A Genetic Algorithm for Power-Aware Virtual Machine Allocation in Private Cloud

TL;DR

This paper introduces a genetic algorithm (GAPA) designed to optimize power-aware virtual machine allocation in private clouds, effectively reducing energy consumption without migration techniques, tailored for scheduled lab resources.

Contribution

The paper proposes a novel genetic algorithm for static VM allocation in private clouds, addressing energy efficiency without VM migration, tailored for scheduled resource needs.

Findings

GAPA reduces total energy consumption compared to baseline.

Simulation shows GAPA's effectiveness in scheduled lab environments.

GAPA outperforms traditional scheduling algorithms in energy efficiency.

Abstract

Energy efficiency has become an important measurement of scheduling algorithm for private cloud. The challenge is trade-off between minimizing of energy consumption and satisfying Quality of Service (QoS) (e.g. performance or resource availability on time for reservation request). We consider resource needs in context of a private cloud system to provide resources for applications in teaching and researching. In which users request computing resources for laboratory classes at start times and non-interrupted duration in some hours in prior. Many previous works are based on migrating techniques to move online virtual machines (VMs) from low utilization hosts and turn these hosts off to reduce energy consumption. However, the techniques for migration of VMs could not use in our case. In this paper, a genetic algorithm for power-aware in scheduling of resource allocation (GAPA) has been proposed to solve the static virtual machine allocation problem (SVMAP). Due to limited resources (i.e. memory) for executing simulation, we created a workload that contains a sample of one-day timetable of lab hours in our university. We evaluate the GAPA and a baseline scheduling algorithm (BFD), which sorts list of virtual machines in start time (i.e. earliest start time first) and using best-fit decreasing (i.e. least increased power consumption) algorithm, for solving the same SVMAP. As a result, the GAPA algorithm obtains total energy consumption is lower than the baseline algorithm on simulated experimentation.