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Mobility Prediction-Based Service Scheduling Optimization Algorithm in Cloudlets

机译：Cloudlets中基于移动预测的服务调度优化算法

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摘要

Cloudlet is an emerging technology in mobile cloud computing. However users may be far away from cloudlets due to the mobility of mobile users, which leads to a poor network connectivity, thus, user experience will be poor. While a user moves across multiple cloudlets areas, issues of service scheduling between cloudlets to better support user experience become important. In this paper, we consider a latency-sensitive and stateful service scheduling problem in cloudlets. We propose a novel cloudlet service model and formulate the problem with the goal of finding the optimal service running sequence which minimizes the average service response time during the whole running process of the service for a user. To solve this problem, we propose an algorithm called Mobility Prediction-based Markov Decision Process (MPMDP). The proposed algorithm takes user's mobility prediction into account, and makes an decision based on Markov Decision Process to decide on which cloudlet the service should run for a user each time. Finally, we evaluate the effectiveness of the proposed MPMDP algorithm by simulations with real-world users' traces. The simulation result shows our algorithm achieves a lower average response time compared with previous schemes.
机译：Cloudlet是移动云计算中的新兴技术。然而，由于移动用户的移动性，用户可能远离小云，这导致差的网络连接性，因此，用户体验将很差。当用户跨多个Cloudlet区域移动时，为了更好地支持用户体验，在Cloudlet之间进行服务调度的问题变得很重要。在本文中，我们考虑了Cloudlets中的延迟敏感和状态服务调度问题。我们提出了一种新颖的cloudlet服务模型，并提出了该问题，其目的是找到最佳服务运行顺序，从而在为用户服务的整个运行过程中将平均服务响应时间减至最少。为了解决此问题，我们提出了一种称为基于移动性预测的马尔可夫决策过程（MPMDP）的算法。所提出的算法考虑了用户的移动性预测，并基于马尔可夫决策过程做出了决定，以决定每次服务应该为用户运行哪个cloudlet。最后，我们通过对真实用户跟踪的仿真来评估所提出的MPMDP算法的有效性。仿真结果表明，与以前的方案相比，我们的算法实现了更低的平均响应时间。

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