On Cost Aware Cloudlet Placement for Mobile Edge Computing
On Cost Aware Cloudlet Placement for Mobile Edge Computing
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DOI：
10.1109/jas.2019.1911564
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发表时间：
2019-07-01
影响因子：
11.8
通讯作者：
Ansari, Nirwan
中科院分区：
文献类型：
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作者：
Fan, Qiang;Ansari, Nirwan
由于从远程云访问计算资源固有地为移动的用户带来高的端到端（E2E）延迟，部署在网络边缘的小云可以潜在地缓解该问题。尽管一些研究工作集中在在小云中分配工作负载，但旨在最小化部署成本（即，由微云成本和平均E2E延迟成本两者组成）的问题迄今尚未得到有效解决。微云的位置和数量对网络中的微云成本和用户的平均E2E延迟都有至关重要的影响。因此，在本文中，我们提出了移动的边缘计算中的成本感知小云放置（CAPABLE）策略，其中在小云放置中考虑了小云成本和平均E2E延迟。为了解决这个问题，拉格朗日启发式算法开发，以获得次优解。在网络中放置云之后，我们还设计了一个工作负载分配方案，通过考虑用户的移动性来最小化用户和他们的云之间的E2E延迟。CAPABLE的性能已经通过大量的仿真验证。
As accessing computing resources from the remote cloud inherently incurs high end-to-end (E2E) delay for mobile users, cloudlets, which are deployed at the edge of a network, can potentially mitigate this problem. Although some research works focus on allocating workloads among cloudlets, the cloudlet placement aiming to minimize the deployment cost (ie., consisting of both the cloudlet cost and average E2E delay cost) has not been addressed effectively so far. The locations and number of cloudlets have a crucial impact on both the cloudlet cost in the network and average E2E delay of users. Therefore, in this paper, we propose the Cost Aware cloudlet PlAcement in moBiLe Edge computing (CAPABLE) strategy, where both the cloudlet cost and average E2E delay are considered in the cloudlet placement. To solve this problem, a Lagrangian heuristic algorithm is developed to achieve the suboptimal solution. After cloudlets are placed in the network, we also design a workload allocation scheme to minimize the E2E delay between users and their cloudlets by considering the user mobility. The performance of CAPABLE has been validated by extensive simulations.