EPBLA: energy-efficient consolidation of virtual machines using learning automata in cloud data centers

Abstract

High demand for computational power by business, science, and applications has led to the creation of large-scale data centers that consume enormous amounts of energy. This high energy consumption not only imposes a significant operating cost but also has a negative impact on the environment (greenhouse gas emissions). A promising solution to reduce the amount of energy used by data centers is the consolidation of virtual machines (VMs) that allows some hosts to enter low consuming sleep modes. Dynamic migration (replacement) of VMs between physical hosts is an effective strategy to achieve VM consolidation. Dynamic migration not only saves energy by migrating the VMs hosted by idle hosts but can also avoid hotspots by migrating VMs from over-utilized hosts. In this paper, we presented a new approach, called extended-placement by learning automata (EPBLA), based on learning automata for dynamic replacement of VMs in data centers to reduce power consumption. EPBLA consists of two parts (i) a linear reward penalty scheme which is a finite action-set learning automata that runs on each host to make a fully distributed VM placement considering CPU utilization as a metric to categorize the hosts, and (ii) a continuous action-set learning automata as a policy for selecting an underload host initiating the migration process. A real-world workload is used to evaluate the proposed method. Simulation results showed the efficiency of EPBLA in terms of reduction of energy consumption by 20% and 30% compared with PBLA and Firefly, respectively.