HyEdge: A Cooperative Edge Computing Framework for Provisioning Private and Public Services

IF 3.5 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Siyuan Gu, Deke Guo, Guoming Tang, Lailong Luo, Yuchen Sun, Xueshan Luo
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Abstract

With the widespread use of Internet of Things (IoT) devices and the arrival of the 5G era, edge computing has become an attractive paradigm to serve end-users and provide better QoS. Many efforts have been paid to provision some merging public network services at the network edge. We reveal that it is very common that specific users call for private and isolated edge services to preserve data privacy and enable other security intentions. However, it still remains open to fulfill such kind of mixed requests in edge computing. In this article, we propose a cooperative edge computing framework, i.e., HyEdge, to offer both public and private edge services systematically. To fully exploit the benefits of this novel framework, we define the problem of optimal request scheduling over a given placement solution of hybrid edge servers to minimize the response delay. This problem is further modeled as a mixed integer non-linear programming problem (MINLP), which is typically NP-hard. Accordingly, we propose the partition-based optimization method, which can efficiently solve this NP-hard problem via the problem decomposition and the branch and bound strategies. We finally conduct extensive evaluations with a real-world dataset to measure the performance of our method. The results indicate that the proposed method achieves elegant performance with low computation complexity.
HyEdge:用于提供私有和公共服务的协作边缘计算框架
随着物联网(IoT)设备的广泛使用和5G时代的到来,边缘计算已经成为服务最终用户和提供更好QoS的有吸引力的范式。在网络边缘提供一些合并的公共网络服务已经付出了许多努力。我们发现,特定用户通常会要求私有和隔离的边缘服务来保护数据隐私并实现其他安全意图。然而,在边缘计算中,它仍然可以满足这种混合请求。在本文中,我们提出了一个协作边缘计算框架,即HyEdge,以系统地提供公共和私有边缘服务。为了充分利用这种新框架的优势,我们定义了在混合边缘服务器的给定放置解决方案上的最优请求调度问题,以最大限度地减少响应延迟。该问题进一步建模为混合整数非线性规划问题(MINLP),这是典型的np困难问题。因此,我们提出了基于分区的优化方法,通过问题分解和分支定界策略有效地解决了这一NP-hard问题。最后,我们使用真实世界的数据集进行了广泛的评估,以衡量我们的方法的性能。结果表明,该方法具有较好的性能和较低的计算复杂度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
5.20
自引率
3.70%
发文量
0
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