A Threshold-based Cloud Resource Allocation Framework with Quality of Services Considerations - Husaini, Husaini; Misbullah, Alim; Farsiah, Laina (Transcendent Journal of Mathematics and Applications, 2023)
Allocating the number of resources needed by cloud applications is very crucial concern in the cloud environment. If the resource allocation is not managed precisely, the cloud services may starve during the peak load time or waste the resources during the off-peak time. Auto-scaling mechanism is one approach used in cloud environment in which service providers can maintain the resources and reduce waste resources by automatically increasing or decreasing them when needed. It is still difficult to predict the client-side experience which later will cause in decreasing performance because of lacking computing instances. This paper focuses on allocating resources at the application level for the efficient resource utilization and presents a novel cloud resource management framework. The proposed system monitored the end-users response time directly from client-side. Several thresholds were defined with Quality of Services (QoS) considerations which include response time and error rates sampling to optimize the decision of reallocating the virtual resources. The results dynamically allocate the virtual resources among the cloud applications based on their workload. Based on the experimental results, the recommendation threshold is 0.6 for the cloud system, as it can improve performance while minimizing costs.