Andreas Cederström MSE-2010:06, pp. 44. COM/School of Computing, 2010.
Context. When dealing with large data sets and heavy calculations the common solution is clusters, supercomputers or Grids of these two. However, there are ways of gaining large computational power by utilizing the unused cycles of regular home or office computers, this are referred to as Desktop Grids.
Objectives. In this study we review the current field of solutions for open source Desktop Grid computing capable of dealing with a heterogeneous set of clients and dynamic size of the Desktop Grid. We investigate current use, interest of use and priority of key attributes of Desktop Grids. Finally we want to show how time effective Desktop Grids are compared to execution on a single machine and in the process show effort needed to setup a Desktop Grid and start computing. The overall purpose of this study is to provide a path for industry organizations to take when taking the first step into Desktop Grid computing.
Methods. We use a systematic review to collect information of existing open source Desktop Grid solutions. Studies are selected based on inclusion criterions and a quality assessment. A survey questioner is used to assess industry usage, interest and prioritization of attributes of Desktop Grids. We will conduct an experiment to show execution speedup as well as setup effort.
Results. We found ten open source Desktop Grids fulfilling our requirements. The survey shows that Desktop Grids is used to a very little extent within industry while a majority of the participants state that there is an interest for Desktop Grids. As result of the experiment, we can say that we achieved very high speedup and that effort needed to setup a Desktop Grid is about 40 hours for one person with no prior experience to the selected Desktop Grid system.
Conclusions. We conclude that industry organizations have a possible need for Desktop Grids but in order to be more successful, Desktop Grid developers must put more effort into areas as automated testing and code compilation.