边缘网络的高效数据缓存和计算卸载策略

D. Gupta, Aditi Moudgil, Shivani Wadhwa, Vikas Solanki
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引用次数: 3

摘要

我们生活在一个巨大的终端设备每天都在执行计算的世界。随着越来越多的复杂应用程序(例如,增强现实和人脸识别)需要相当多的计算能力,它们正在转向移动云计算(MCC),或者将计算卸载到云端。不幸的是,由于云通常远离终端设备,因此延迟敏感型应用程序的延迟和体验质量(QoE)会受到影响。移动边缘计算(MEC)被认为是满足低延迟要求的可行解决方案。先前的边缘计算工作主要集中在计算卸载上,以支持低延迟。本文将数据缓存和计算卸载结合起来,为终端设备用户提供更好的QoE。通过使用一种称为数据缓存和边缘计算卸载(dco - e)的有效方法缓存已完成的任务数据并在边缘云卸载计算,仿真结果证明了dco - e在低能耗和减少延迟方面与其他方案相比具有出色的性能。
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Efficient Data Caching and Computation Offloading Strategy for Edge Network
We live in a world where huge end devices execute computing on a daily basis. With the growing number of sophisticated apps (e.g., augmented reality and face recognition) that require considerably more computational capacity, they are shifting to mobile cloud computing (MCC), or offloading computation to the cloud. Unfortunately, because the cloud is typically located far away from end devices, latency and quality of experience (QoE) for delay-sensitive applications suffer. Mobile edge computing (MEC) is considered to be a viable solution for meeting the requirement for low latency. Prior works on edge computing mostly focused on computation offloading to support low latency. This paper Jointly considered data caching and computation offloading to support better QoE for end device users. With caching of completed tasks data and offloading of computations at edge cloud using an efficient approach termed as data caching and computation offloading at edge (DCCO-E), the simulation results proved outstanding performance of the DCCO-E against other schemes in terms of low energy consumption and reduced latency.
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