Anne Holler

An analysis of disk performance in VMware ESX server virtual machines

VMware ESX Server is a software platform that efficiently multiplexes the hardware resources of a server among virtual machines. This paper studies the performance of a key component of the ESX Server architecture: its storage subsystem. We characterize the performance of native systems and virtual machines using a series of disk microbenchmarks on several different storage systems. We show that the virtual machines perform well compared to native, and that the I/O behavior of virtual machines closely matches that of the native server. We then discuss how the microbenchmarks can be used to estimate virtual machine performance for disk-intensive applications by studying two workloads: a simple file server and a commercial mail server.

CloudPowerCap: Integrating Power Budget and Resource Management across a Virtualized Server Cluster

In many datacenters, server racks are highly underutilized. Rack slots are left empty to keep the sum of the server nameplate maximum power below the power provisioned to the rack. And the servers that are placed in the rack cannot make full use of available rack power. The root cause of this rack underutilization is that the server nameplate power is often much higher than can be reached in practice. To address rack underutilization, server vendors are shipping support for per-host power caps, which provide a server-enforced limit on the amount of power that the server can draw. Using this feature, datacenter operators can set power caps on the hosts in the rack to ensure that the sum of those caps does not exceed the rack’s provisioned power. While this approach improves rack utilization, it burdens the operator with managing the rack power budget across the hosts and does not lend itself to flexible allocation of power to handle workload usage spikes or to respond to changes in the amount of powered-on server capacity in the rack. In this paper we present CloudPowerCap, a practical and scalable solution for power budget management in a virtualized environment. CloudPowerCap manages the power budget for a cluster of virtualized servers, dynamically adjusting the per-host power caps for hosts in the cluster. We show how CloudPowerCap can provide better use of power than per-host static settings, while respecting virtual machine resource entitlements and constraints. Keywords-power cap; resource management; virtualization; cloud computing

Crowdsourced Resource-Sizing of Virtual Appliances

Using a population of VMware Virtual Center Virtual Appliances (VCVA) and their respective workloads we de- scribe techniques for constructing a model of their resource consumption and performance, specially memory requirements, and average operation-latency by mining logs of application (VCVA) performance. We use our model to provide sizing recommendations for the virtual appliance and identify features that can be used to provide rough estimates of expected memory consumption. We show results of better than 70% prediction accuracy (recall) for predicting Physical Memory Usage and better than 80% prediction accuracy (recall) for predicting the average latency of work- load operations. We describe modeling techniques from statistical machine learning that are amenable to representing complex, non-linear systems. Further, via the choice of techniques, we present an approach for reasoning about the limitations of our model, i.e., identifying when (and why) our model is expected to perform well and poorly.