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.
翻译:在一个边缘-高悬悬悬崖系统中,移动装置可以将其计算密集的任务卸到边缘或云端服务器上,以保证服务质量或满足任务期限要求;然而,要确定任务应卸卸和处理何处,以及应分配多少网络和计算资源,以便一个资源有限的系统能够在遵守最后期限时获得最大利润;这个问题的一个关键挑战是,可以在不同服务器上分配网络和计算资源,因为任务被卸去的服务器(例如,具有接入点的服务器)可能不同于任务最终处理的服务器,因此,移动装置可以将其计算密集的任务卸载到边缘或云端服务器,以保证服务质量的质量或满足任务最后期限要求;然而,要应对这一挑战,我们首先将任务绘图和资源分配问题确定为任务应卸卸卸和处理哪些任务,并分配给它们多少网络和计算资源,以便一个资源有限的系统能够在最后期限前获得最大利润最大利润;然后,我们提出一个基于零的贪贪贪贪算算算算算法和线离解方法来解决这个MILLPPLP问题。 与各种合成任务实验结果显示,ZSG的平均值为2.98美元,但比15.88美元比LDM的LDM的最低限度业绩单位至少一个比LDM的最低限度业绩单位,比LDM的LDM的LDM有比LDM的最低限度的LDM的LDM的LDM单位平均平均5美元,比LDM的LDM的LDM的LDM的LDM的LDM的最低限度,其最低性工作平均值平均值比LDM单位的5美元,比LDM的LDM单位的5美元,比LDM有比LDM的5的LDM的5的LDM有比LDM的5的5的LDM的5的5的5的5的5的5的LDM标准单位的LDM的5的5的5的5的5的LDM的LDM的LDM的LDM的LB单位的LDM的5的5的LB单位的LB单位的LB的LB的LB的LB的LB的LB的LB的P单位的比LDM更值平均值平均值平均值平均值平均值平均值平均值平均值比为0.</s>