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High-Throughput and Predictable VM Scheduling for High-Density Workloads

Manohar Vanga
SWS Student Defense Talks - Thesis Proposal
Public Audience

Date, Time and Location

Monday, 26 March 2018
-- Not specified --


In the increasingly competitive public-cloud marketplace, improving the efficiency of data centers is a major concern. One way to improve efficiency is to consolidate as many virtual machines (VMs) onto as few physical cores as possible, provided that customers' performance expectations are not violated. However, as a prerequisite for supporting increased VM densities, the hypervisor’s VM scheduler must allocate processor time efficiently and in a timely fashion. Unfortunately, we show that contemporary VM schedulers leave substantial room for improvements in both regards when facing challenging high-VM-density workloads that frequently trigger the VM scheduler.

We identify the root causes of this inability to support high-density VM scenarios to be (i) high runtime overheads and (ii) unpredictable scheduling heuristics. To better support high VM densities, we propose Tableau, a VM scheduler that guarantees a minimum processor share and a maximum bound on scheduling delay for every VM in the system. Tableau achieves this by combining a low-overhead, core-local, table-driven dispatcher within the hypervisor with a fast on-demand table-generation procedure (triggered asynchronously upon VM creation and teardown) that employs scheduling techniques typically used in hard real-time systems.

In an evaluation comparing Tableau against three current Xen schedulers on a 16-core Intel Xeon machine, Tableau is shown to improve both tail latency (e.g., a 17x reduction in maximum ping latency compared to Credit, Xen's default scheduler) and throughput (e.g., 1.6x peak web server throughput compared to Xen's real-time RTDS scheduler when serving 1 KiB files with a 100 ms SLA).
While Tableau solves one piece of the unpredictability puzzle, namely the VM scheduler, there are other sources of unpredictability that arise in a shared, high-density setting. We therefore propose extensions of Tableau to deal with two other major sources of unpredictability: LLC interference caused by other VMs co-located on the same CPU socket, and delays that arise due to I/O scheduling.


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Maria-Louise Albrecht, 04/05/2018 14:11
Maria-Louise Albrecht, 03/23/2018 15:18 -- Created document.