Adaptive Multivariable Control for Multiple Resource Allocation of Service-Based Systems in Cloud Computing

Abstract

Service-based systems resource allocation in cloud computing is a key method of meeting service requests because service request workloads and resource demands change over time. When coping with dynamic fluctuating service requests and resource demands, adaptive resource allocation to ensure the quality of service (QoS) with the lowest resource consumption becomes challenging. In cloud computing, services share the same resource pool and compete for critical resources, such as CPU and memory resources. Because services need arbitrary resource combinations, focusing on a single resource may lead to excessive or deficient resource allocations or even service request failures. Due to the shared nature of cloud computing, QoS may be impacted by interference with co-hosted services. In this paper, we propose an adaptive control approach for resource allocation that adaptively reacts to dynamic request workloads and resource demands. The multivariable control is adopted to allocate multiple resources for multiple services according to the dynamic fluctuating requests and considers the interference between co-hosted services, thereby ensuring QoS even if the resource pool is insufficient. The comparative experiments show that the proposed approach can meet service requests and can improve resource utilization regardless of whether the resource pool is sufficient.