Latency aware mobile task assignment and load balancing for edge cloudlets

V. Chamola, C. Tham, G. Chalapathi
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引用次数: 35

Abstract

With the various technological advances, mobile devices are not just being used as a means to make voice calls; but are being used to accomplish a variety of tasks. Mobile devices are being envisioned to practically accomplish any task which could be done on a computer. This is hurdled by the limited computational resources available with the mobile devices due to their portable size. With the mobile devices being connected to the Internet, leveraging cloud services is being seen as a promising solution to overcome this hurdle. Computationally intensive tasks can be offloaded to the Cloud servers. However, owing to the latency and cost associated with using cloud services, edge devices (termed cloudlets) stationed near the mobile devices are being seen as a prospective alternative to replace/assist the Cloud services. The mobile devices have an easier access to the cloudlets being situated in their vicinity and can offload their task requests to them to be served at a lower cost. This paper considers a network of such connected cloudlets which provide service to the mobile devices in a given area. We address the issue of task assignment in such a scenario (i.e. which cloudlet serves which mobile device) aimed towards improving the quality of service experienced by the mobile devices in terms of minimizing the latency. Through numerical simulations we demonstrate the performance gains of the proposed task assignment scheme showing lower latency as compared to the traditional scheme for task assignment.
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边缘云的延迟感知移动任务分配和负载平衡
随着各种技术的进步,移动设备不仅仅被用作拨打语音电话的手段;而是被用来完成各种任务。人们设想移动设备实际上可以完成任何可以在计算机上完成的任务。由于移动设备的便携尺寸,这受到可用的有限计算资源的阻碍。随着移动设备连接到互联网,利用云服务被视为克服这一障碍的一个有前途的解决方案。计算密集型任务可以卸载到云服务器上。然而,由于与使用云服务相关的延迟和成本,部署在移动设备附近的边缘设备(称为cloudlets)被视为替代/辅助云服务的潜在替代方案。移动设备可以更容易地访问位于其附近的云,并可以将其任务请求卸载给它们,以便以较低的成本提供服务。本文考虑了这样一个连接的云的网络,这些云为给定区域的移动设备提供服务。我们解决了这样一个场景中的任务分配问题(即哪个cloudlet为哪个移动设备服务),旨在通过最小化延迟来提高移动设备体验的服务质量。通过数值模拟,我们证明了与传统的任务分配方案相比,所提出的任务分配方案具有更低的延迟。
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