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Towards cost-efficient resource provisioning with multiple mobile users in fog computing

机译:在雾计算中具有多个移动用户的成本高效的资源供应

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Fog computing is an emerging paradigm that brings computing capabilities closer to distributed IoT devices, which provides networking services between end devices and traditional cloud data centers. One important mission is to further reduce the monetary cost of fog resources while meeting the ever-growing demands of multiple users. In this paper, we focus on minimizing the total cost for multiple mobile users to provide an efficient resource provisioning scheme in fog computing. The total cost includes two aspects: the replication cost and the transmission cost. We consider three cases for the resource provision problem by focusing on different cost models. First, one simple case where users can only upload one replication is discussed, and an optimal solution is proposed by converting the original problem into a bipartite graph matching. Then we consider a more complicated case in which each user can upload multiple replications on fog nodes in the resource provisioning. Specifically, two models are discussed: the 0-1 transmission cost model and the different transmission cost model. For the 0-1 transmission cost model, each user can upload multiple replications with a constant transmission cost, and one optimal greedy solution is proposed. For the different transmission cost model, the transmission cost is related to the distance of each pair of fog nodes. This problem is proven to be NP-hard. We first propose a non-adaptive algorithm which is proved to be bounded by (2/3)W+(1/3)OPT. Another 3 + ε-approximation algorithm is proposed based on local search, which has better performance with higher complexity. Extensive simulations also prove the efficiency of our schemes.
机译:雾计算是一种新兴范式,可使计算能力更接近分布式物联网设备,该设备在终端设备和传统云数据中心之间提供网络服务。一个重要的任务是进一步减少雾资源的货币成本,同时满足多个用户不断增长的需求。在本文中,我们专注于最大限度地减少多个移动用户的总成本,以提供雾计算中有效的资源供应方案。总成本包括两个方面:复制成本和传输成本。通过专注于不同的成本模型,我们考虑了资源提供问题的三个案例。首先,讨论用户只能上载一个复制的一个简单情况,并且通过将原始问题转换为二分钟匹配来提出最佳解决方案。然后,我们考虑一个更复杂的情况,其中每个用户可以在资源配置中上传雾节点上的多个复制。具体地,讨论了两个模型:0-1传输成本模型和不同传输成本模型。对于0-1传输成本模型,每个用户可以通过恒定的传输成本上传多个复制,并提出了一个最佳贪婪解决方案。对于不同的传输成本模型,传输成本与每对雾节点的距离有关。这一问题被证明是NP-HARD。我们首先提出了一种非自适应算法,被证明被(2/3)W +(1/3)选择界限。基于本地搜索提出另外的3 +ε-近似算法,其具有更好的性能,具有更高的复杂性。广泛的模拟也证明了我们计划的效率。

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