Energy efficient scheduling and reduction of failure virtual machines with deadline constraints

Abstract

Cloud computing is a scale-based computing model, and requires more physical machines and consumes an extremely large amount of electricity, which will reduce the profit of the service providers and harm the environment. It is shown that the cost of energy consumed by a server during its lifetime will exceed the cost of server itself. Virtual machine scheduling is one of the most important and efficient technologies of reducing energy consumption in cloud. The main idea of scheduling VMs energy efficiently is placing them on only part of the physical machines and transforming the other ones into low power state (sleep or off). Existing energy efficient scheduling methods of virtual machines (VMs) in cloud cannot work well if the physical machines (PMs) are heterogeneous and their total power is considered, and typically do not use the energy saving technologies of hardware, such as dynamic voltage and frequency scaling (DVFS).Also, datacenters have to reduce the number of failed virtual machines (tasks) in order to achieve high quality service. To avoid these kinds of issues, this paper proposes an algorithm which reduces energy consumption in data centers and extends the deadline of virtual machines. The algorithm works as follows: There exists optimal frequency for a PM to process certain VMs, based on which the notion of optimal performance–power ratio is defined to weight the homogeneous PMs. The PM with higher optimal performance–power ratio will be assigned to VMs first to save energy. The process is divided into some equivalent schedule periods, in each of which VMs are allocated to proper PMs and each active core operates on the optimal frequency. After each period, the cloud should be reconfigured to consolidate the computation resources to further reduce the energy consumption and check for enough resources to complete the tasks within deadline. VMs having tasks which are not completed within the deadline can ask the user to extend th
deadline.If yes, reconfigure the cloud and allocate the incomplete VMs to the appropriate PM. Our proposed work reduces energy consumption, number of failed virtua machines effectively.