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Cloudlets Activation Scheme for Scalable Mobile Edge Computing with Transmission Power Control and Virtual Machine Migration

机译:具有传输功率控制和虚拟机迁移的可扩展移动边缘计算的Cloudlets激活方案

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摘要

Mobile devices have several restrictions due to design choices that guarantee their mobility. A way of surpassing such limitations is to utilize cloud servers called cloudlets on the edge of the network through Mobile Edge Computing. However, as the number of clients and devices grows, the service must also increase its scalability in order to guarantee a latency limit and quality threshold. This can be achieved by deploying and activating more cloudlets, but this solution is expensive due to the cost of the physical servers. The best choice is to optimize the resources of the cloudlets through an intelligent choice of configuration that lowers delay and raises scalability. Thus, in this paper we propose an algorithm that utilizes Virtual Machine Migration and Transmission Power Control, together with a mathematical model of delay in Mobile Edge Computing and a heuristic algorithm called Particle Swarm Optimization, to balance the workload between cloudlets and consequently maximize cost-effectiveness. Our proposal is the first to consider simultaneously communication, computation, and migration in our assumed scale and, due to that, manages to outperform other conventional methods in terms of number of serviced users.
机译:由于保证移动性的设计选择,移动设备具有多个限制。克服此类限制的一种方法是通过移动边缘计算在网络边缘上利用称为cloudlets的云服务器。但是,随着客户端和设备数量的增加,该服务还必须增加其可伸缩性,以保证延迟限制和质量阈值。这可以通过部署和激活更多的cloudlet来实现,但是由于物理服务器的成本,该解决方案非常昂贵。最好的选择是通过明智的配置选择来优化cloudlets的资源,从而降低延迟并提高可伸缩性。因此,在本文中,我们提出了一种利用虚拟机迁移和传输功率控制以及移动边缘计算中的延迟数学模型和称为粒子群优化的启发式算法的算法,以平衡小云之间的工作量,从而最大程度地降低成本。效力。我们的建议是第一个在假定的规模上同时考虑通信,计算和迁移的建议,因此,就服务用户数量而言,它的表现优于其他传统方法。

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