Context: In cloud computing, there is a multitude of definitions and metrics for scalability,
elasticity, and efficiency. However, stakeholders have little guidance for choosing fitting …

Today's infrastructure clouds provide resource elasticity (ie Auto-scaling) mechanisms
enabling self-adaptive resource provisioning to reflect variations in the load intensity over …

In only a decade, cloud computing has emerged from a pursuit for a service-driven
information and communication technology (ICT), becoming a significant fraction of the ICT …

Auto-scaling of Web applications is an extensively investigated issue in cloud computing. To
evaluate auto-scaling mechanisms, the cloud community is facing considerable challenges …

In the past decade, cloud computing has emerged from a pursuit for a service-driven
information and communication technology (ICT), into a signifcant fraction of the ICT market …

The growing amount of data generated at the edge of the network, eg, by Internet of Things
(IoT) devices, made it indispensable to relocate computational power close to the data …

Increasing availability and popularity of cloud Storage as a Service (STaaS) offers
alternatives to traditional on-line video entertainment models, which rely on expensive …

Auto-scaling is able to change the scale of an application at runtime. Understanding the
application characteristics, scaling impact as well as the workload, an auto-scaler aligns the …

In this chapter, we propose a list of metrics grouped by the MAPE-K paradigm for quantifying
properties of self-aware computing systems. This set of metrics can be seen as a starting …

In this chapter, we present a set of intuitively understandable metrics for characterizing the
elasticity of a cloud platform including ways to aggregate them. The focus is on IaaS clouds; …