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Cloud computing is driven by economies of scale. A cloud system usesvirtualization technology to provide cloud resources (e.g. CPU, memory) tousers in form of virtual machines. Virtual machine (VM), which is a sandbox foruser application, fits well in the education environment to providecomputational resources for teaching and research needs. In resourcemanagement, they want to reduce costs in operations by reducing expensive costof electronic bill of large-scale data center system. A lease-based model issuitable for our Virtual Computing Lab, in which users ask resources on a leaseof virtual machines. This paper proposes two host selection policies, named MAP(minimum of active physical hosts) and MAP-H2L, and four algorithms solving thelease scheduling problem. FF-MAP, FF-MAP-H2L algorithms meet a trade-offbetween the energy consumption and Quality of Service (e.g. performance). Thesimulation on 7-day workload, which converted from LLNL Atlas log, showed theFF-MAP and FF-MAP-H2L algorithms reducing 7.24% and 7.42% energy consumptionthan existing greedy mapping algorithm in the leasing scheduler Haizea. Inaddition, we introduce a ratio \theta of consolidation in HalfPI-FF-MAP andPI-FF-MAP algorithms, in which \theta is \pi/2 and \pi, and results on theirsimulations show that energy consumption decreased by 34.87% and 63.12%respectively.

Performance Constraint and Power-Aware Allocation For User Requests In Virtual Computing Lab