An autonomic resource provisioning framework for efficient data collection in cloudlet-enabled wireless body area networks: a fuzzy-based proactive approach

Abstract

Integrating wireless body area networks (WBANs) with cloudlet introduces an edge-of-things computing environment for pervasive applications. The variation in the number of active WBANs nodes and its data transmission rate requires optimal computing resources to avoid performance degradation and data loss. We argue the research gap in terms of optimal resource provisioning that predicts and automatically adjusts the computing resources on the basis of sensory data volume and application’s type. In this paper, we propose a hybrid autonomic resource provisioning framework, which is the combination of autonomic computing, fuzzy logic control and linear regression model. The proposed framework is built over CloudSim toolkit with autonomic resource provisioning framework inspired by the cloud layer model. The effectiveness of the proposed approach is evaluated under a real workload trace. The experimental results show that the proposed approach minimizes the cost by at least 27% and SLA violations by at least 78% as compared to other approaches.