QoS improvement in multi-cloud system: installation cost optimization strategy

Installation costs in multi-cloud systems are costs incurred during the configuration and deployment of applications across multiple cloud providers and computing resources. These expenses include elements like resource supply, data transport, and configuration overhead. Optimizing installation costs is critical for optimum resource utilization and cost-effectiveness in multi-cloud systems. Effective solutions for reducing installation costs can improve the overall performance and economic feasibility of multi-cloud installations. The goal of the study is to increase the quality of service in multi-cloud systems with an Installation cost optimization strategy. With the growing variety of cloud providers and varieties of computing resources, selecting the best resources to fulfill different QoS standards has become increasingly difficult. We present an effective and cost-effective approach named the resilient ant search optimisation (RASO) algorithm, as a solution to the problem of multi-cloud application installation, which uses a local search strategy to reduce total installation expenses and response time. We conducted a comparison analysis with other conventional multi-objective optimization methodologies with baseline datasets. The experimental findings demonstrate that the RASO has better adaptability to varied cloud environments, thus demonstrating its usefulness as a cost-effective strategy for improving QoS in multi-cloud systems.