CN113286329B - 基于移动边缘计算的通信和计算资源联合优化方法 - Google Patents

基于移动边缘计算的通信和计算资源联合优化方法 Download PDF

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CN113286329B
CN113286329B CN202110544546.3A CN202110544546A CN113286329B CN 113286329 B CN113286329 B CN 113286329B CN 202110544546 A CN202110544546 A CN 202110544546A CN 113286329 B CN113286329 B CN 113286329B
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朱红
田峰
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Nanjing University of Posts and Telecommunications
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Abstract

本发明公开了无线通信技术领域的一种基于移动边缘计算的通信和计算资源联合优化方法，其包括：基于移动边缘计算系统模型、任务排队计算模型以及通信模型，制定进行任务卸载时最优化系统功耗和吞吐量的优化问题；将优化问题分解为设备端到边缘服务器的负载流量预测问题和基于系统功耗和吞吐量的边缘计算联合优化通信资源和计算资源问题；以最优化的系统功耗和吞吐量为目标，解决上述问题，从而完成资源分配任务。本发明能够根据设备侧与边缘服务器层的流量负载预测、高效的资源分配调度策略，以最大程度地优化功耗和吞吐量。

Description

基于移动边缘计算的通信和计算资源联合优化方法
技术领域
本发明涉及一种基于移动边缘计算的通信和计算资源联合优化方法，属于无线通信技术领域。
背景技术
随着物联网技术的不断推进，设备终端上将运行着越来越多的数据密集型应用和时延敏感型应用。这些应用具备低时延、高带宽的要求对于设备有限的资源提出了很大的挑战，严重影响了用户服务体验质量。
为了满足时延和带宽需求，研究者提出了云计算和边缘计算。云计算配备有大型数据中心，具有很高的计算能力，它可以接收并处理来自设备侧的不同数据，但是传统的云计算服务器通常和移动设备间隔很远的距离，整个传输过程会导致巨大的传输时延压力。边缘计算在无线网络边缘扩展计算、带宽、存储等资源，以此为设备侧提供强有效的计算能力、存储能力、位置感知服务等，缓解传输通信网络成本，然而边缘计算服务器计算存储资源有限，难以满足大型任务的服务要求。
现有的移动边缘计算资源分配研究中，大多数考虑的是多个边缘服务器之间的协作，或是边缘服务器和云服务器之间的协作，很少同时考虑边缘节点和边缘节点的合作以及边缘节点和云之间的协作，共同为用户提供服务。
发明内容
本发明的目的在于克服现有技术中的不足，提供一种基于移动边缘计算的通信和计算资源联合优化方法，考虑了设备侧与边缘服务器层的流量负载预测、高效的资源分配调度策略，以最大程度地优化功耗和吞吐量。
为达到上述目的，本发明是采用下述技术方案实现的：
本发明提供了一种基于移动边缘计算的通信和计算资源联合优化方法，包括以下步骤：
基于移动边缘计算系统模型、任务排队计算模型以及通信模型，制定进行任务卸载时最优化系统功耗和吞吐量的优化问题；
将优化问题分解为设备端到边缘服务器的负载流量预测问题和基于系统功耗和吞吐量的边缘计算联合优化通信资源和计算资源问题；
以最优化的系统功耗和吞吐量为目标，解决上述问题，从而完成资源分配任务；
其中，所述移动边缘计算系统模型基于移动边沿计算的任务调度和资源分配框架建立；
通过李雅普诺夫优化方法将通信资源和计算资源问题分解为多个子问题并逐一进行解决；所述子问题包括基于FDMA的发射功率和带宽优化问题、边缘服务器和云服务器计算资源优化问题、边缘服务器之间的任务迁移优化问题。
优选的，所述移动边缘计算系统模型包括：
由终端资源请求者构成的用户设备层，所述用户设备层包括多个不同的物联网传感器设备；
由边缘计算资源提供者构成的边缘计算层，所述边缘计算层包括边缘服务器以及边缘节点；其中，边缘服务器中的虚拟处理单元可以自适应地开启和关闭边缘节点，边缘节点分布在不同区域，可实时感知用户设备层的终端设备请求，提供设备接入、数据处理服务，并且不同的边缘节点之间可通过有线链路进行任务传输；
由集中式云服务器构成的中心云层，所述中心云层包括存储容量大、计算能力强的服务器集群，用于为边缘计算层提供大量的计算处理服务。
优选的，所述通信模型包括：
边缘服务器与云服务器直接采用无线链路通信方式OFDM进行任务传输；
根据香农定理，边缘服务器的边缘节点i传输速率Ri(t)表达式如下所示：
Figure GDA0003824375090000031
