Harvesting idle CPU resources for desktop grid computing while limiting the slowdown generated to end-users

We address the challenge of both harvesting idle CPU resources on off-the-shelf desktops donated to Desktop Grid Computing while at once limiting the slowdown generated to the resource owner, also known as end-user, to customized values. In this context, slowdown is studied as the increase in completion times of end-user tasks while a Desktop Grid harvests idle CPU resources by executing CPU intensive workloads. To achieve this, we deploy two Desktop Grids, one virtualization-based (UnaCloud) and one agent-based (BOINC). We then quantify the slowdown generated to simultaneously-running, end-user tasks. The results show that dynamic performance and energy-efficient technologies, specifically overclocking features, directly affect the slowdown generated to the end-user when incorporated into the processor used by the Desktop Grid. Furthermore, we propose, implement, and test a first set of resource allocation policies for the BOINC client in order to effectively harvest idle CPU resources while avoiding to exceed a customizable slowdown limit.