Online hosed services are what is referred to as Cloud Computing. Access to these services is via
the internet. h shifts the traditional IT resource ownership model to renting. Thus, high cost of
infrastructure cannot limit the less privileged from experiencing the benefits that this new
paradigm brings. Therefore, c loud computing provides flexible services to cloud user in the form
o f software, platform and infrastructure as services. The goal behind cloud computing is to provide
computing resources on-demand to cloud users efficiently, through making data centers as friendly
to the environment as possible, by reducing data center energy consumption and carbon emissions.
With the massive growth of high performance computational services and applications, huge
investment is required to build large scale data centers with thousands o f centers and computing
model. Large scale data centers consume enormous amount s of electrical energy. The
computational intensity involved in data center is likely to dramatically increase the difference
between the amount of energy required for peak periods and of T-peak periods in a cloud
computing data center. In addition to the overwhelming operational cost, the overheating caused
by high power consumption will affect the reliability o f machines and hence reduce their lifetime.
There fore, in order to make the best u e of precious electricity resources, it is important to know
how much energy will be required under a certain circumstance in a data center. Consequently,
this dissertation addresses the challenge by developing and energy-efficient model and a
defragmentation algorithm. We further develop an efficient energy usage metric to calculate the
power consumption along with a Load Balancing Virtual Machine Aware Model for improving
delivery of no-demand resource in a cloud-computing environment. The load balancing model
supports the reduction of energy consumption and helps to improve quality of service. An
experimental design was carried out using cloud analyst as a simulation tool. The results obtained
show that the LBVMA model and throttled load balancing algorithm consumed less energy. Also,
the quality or service in terms of response time is much better for data centers that have more
physical machines. but memory configurations at higher frequencies consume more energy.
Additionally, while using the LBVMA model in conjunction with the throttled load balancing
algorithm, less energy is consumed. meaning less carbon is produced by the data center. / Thesis (M.Sc.(Computer Science) North-West University, Mafikeng Campus, 2013
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:nwu/oai:dspace.nwu.ac.za:10394/15831 |
| Date | January 2013 |
| Creators | Moemi, Thusoyaone Joseph |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Thesis |
Page generated in 0.0015 seconds