Simulation of Cloud Resource Allocation

Cloud computing allows for large scale sharing of computing resources in a technically and administratively scalable manner. It is, therefore, quickly adopted by a large number of companies (not limited to the IT sector) also resulting in a high end-user adoption. While cloud computing already enabled a plethora of new applications and business models, expertise to exploit its full potential is still vastly missing [1]. While companies search for cloud experts this topic is not always covered in academia. To bridge this gap, this thesis focuses on a fundamental aspect of cloud computing, thereby allowing the student to gain industry-relevant competence.

In clouds resources are deployed by virtual machines (VMs), i.e., if a cloud customer wants to run a job in a cloud, an according VM that executes the job is started. Because clouds apply statistical multiplexing, different resources (CPU, RAM, disk access, bandwidth) may get scarce, in which case VMs receive less resources then requested. Scarcity effects of different resources on VM performance and behaviour is currently researched at the CSG. Thus, the next step is implementing these findings as a simulator, which is the student's primary task in this thesis. In particular, the simulator shall be able to simulate resource contention of VMs on a host, i.e., physical machine (PM). Since a cloud is comprised by numerous PMs, the simulator must also be able to simulate VM scheduling (the mapping of VMs to PMs). The simulator may either be implemented by a CloudSim module or an OpenStack fake driver.

[1] L. Schubert and K. Jeffery. Advances in Clouds. European Commission, Publications Office of the European Union, Luxembourg, 2012.