  Cloud computing is driven by economies of scale. A cloud system uses
virtualization technology to provide cloud resources (e.g. CPU, memory) to
users in form of virtual machines. Virtual machine (VM), which is a sandbox for
user application, fits well in the education environment to provide
computational resources for teaching and research needs. In resource
management, they want to reduce costs in operations by reducing expensive cost
of electronic bill of large-scale data center system. A lease-based model is
suitable for our Virtual Computing Lab, in which users ask resources on a lease
of virtual machines. This paper proposes two host selection policies, named MAP
(minimum of active physical hosts) and MAP-H2L, and four algorithms solving the
lease scheduling problem. FF-MAP, FF-MAP-H2L algorithms meet a trade-off
between the energy consumption and Quality of Service (e.g. performance). The
simulation on 7-day workload, which converted from LLNL Atlas log, showed the
FF-MAP and FF-MAP-H2L algorithms reducing 7.24% and 7.42% energy consumption
than existing greedy mapping algorithm in the leasing scheduler Haizea. In
addition, we introduce a ratio \theta of consolidation in HalfPI-FF-MAP and
PI-FF-MAP algorithms, in which \theta is \pi/2 and \pi, and results on their
simulations show that energy consumption decreased by 34.87% and 63.12%
respectively.
