论文标题
联合最佳软件缓存,计算卸载和通信资源分配用于移动边缘计算
Joint Optimal Software Caching, Computation Offloading and Communications Resource Allocation for Mobile Edge Computing
论文作者
论文摘要
由于多个用户可以使用软件,因此已考虑使用无线边缘的流行软件来节省移动边缘计算(MEC)的计算和通信资源。但是,到目前为止,从核心网络中获取未适应的软件和多播流的软件已被忽略。因此,现有设计是不完整的且不太实用的。在本文中,我们提出了一个联合缓存,计算和通信机制,其中涉及软件获取,缓存和多播,以及任务输入数据上传,任务执行(不可忽略的时间持续时间)以及下载计算结果以及数学上的表征。然后,我们优化关节缓存,下载和时间分配政策,以最大程度地减少受缓存和截止日期约束的加权总和消耗。这个问题是一个具有挑战性的两次混合整数非线性编程(MINLP)问题,并且通常是NP-HARD。我们通过使用一些适当的变换将其转换为等效的凸Minlp问题,并提出了两种低复杂性算法,以获得原始非凸X MINLP问题的次优溶液。具体而言,通过使用共识交替的乘数(ADMM)方法来解决松弛的凸问题,然后正确地舍入其最佳解决方案,从而获得了第一个次优的解决方案。通过使用惩罚凸孔孔孔程序(罚款-CCP)和ADMM获得等效差异(DC)问题的固定点,提出了第二个次优的解决方案。最后,通过数值结果,我们表明所提出的解决方案优于现有方案,并揭示了它们在有效利用存储,计算和通信资源方面的优势。
As software may be used by multiple users, caching popular software at the wireless edge has been considered to save computation and communications resources for mobile edge computing (MEC). However, fetching uncached software from the core network and multicasting popular software to users have so far been ignored. Thus, existing design is incomplete and less practical. In this paper, we propose a joint caching, computation and communications mechanism which involves software fetching, caching and multicasting, as well as task input data uploading, task executing (with non-negligible time duration) and computation result downloading, and mathematically characterize it. Then, we optimize the joint caching, offloading and time allocation policy to minimize the weighted sum energy consumption subject to the caching and deadline constraints. The problem is a challenging two-timescale mixed integer nonlinear programming (MINLP) problem, and is NP-hard in general. We convert it into an equivalent convex MINLP problem by using some appropriate transformations and propose two low-complexity algorithms to obtain suboptimal solutions of the original non-convex MINLP problem. Specifically, the first suboptimal solution is obtained by solving a relaxed convex problem using the consensus alternating direction method of multipliers (ADMM), and then rounding its optimal solution properly. The second suboptimal solution is proposed by obtaining a stationary point of an equivalent difference of convex (DC) problem using the penalty convex-concave procedure (Penalty-CCP) and ADMM. Finally, by numerical results, we show that the proposed solutions outperform existing schemes and reveal their advantages in efficiently utilizing storage, computation and communications resources.