High reliable real-time bandwidth scheduling for virtual machines with hidden Markov predicting in telehealth platform

Reliable and high-performance resource scheduling for Virtual Machines (VMs) in cloud can guarantee the efficiency of remote rescue with telehealth system. When a local disaster, e.g. earthquake and tsunami, happens in a densely populated area, the surging health care demand leads to the increasing workload in Data Centers (DCs) by storing and delivering a mass of patients' information and real-time physiology signals. However, the current self-adaptive scheduling methods cannot provide a high-accuracy recognizing of the two conditions: urgency or normal, which would procrastinate the system into a high-performance status, while the best rescue time is lost. In this paper, we propose a Primary Node-based architecture for typical telehealth service on cloud, which takes into account both storage and delivery efficiency. We also design a novel algorithm to predicting and allocating the future bandwidth of all VMs in the telehealth service context. This method is able to dynamically adjust each parameter of a Hidden Markov Model (HMM) through collecting the historical information of the bandwidth workload. After we predict the future bandwidth consumption of VMs, a high-performance scheduling method is used to adjust the bandwidth to each VM for health care service. The simulation results prove that this algorithm provides a high-accurate prediction, which guides the allocating module to make decision before the request burst comes. Nevertheless, our algorithm improves the reliability of telehealth services for storing and delivering patients' information among DCs. We design a service-oriented architecture (SOA) for telehealth service in cloud.We propose a data coherence protocol among distributed Dcs to measure the bandwidth demand.A HMM-based bandwidth predicting algorithm is designed to help cloud broker manage the network resource effectively.A bandwidth allocating module is implemented for bandwidth allocating and recycling.The reliability and sensitivity of the model are tested, and the performance is much better than other four traditional predicting approaches.