Cloud Resource Management Framework Using Monarch Butterfly Harmony Search And Case Based Reasoning
Cloud services have evolved rapidly and some have adopted a multi-tier architecture for flexibility and reusability. Various rule- and model-based approaches have designed to manage quality of service for these services. A few of existing resource management approaches aim to increase the cloud p...
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| Format: | Thesis |
| Language: | English |
| Published: |
2017
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| Subjects: | |
| Online Access: | http://eprints.usm.my/63062/ http://eprints.usm.my/63062/1/Pages%20from%206-PHS18020062.pdf |
| Summary: | Cloud services have evolved rapidly and some have adopted a multi-tier architecture
for flexibility and reusability. Various rule- and model-based approaches have
designed to manage quality of service for these services. A few of existing resource
management approaches aim to increase the cloud provider (cp) service provisioning
profits. However, they are based on local search optimization algorithms, which may
not obtain the best resource provisioning decision in a large-scale cloud environment.
This research proposes a new resource optimization and provisioning (rop) framework
to detect, solve the bottlenecks, and satisfy the service-level qos requirements of several
multi-tier cloud services and to increase the cp service provisioning profits. The
rop framework consists of two main components: global resource optimizer (gro)
and resource identifier (ri). This research enhances the butterfly optimization algorithm
and plugs the resulting algorithm into the rop as a gro. In addition, a new ri
is developed using case-based reasoning and is then plugged into the rop framework.
To demonstrate the effectiveness of the proposed rop against rule- and model-based
approaches, a prototype running on a cloud platform is developed, and a workload generator
and multi-tier service model are adopted. |
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