Application partitioning and offloading in mobile cloud computing.

With the emergence of high quality and rich multimedia content, the end user demands of content processing and delivery are increasing rapidly. In view of increasing user demands and quality of service (QoS), cloud computing offers a huge amount of online processing and storage resources which can be exploited on demand. Moreover, the current high speed 4G mobile network i.e. Long Term Evolution (LTE) enables leveraging of the cloud resources. Mobile Cloud Computing (MCC) is an emerging paradigm comprising three heterogeneous domains of mobile computing, cloud computing, and wireless networks. MCC aims to enhance computational capabilities of resource-constrained mobile devices towards rich user experience. Decreasing cloud cost and latency is attracting the research community to exploit the cloud computing resource to offload and process multimedia content in the cloud. High bandwidth and low latency of LTE makes it a suitable candidate for delivering of rich multi-media cloud content back to the user. The convergence of cloud and LTE give rise to an end-to-end communication framework which opens up the possibility for new applications and services. In addition to cloud and network, end user and application constitute the other enti-ties of the end-to-end communication framework. End user quality of service and particular application profile dictate about resource allocation in the cloud and the wireless network. This research formulates different building blocks of the end-to-end communications and in-troduces a new paradigm to exploit the network and cloud resources for the end user. In this way, we employ a multi-objective optimization strategy to propose and simulate an end-to-end communication framework which promises to optimize the behavior of MCC based end-to-end communication to deliver appropriate quality of service (QoS) with utilization of min-imum cloud and network resources. Then we apply application partitioning and offloading schemes to offload certain parts of an application to the cloud to improve energy efficiency and response time. As deliverables of this research, behavior of different entities (cloud, LTE based mobile network, user and application context) have been modeled. In addition, a com-prehensive application partitioning and offloading framework has been proposed in order to minimize the cloud and network resources to achieve user required QoS. Keywords: Long Term Evolution (LTE), Cloud computing, Application partitioning and offloading, Image Retrieval