QoS-Aware Dynamic Resource Management in Heterogeneous Mobile Cloud Computing Networks

In mobile cloud computing(MCC) systems,both the mobile access network and the cloud computing network are heterogeneous,implying the diverse configurations of hardware,software,architecture,resource,etc.In such heterogeneous mobile cloud(HMC) networks,both radio and cloud resources could become the system bottleneck,thus designing the schemes that separately and independently manage the resources may severely hinder the system performance.In this paper,we aim to design the network as the integration of the mobile access part and the cloud computing part,utilizing the inherent heterogeneity to meet the diverse quality of service(QoS)requirements of tenants.Furthermore,we propose a novel cross-network radio and cloud resource management scheme for HMC networks,which is QoS-aware,with the objective of maximizing the tenant revenue while satisfying the QoS requirements.The proposed scheme is formulated as a restless bandits problem,whose "indexability" feature guarantees the low complexity with scalable and distributed characteristics.Extensive simulation results are presented to demonstrate the significant performance improvement of the proposed scheme compared to the existing ones.

Optimal Resource Allocation and Quality of Service Prediction in Cloud

In the present scenario,cloud computing service provides on-request access to a collection of resources available in remote system that can be shared by numerous clients.Resources are in self-administration;consequently,clients can adjust their usage according to their requirements.Resource usage is estimated and clients can pay according to their utilization.In literature,the existing method describes the usage of various hardware assets.Quality of Service(QoS)needs to be considered for ascertaining the schedule and the access of resources.Adhering with the security arrangement,any additional code is forbidden to ensure the usage of resources complying with QoS.Thus,all monitoring must be done from the hypervisor.To overcome the issues,Robust Resource Allocation and Utilization(RRAU)approach is developed for optimizing the management of its cloud resources.The work hosts a numerous virtual assets which could be expected under the circumstances and it enforces a controlled degree of QoS.The asset assignment calculation is heuristic,which is based on experimental evaluations,RRAU approach with J48 prediction model reduces Job Completion Time(JCT)by 4.75 s,Make Span(MS)6.25,and Monetary Cost(MC)4.25 for 15,25,35 and 45 resources are compared to the conventional methodologies in cloud environment.