Multi-persona mobile computing has begun to make its way to determine the battle about practical strategy for adopting personal devices in workplace. Though its competency, multi-persona performance and viability are critically threatened by the limited resources of mobile devices. In recent years, mobile edge computing (MEC) has risen as promising paradigm within the internet of things era bringing benefits to the proximity of mobile terminals, leveraging intelligent computations offloading services to address the severity of their resource scarcity. Yet, embracing mobile edge-based services to augment personas resources and performance raises new concerns including determining what computations to offload for serving the highest number of mobile devices and reducing the remote execution fees imposed on the institution. In this context, we propose new cost-effective MEC-based solution to address these issues. We develop two-level multi-objective optimization realized through an intelligent offloading decision model able to settle both concerns, by minimizing processing, memory and energy while augmenting virtual mobile instances performance on a wide range of physical devices with minimal offloading service fees. We also propose a redesigned smart genetic-based method able to accelerate and reduce the overhead of offloading decision evaluation. Extensive analysis is performed and the results show that our proposition can get more quickly the offloading strategy than other schemes. The results also demonstrate the ability to enforce the virtual mobile devices by reducing local processing, memory usage, energy consumption and execution time along with acceptable minimal additional fees compared to other techniques.

中文翻译：

---

多人移动：联合成本效益和资源感知移动边缘计算卸载

多人移动计算已经开始决定在工作场所采用个人设备的实际策略的战斗。尽管其能力、多角色性能和可行性受到移动设备有限资源的严重威胁。近年来，移动边缘计算 (MEC) 已成为物联网时代有前景的范式，为移动终端带来好处，利用智能计算卸载服务来解决其资源稀缺的严重问题。然而，采用基于移动边缘的服务来增强角色资源和性能引发了新的担忧，包括确定为最多数量的移动设备服务需要卸载哪些计算以及降低对机构征收的远程执行费用。在这种情况下，我们提出了新的经济高效的基于 MEC 的解决方案来解决这些问题。我们开发了通过智能卸载决策模型实现的两级多目标优化，通过最小化处理、内存和能源，同时以最低的卸载服务费用增强各种物理设备上的虚拟移动实例性能，从而解决这两个问题。我们还提出了一种重新设计的基于智能遗传的方法，能够加速和减少卸载决策评估的开销。进行了广泛的分析，结果表明我们的提议可以比其他方案更快地获得卸载策略。结果还证明了通过减少本地处理、内存使用、