论文标题
边缘云系统的截止日期限制的多资源任务映射和分配
Deadline-constrained Multi-resource Task Mapping and Allocation for Edge-Cloud Systems
论文作者
论文摘要
在Edge-Cloud系统中，移动设备可以将其计算密集型任务卸载到边缘或云服务器，以确保服务质量或满足任务截止日期的要求。但是，确定应在何处卸载和处理任务，以及应分配多少网络和计算资源，从而使资源有限的系统在满足截止日期时可以获得最大利润是一项挑战。此问题的一个关键挑战是，网络和计算资源可以在不同的服务器上分配，因为任务被卸载的服务器（例如，具有访问点的服务器）可能与最终处理任务的服务器不同。为了应对这一挑战，我们首先将任务映射和资源分配问题作为非convex混合企业非线性编程（MINLP）问题，称为NP-HARD。然后，我们提出了一种基于零的基于零的贪婪算法（ZSG）和线性离散方法（LDM）来解决此MINLP问题。各种合成任务的实验结果表明，ZSG的平均价格比LDM的平均$ 2.98 \％$差，最低单位为5美元，但平均比LDM的平均$ 6.88 \％$ $ $，最低单位15。
In an edge-cloud system, mobile devices can offload their computation intensive tasks to an edge or cloud server to guarantee the quality of service or satisfy task deadline requirements. However, it is challenging to determine where tasks should be offloaded and processed, and how much network and computation resources should be allocated to them, such that a system with limited resources can obtain a maximum profit while meeting the deadlines. A key challenge in this problem is that the network and computation resources could be allocated on different servers, since the server to which a task is offloaded (e.g., a server with an access point) may be different from the server on which the task is eventually processed. To address this challenge, we first formulate the task mapping and resource allocation problem as a non-convex Mixed-Integer Nonlinear Programming (MINLP) problem, known as NP-hard. We then propose a zero-slack based greedy algorithm (ZSG) and a linear discretization method (LDM) to solve this MINLP problem. Experiment results with various synthetic tasksets show that ZSG has an average of $2.98\%$ worse performance than LDM with a minimum unit of 5 but has an average of $6.88\%$ better performance than LDM with a minimum unit of 15.
