned to a cloud application
to keep up with load changes and interference from other uses typically requires detailed …

Replicating data across multiple data centers allows using data closer to the client, reducing
latency for applications, and increases the availability in the event of a data center failure …

A longstanding goal of SSD virtualization has been to provide performance isolation
between multiple tenants sharing the device. Virtualizing SSDs, however, has traditionally …

Developers use Machine Learning (ML) platforms to train ML models and then deploy these
ML models as web services for inference (prediction). A key challenge for platform providers …

We study storage performance in over 450,000 disks and 4,000 SSDs over 87 days for an
overall total of 857 million (disk) and 7 million (SSD) drive hours. We find that storage …

A database management system (DBMS) serving a cloud platform must handle large
numbers of application databases (or tenants) that are characterized by diverse schemas …