Improving energy consumption in cloud computing datacenters using a combination of energy-aware resource allocation and scheduling mechanism
Cloud datacenters consume huge amounts of electrical energy resulting in carbon dioxide emissions and high operating costs. In 2013, energy consumed by global datacenters was estimated to be between 1.1% and 1.5% of the worldwide energy usage and is predicted to grow further. This thesis introduc...
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| Format: | Thesis |
| Language: | English |
| Published: |
2017
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| Subjects: | |
| Online Access: | http://psasir.upm.edu.my/id/eprint/71108/1/FK%202017%2023%20-%20IR.pdf |
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| Summary: | Cloud datacenters consume huge amounts of electrical energy resulting in carbon
dioxide emissions and high operating costs. In 2013, energy consumed by global
datacenters was estimated to be between 1.1% and 1.5% of the worldwide energy
usage and is predicted to grow further. This thesis introduces a mechanism for
dynamic virtual machines (VMs) consolidation in cloud datacenters. The aim is
improving the utilization of computing resources that can decrease the number of
activated physical machines (PMs) to decrease energy consumption. The main target
is to design a combination of energy-aware resource allocation and scheduling
mechanism to decrease the overall energy consumption, and active PMs, besides
maximizing resource utilization and minimizing VM migration.
In this study, to improve the utilization of cloud resources and reduce the energy
consumption of datacenters, a combination of energy-aware resource allocation and
scheduling mechanism including DNA based Fuzzy Genetic Algorithm (DFGA) is
proposed. By designing a scheduling technique, cloud resources can be allocated
efficiently to reduce the energy consumption of the cloud datacenter. Nowadays, DNA
plays a vital role in many computing applications due to the massive processing
parallelism. In addition, using fuzzy theory in genetic algorithm reduces the iteration
of producing the population and assigning the suitable resources to the tasks-based
and task length in the node capacity. Therefore, using DNA based fuzzy genetic can
obtain the best chromosomes in a few iterations to maximize utilization and minimize
VM migration. For subsequent, the energy consumption of cloud computing
datacenter is reduced.
Energy consumption was analysed in idle and dynamic states of the server, depending
on the energy consumed, processes number and size of the data processed, and size of
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the data transmitted for each host.The experimental results were analysed in both
synthetic and real Google trace log environments. These experiments were conducted
with varying workloads and comparatively analysed through three different metrics:
overall energy consumption, resource utilization, and VM migration. The
experimental results of applying DFGA algorithm to real Google cloud trace logs
show that the energy consumption of the proposed work was 2.15 kWh which was
more efficient when compared to other works: Energy-aware Rolling Horizon
(EARH) (2.55 kWh), Modified Bit Field Decreasing (MBFD) and Minimization of
Migration (MM) (2.65 kWh). The percentage of the system's resource utilization was
82%, compared to other works: EARH (72.8%), MBFD and MM (70%). This study's
VMM (X1000) was 2, whereas EARH was 3.2 and MM was 5.
It can be concluded that the proposed combination of energy-aware resource
allocation and scheduling mechanism can reduce the total energy consumption of the
datacenters. The number of activated servers can be minimized by switching off the
idle PMs. The resource utilization ratio can be increased and the number of VM
migration can be minimized. Future works can apply the proposed mechanism to
other cloud platforms. |
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