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BFD-NN: best fit decreasing-neural network for online energy-aware virtual machine allocation problems

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Published:08 December 2016Publication History

ABSTRACT

High performance computing (HPC) clouds consume a lot of energy (kWh); therefore reducing energy consumption is a high priority for any cloud provider. This paper studies the applications of vector bin packing heuristic and Neural Network (NN) to allocate virtual machines (VMs) onto physical machines (PMs) that minimizes total energy consumption of the physical machines. In our scenario, a list of virtual machines from request queue needs to assign to system for every interval time (T) and minimize total energy consumption. We proposed Best Fit Decreasing Neural Network (BFD-NN), which contains an evaluation function (f) that finds the most efficient physical machine for each VM from requests in system. We also proposed a process to optimize weight of coefficients in f for every request which users submit to the system based on information of users' requests in the past by using Parallel Genetic Algorithm (PGA) and Neural Network. Our method is a new approach because it not only uses knowledge from requests of users but also considers time dimension of virtual machines. Two job parallel workload models in Parallel Workloads Archive are used to evaluate our approach. The simulation results illustrate that BFD-NN could reduce up to 15% total energy consumption compared with state-of-the-art heuristics (such as Best Fit and First Fit Decreasing) in online allocation virtual machines.

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