Abstract

This paper studies the preemption between programs running in different virtual machines on the same computer. One of the current monitoring methods consist of updating the average steal time through collaboration with the hypervisor. However, the average is insufficient to diagnose abnormal latencies in time-sensitive applications. Moreover, the added latency is not directly visible from the virtual machine point of view. The main challenge is to recover the cause of preemption of a task running in a virtual machine, whether it is a task on the host computer or in another virtual machine. We propose a new method to study thread preemption crossing virtual machines boundaries using kernel tracing. The host computer and each monitored virtual machine are traced simultaneously. We developed an efficient and portable trace synchronization method, which is required to account for time offset and drift that occur within each virtual machine. We then devised an algorithm to recover the root cause of preemption between threads at every level. The algorithm successfully detected interactions between multiple competing threads in distinct virtual machines on a multi-core machine.

Highlights

  • Cloud environments present advantages of increased flexibility and reduced maintenance cost through resource sharing and server consolidation [1]

  • For a task executing inside a virtual machine, the computation of the execution flow should be adjusted to take into consideration interactions between different operating systems through the usage of shared resources

  • Cloud computing and virtualization are evolving at a rapid pace

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Summary

Introduction

Cloud environments present advantages of increased flexibility and reduced maintenance cost through resource sharing and server consolidation [1]. As we present in section ‘Use cases’, the tools resulting from our study help the users to find the latency cause due to CPU sharing among virtual machines, as well as the actual threads that affect the completion time of a certain workload.

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