Dresden University of Technology
Infrastructure as a Service (IaaS) providers currently have no knowledge of the time frame customers intend to lease resources. However, scheduling in the absence of lease time information leads to wasted resources in times of decreasing demand. The authors explore how IaaS providers can use lease times to optimize resource allocation. They present two virtual machine scheduling algorithms to optimize the virtual-to-physical machine mapping taking lease time into account. Through simulation with synthetic and real-world workloads they evaluate the algorithms' potential to reduce the number of powered-up physical machines. Depending on data center size and request distribution the cumulative machine uptime is reduced by 28.4% to 51.5% when compared to round robin scheduling and by 3.3% to 16.7% when compared to first fit.