- مبلغ: ۸۶,۰۰۰ تومان
- مبلغ: ۹۱,۰۰۰ تومان
Mobile cloud computing is a paradigm that delivers applications to mobile devices by using cloud computing. In this way, mobile cloud computing allows for a rich user experience; since client applications run remotely in the cloud infrastructure, applications use fewer resources in the user’s mobile devices. In this paper, we present a new mobile cloud computing model, in which platforms of volunteer devices provide part of the resources of the cloud, inspired by both volunteer computing and mobile edge computing paradigms. These platforms may be hierarchical, based on the capabilities of the volunteer devices and the requirements of the services provided by the clouds. We also describe the orchestration between the volunteer platform and the public, private or hybrid clouds. As we show, this new model can be an inexpensive solution to different application scenarios, highlighting its benefits in cost savings, elasticity, scalability, load balancing, and efficiency. Moreover, with the evaluation performed we also show that our proposed model is a feasible solution for cloud services that have a large number of mobile users. Keywords: fog computing, heterogeneous cloud, hybrid cloud, mobile cloud computing, mobile edge computing, participating device.
5. Conclusion and Future Work
This paper gave an overview of mobile cloud computing (MCC), in addition to a new MCC model that can provide more computing and storage resources to public, private or hybrid clouds. The proposed heterogeneous model uses the computing and storage resources of devices from the general public to contribute to cloud systems, so the organizations can leverage the idle periods of these devices to gain computing and storage resources for their cloud services, in a similar way that volunteer devices contribute to BOINC projects. As we have shown throughout the paper, our proposed model can provide several benefits to the cloud systems, including cost savings, as it avoids monetary investments in infrastructure, since the resources are volunteered; elasticity, as it enables the system to adapt to significant workload changes in the cloud just by using the volunteered resources; scalability, as it provides more computing and storage resources to the system, so more users can use these resources; efficiency, as the volunteer devices can be closer than the cloud servers for the mobile users, thus reducing the network latency; load balancing, as the cloud controllers can choose to use the cloud’s own resources or the volunteer resources, so different load balancing algorithms can be implemented; easy deployment, as we propose to use the current BOINC open-source software in order to deploy our solution in current cloud systems. Moreover, with the evaluation performed we have also shown that our proposed model is a feasible solution for cloud services that have a large number of mobile users. This evaluation demonstrate the scalability of our solution.
For future work, we plan to use the BOINC software to deploy a prototype of this approach. We look forward to analyzing the impact of the proposed model in different scenarios, not only the ones showed in this document. Moreover, we want to analyze different load balancing policies, in order to increase the performance and scalability.