Cloud computing has paved the way to the flexible
deployment of software applications. This flexibility offers service
providers a number of options to tailor their deployments to the
observed and foreseen customer workloads, without incurring
in large capital costs. However, cloud deployments pose novel
challenges regarding application reliability and performance. Ex-
amples include managing the reliability of deployments that make
use of spot instances, or coping with the performance variability
caused by multiple tenants in a virtualized environment.
In this paper we introduce
L
INE
, a tool for performance and
reliability analysis of software applications.
L
INE
solves Layered
Queueing Network (LQN) models, a popular class of stochastic
models in software performance engineering, by setting up and
solving an associated system of ordinary differential equations.
A key differentiator of
L
INE
compared to existing solvers for
LQNs is that
L
INE
incorporates a model of the environment the
application operates in. This enables the modeling of reliability
and performance issues such as resource failures, server break-
downs and repairs, slow start-up times, resource interference due
to multi-tenancy, among others. This paper describes the
L
INE
tool, its support for performance and reliability modeling, and
illustrates its potential by comparing
L
INE
predictions against
data obtained from a cloud deployment. We also illustrate the
applicability of
L
INE
with a case study on reliability-aware
resource provisioning.