Network-Aware Resource Management for Mobile Cloud

The author proposes a novel system architecture for mobile cloud computing (MCC) that includes a controller for managing computing and communication resources in Cloud Radio Access Network (C-RAN) environment. The gathered monitoring information in the controller is used when making resource allocation/management decisions. A unified protocol has been proposed, which utilises the same packet format for mobile task offloading and resource management. Moreover, the packet format and the message types of the protocol have been presented. An MCC scenario (i.e., cloudlet+clone) that consists of a cloudlet layer has been studied, in which the cloudlets are deployed next to Wi-Fi access points and serve as a localised service point in proximity to mobile devices to improve the performance of mobile cloud services. On top of this, an offloading algorithm is proposed with the main aim of deciding whether to offload to a clone or a cloudlet. The architecture described above has been implemented as a prototype by focussing on resource management in the mobile cloud. A partial implementation of a resource monitoring module that monitors both computing and communication resources have also been presented. Auto-scaling enables efficient computing resource management in the mobile cloud. An empirical performance analysis of cloud vertical scaling for mobile cloud resource management has been conducted. The working procedures of the proposed unified protocol have been illustrated to show the mobile task offloading and resource allocation functions. Simulation results of cloudlet+clone mobile task offloading algorithm demonstrate the effectiveness and efficiency of the presented task offloading architecture, and offloading algorithm on response time and energy consumption. The empirical vertical auto-scaling performance analysis for mobile cloud resource allocation shows that time delays when scaling resources (CPU, RAM, disk) in mobile cloud varies. Moreover, the scaling delay depends on the scaling amount at the given iteration.