Cache Optimization Strategy for Mobile Edge Computing in Maritime IoT

Hailong Feng, Zhengqi Cui, Tingting Yang
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引用次数: 1

Abstract

With the increasing storage capacity of Internet of Things (IoT) mobile devices, cache-enabled device-to-device (D2D) networks enable efficient information sharing, thereby increasing the transmission efficiency of the entire network. The efficiency is further improved by the rational deployment of caching strategies on mobile devices in combination with traditional base station transmission methods. In this paper, the mobile-aware caching strategy is divided into two problems to solve. The first problem is to solve the user's latency-minimizing cache placement problem. We transform the problem into a decision problem, propose a low-complexity algorithm that approximates the optimal solution, and justify the method using the properties of submodular functions. The second problem addresses external restriction parameters, such as cache file type, cache upper limit, and deadline. We find through simulation that there is a bottleneck in the performance improvement of the whole system as the external parameters change. A suitable formulation of these parameters can put the system in a range where the input and output are most effective, further maximizing the performance of the optimization method. We introduce the concept of marginal efficiency and use Bayesian optimization to solve the selection of these parameters. The final validation is obtained by simulation with real data.
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海事物联网中移动边缘计算缓存优化策略
随着物联网(IoT)移动设备存储容量的不断增加,支持缓存的设备到设备(device-to-device, D2D)网络可以实现高效的信息共享,从而提高整个网络的传输效率。结合传统基站传输方式,在移动设备上合理部署缓存策略,进一步提高了效率。本文将移动感知缓存策略分为两个问题来解决。第一个问题是解决用户最小化延迟的缓存放置问题。我们将该问题转化为决策问题,提出了一种近似最优解的低复杂度算法,并利用子模函数的性质对该方法进行了证明。第二个问题涉及外部限制参数,例如缓存文件类型、缓存上限和截止日期。通过仿真发现,随着外部参数的变化,整个系统的性能提升存在瓶颈。这些参数的适当公式可以使系统处于输入和输出最有效的范围内,从而进一步最大化优化方法的性能。我们引入了边际效率的概念,并用贝叶斯优化方法解决了这些参数的选择问题。通过对实际数据的仿真,得到了最后的验证结果。
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