Data-Driven Edge Computing Resource Scheduling for Protest Crowds Incident Management
Data-Driven Edge Computing Resource Scheduling for Protest Crowds Incident Management
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DOI：
10.1109/nca.2018.8548069
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发表时间：
2018-11
期刊：
影响因子：
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通讯作者：
Jon Patman;Peter Lovett;A. Banning;Annie Barnert;D. Chemodanov;P. Calyam
中科院分区：
文献类型：
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作者：
Jon Patman;Peter Lovett;A. Banning;Annie Barnert;D. Chemodanov;P. Calyam
计算卸载已被证明是一种可行的解决方案，可以解决在低功耗设备和附近的服务器（称为云）之间处理计算密集型工作负载的挑战。然而，诸如动态网络条件、并发用户访问和有限的资源可用性等因素通常导致卸载决策在延迟和能耗方面对最终用户产生负面影响。为了解决这些缺点，我们在一系列现实的无线实验中研究了使用机器学习来预测面部识别服务的卸载成本的好处。我们还进行了一组跟踪驱动的模拟，以模拟多边缘抗议人群事件的案例研究，并制定了一个优化模型，最大限度地减少了所有服务任务完成所需的时间。由于优化卸载这样一个系统的时间表是一个众所周知的NP完全问题，我们使用混合整数规划，并表明我们的调度解决方案的规模有效地为中等数量的用户设备（10-100）与相应的少量的云（1-10），规模通常足以为公共安全官员在人群事件管理。此外，我们的研究结果表明，使用机器学习来预测卸载成本，在我们调查的70%的情况下，可以实现接近最佳的调度，并且与基线估计技术相比，性能提高了40%。
Computation offloading has been shown to be a viable solution for addressing the challenges of processing compute-intensive workloads between low-power devices and nearby servers known as cloudlets. However, factors such as dynamic network conditions, concurrent user access, and limited resource availability often result in offloading decisions negatively impacting end users in terms of delay and energy consumption. To address these shortcomings, we investigate the benefits of using Machine Learning for predicting offloading costs for a facial recognition service in a series of realistic wireless experiments. We also perform a set of trace-driven simulations to emulate a multi-edge protest crowd incident case study and formulate an optimization model that minimizes the time taken for all service tasks to be completed. Because optimizing offloading schedules for such a system is a well-known NP-complete problem, we use mixed integer programming and show that our scheduling solution scales efficiently for a moderate number of user devices (10–100) with a correspondingly small number of cloudlets (1–10), a scale commonly sufficient for public safety officials in crowd incident management. Moreover, our results indicate that using Machine Learning for predicting offloading costs leads to near-optimal scheduling in 70 % of the cases we investigated and offers a 40 % gain in performance over baseline estimation techniques.