Reinforcement Learning (RL) has demonstrated a great potential for automatically solving decision-making problems in complex, uncertain environments. RL proposes a computational approach that allows learning through ...

Autoscaling strategies achieve efficient and cheap executions of scientific workflows running in the cloud by determining appropriate type and amount of virtual machine instances to use while scheduling the tasks/data . ...

Autoscalers exploit cloud-computing elasticity to cope with the dynamic computational demands of scientific workflows. Autoscalers constantly acquire or terminate virtual machines (VMs) on-the-fly to execute workflows ...

The Cloud Computing paradigm is focused on the provisioning of reliable and scalable virtual infrastructures that deliver execution and storage services. This paradigm is particularly suitable to solve resource-greedy ...

Cloud Computing is becoming the leading paradigm for executing scientific and engineering workflows. The large-scale nature of the experiments they model and their variable workloads make clouds the ideal execution environment ...

Parameter Sweep Experiments (PSEs) are commonplace to perform computer modelling and simulation at large in the context of industrial, engineering and scientific applications. PSEs require numerous computational resources ...

An important feature of cloud computing is its elasticity, that is, the ability to have resource capacity dynamically modified according to the current system load. Auto-scaling is challenging because it must account for ...