Abstract

I will present my last PhD work accepted to ACM SOCC 2019 conference. Please find the abstract below.

Abstract: Resource under-utilization is common in cloud data centers. Prior works have proposed improving utilization by running provider workloads in the background, colocated with tenant workloads. However, an important challenge that has still not been addressed is considering the tenant workloads as a black-box. We present Scavenger, a batch workload manager that opportunistically runs containerized batch jobs next to black-box tenant VMs to improve utilization. Scavenger is designed to work without requiring any offline profiling or prior information about the tenant workload. To meet the tenant VMs’ resource demand at all times, Scavenger dynamically regulates the resource usage of batch jobs, including processor usage, memory capacity, and network bandwidth. We experimentally evaluate Scavenger on two different testbeds using latency-sensitive tenant workloads colocated with Spark jobs in the background and show that Scavenger significantly increases resource usage without compromising the resource demands of tenant VMs.