Performance Constraint and Power-Aware Allocation For User Requests In Virtual Computing Lab (1210.1026v2)

Abstract: 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.