2L-MC3: A Two-Layer Multi-Community- Cloud/Cloudlet Social Collaborative Paradigm for Mobile Edge Computing
2L-MC3: A Two-Layer Multi-Community- Cloud/Cloudlet Social Collaborative Paradigm for Mobile Edge Computing
复制标题
2L-MC3：移动边缘计算的两层多社区-Cloud/Cloudlet 社交协作范例
DOI：
10.1109/jiot.2018.2867351
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发表时间：
2019
影响因子：
10.6
通讯作者：
Geyong Min
中科院分区：
文献类型：
--
作者：
Fei Hao;Doo-Soon Park;Junho Kang;Geyong Min
移动边缘计算（MEC）通过利用网络边缘的可用资源来增强移动设备的计算和存储容量提供有前途的解决方案。在各种物联网应用程序（IoT）应用程序中，MEC可以帮助我们缩小物联网应用程序和物联网设备的有限资源之间的差距，并实现节能的通信和计算。重要的是，云计算的上和边缘基础架构应有效地协作，以执行移动用户要求的复杂任务。特别是，社区云计算是针对具有共同关注（例如安全性，合规性和管辖权）的特定社区的新型计算模型，可以充分利用网络计算机的备用资源来提供设施，以便社区从中获得服务。但是，如何将子任务分配给社区云和边缘社区云（Cloudlets）正在成为一个关键挑战。为了应对这一挑战，本文首先提出了两层多社区云/Cloudlet社交协作范式，称为MEC的2L-MC3。此外，我们通过考虑任务卸载，任务和云配置文件来解决社区云/云中的任务分配问题。为了解决此问题，我们为任务分配设计了双层编程模型。进行了广泛的模拟，以证明所提出的方法可以实现相对的全球性能，以满足与其他方法相比的每个指标。
Mobile edge computing (MEC) is providing a promising solution for augmenting the computing and storage capacity of mobile devices by exploiting the available resources at the network edge. Among the various Internet of Things (IoT) applications, MEC could help us to narrow the gap between the requirements of IoT applications and the limited resources of IoT devices and to achieve the energy-efficient communication and computing. Importantly, the upper and edge infrastructure of cloud computing should effectively collaborate for executing the complex tasks which are requested by mobile users. In particular, community cloud computing, as a novel computational model for a specific community with common concerns (such as security, compliance, and jurisdiction), can make full use of the spare resources of networked computers to provide the facilities so that the community gains services from them. However, how to allocate the subtasks into community clouds and edge community clouds (cloudlets) is becoming a critical challenge. To tackle this challenge, this paper first proposes a two-layer multi-community-cloud/cloudlet social collaborative paradigm, called 2L-MC3for MEC. Further, we formulate a problem on tasks allocation in community clouds/cloudlets by jointly taking task offloading, tasks and clouds profiles into account. To address this problem, we devise a bi-level programming model for tasks allocation. Extensive simulations are conducted for demonstrating that the proposed approach can achieve the relative global performance for satisfying the each metric comparing to the other approaches.