Cloud Resource Management Framework Using Monarch Butterfly Harmony Search And Case Based Reasoning - Ahmed Mohamed Ghetas, Mohamed Rezk (2017)
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.