Simplifying the task of resource management and scheduling for
customers, while still delivering complex Quality-of-Service (QoS),
is key to cloud computing. Many autoscaling policies have been
proposed in the past decade to decide on behalf of cloud customers
when and how to provision resources to a cloud application utilizing
cloud elasticity features. However, in prior work, when a new policy
is proposed, it is seldom compared to the state-of-the-art, and is
often compared only to static provisioning using a predefined QoS
target. This reduces the ability of cloud customers and of cloud
operators to choose and deploy an autoscaling policy. In our work,
we conduct an experimental performance evaluation of autoscaling
policies, using as application model workflows, a commonly used
formalism for automating resource management for applications
with well-defined yet complex structure. We present a detailed
comparative study of general state-of-the-art autoscaling policies,
along with two new workflow-specific policies. To understand the
performance differences between the 7 policies, we conduct various
forms of pairwise and group comparisons. We report both individual
and aggregated metrics. Our results highlight the trade-offs between
the suggested policies, and thus enable a better understanding of the current state-of-the-art.
Original languageEnglish
Title of host publication8th ACM/SPEC Int'l Conference in Performance Engineering (ICPE)
PublisherACM DL
Number of pages86
StatePublished - 2017

ID: 29617131