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Tier-Centric Resource Allocation in Multi-Tier Cloud Systems

Tier-Centric Resource Allocation in Multi-Tier Cloud Systems In IT service delivery and support, the cloud paradigm has introduced the problem of IT resource over-provisioning through rapid automation (or orchestration) of manual IT operations. Due to the elastic nature of cloud computing, this shortcoming ends up significantly reducing the real benefit, viz., the cost-effectiveness of cloud adoption for Cloud Service Consumers (CSC). Similarly, detecting and eliminating such over-provisioning without affecting the quality of service (QoS) is extremely difficult forCloud Service Providers (CSPs) since they have no visibility into the actual performance of business service but only into the IT services (cloud resources) . In this paper, we propose T-BICA (Tier-centric Business Impact and Cost Analysis), a tier-centric optimal resource allocation algorithm, to address the problem of rapid provisioning of IT resources in modern enterprise cloud environments (private cloud), through extensive data gathering and performance analyses of business services in a simulated environment emulating a mature cloud service provider. Also, we have derived improved analytics to address the issues and to accelerate real cloud adoption for large enterprises within the context of meeting (or exceeding) business service level objectives (SLOs) and minimizing the cloud subscription cost (OpEx) for the business. While investigating the problem, we have ensured that we consider the time and the cost of delivering business service in medium- to large-size enterprise environments, quantifying the negative impact of IT resource over-provisioning (due to highly mature IT services centric orchestration capabilities) syndrome on the business, and how the suggested cloud analytics could assist in reducing total cost of ownership (TCO) of the business service. From our analysis of the test data, we have observed that our suggested approach and analytic reduces the cost of delivering business service by 65.19%, and improves the performance (total time to de- iver) by 74.18% when compared to existing modern cloud management and resource provisioning approach. The proposed approach also dramatically reduces upfront costs (CapEx) for CSPs (from capacity procurement and management point of view) through efficient on-demand resource de-provisioning, without affecting business SLOs and IT service level agreements (SLAs).

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