其中，N0表示高斯白噪声的功率谱密度，pi(t)和hi(t)分别表示边缘服务器的边缘节点i与云服务器之间的发射功率和信道功率，W为边缘服务器与云服务器之间的总信道带宽，ζi(t)表示所分配的带宽资源比例，τ表示时隙。
优选的，所述任务排队计算模型包括：
边缘服务器上的任务队列Qi(t)的更新过程的表达式如下所示：
Figure GDA0003824375090000032
其中，Ai(t)表示从用户设备层的终端设备抵达边缘服务器的数据量，
Figure GDA0003824375090000033
表示从邻居边缘服务器卸载到本地边缘服务器的任务量，
Figure GDA0003824375090000034
表示直接在本地边缘服务器处理的任务量，
Figure GDA0003824375090000035
表示发送到邻居边缘服务器处理的任务量，
Figure GDA0003824375090000036
表示发送到云服务器处理的任务量；
云服务器上的任务队列G(t)的更新过程的表达式如下所示：
Figure GDA0003824375090000037
其中，w(t)表示云服务器处理的任务量，
Figure GDA0003824375090000041
表示从边缘服务器卸载到云服务器的任务量。
优选的，所述最优化系统功耗和吞吐量包括：队列稳定性约束，服务器计算资源约束，发射功率约束以及通信带宽分配比例约束。
优选的，所述负载流量预测问题包括：
根据已知的边缘服务器位置以及终端设备位置，获取任务的数据量；
基于任务的数据量，根据边缘服务器的覆盖范围以及用户数预测出抵达每个边缘服务器的工作负载流量；
所述通信资源和计算资源问题包括：
所有任务抵达边缘服务器的边缘节点后，
直接在本地边缘服务器处理的任务量与边缘服务器计算能力相关；
传输到邻居边缘服务器处理的任务量应尽可能地小以减少时延损失；
发送到云服务处理的任务量与通信传输速率相关；
所述边缘计算联合优化包括：
在优化问题中引入一个虚拟队列进行约束条件转化，采用李雅普诺夫优化方法进行队列稳定性条件转化，构造出李雅普诺夫加罚漂移函数，再结合约束条件，去掉其中的常数项，从而获得新的优化目标函数，通过优化目标函数进行边缘计算联合优化。
优选的，所述解决负载流量预测问题包括：
通过训练好的LSTM神经网络进行负载流量预测，从而解决负载流量预测问题；所述LSTM神经网络的训练包括获取之前时刻的边缘节点的负载流量数据，并通过上述负载流量数据对LSTM神经网络进行多次训练。
优选的，所述基于FDMA的发射功率和带宽优化问题的表达式如下：
Figure GDA0003824375090000051
其中，V表示李雅普诺夫控制优化参数，λ表示一个放大系数，Ri(t)表示传输速率，pi(t)表示发射功率，ω1和ω2表示控制能耗和计算吞吐量的权重系数；
解决所述基于FDMA的发射功率和带宽优化问题包括：
提取上述表达式中的两个优化变量pi(t)和Ri(t)；
优化变量pi(t)的求解表达式如下：
Figure GDA0003824375090000052
Figure GDA0003824375090000053
通过运算获得pi(t)的最优解pi(t)*的表达式如下：
Figure GDA0003824375090000054
优化变量Ri(t)通过ζi(t)进行表示，ζi(t)的求解表达式如下：
Figure GDA0003824375090000055
Figure GDA0003824375090000056
构造对应的拉格朗日函数：
Figure GDA0003824375090000057
其中，a表示非负拉格朗日乘子；
通过拉格朗日函数对ζi(t)和a求偏导数：
Figure GDA0003824375090000058
最后利用KKT条件求出ζi(t)的最优解，从而获取Ri(t)的最优解。
优选的，所述边缘服务器和云服务器计算资源优化问题的表达式如下：
Figure GDA0003824375090000061
Figure GDA0003824375090000062
其中，
Figure GDA0003824375090000063
表示构造的一个虚拟队列，
Figure GDA0003824375090000064
Figure GDA0003824375090000065
表示边缘服务器i的计算能力，fc(t)表示云服务器的计算能力，G(t)表示云服务器队列长度，
Figure GDA0003824375090000066
表示处理1bit任务所需的CPU周期数，ke和kc表示和硬件相关的有效系数，σ表示一个小参数，Le表示边缘服务器的CPU频率；
解决边缘服务器和云服务器计算资源优化问题包括：
Figure GDA0003824375090000067
Figure GDA0003824375090000068
其中，fi e(t)*表示fi e(t)的最优解，fc(t)*表示fc(t)的最优解。
优选的，所述边缘服务器之间的任务迁移优化问题的表达式如下：
Figure GDA0003824375090000069
其中，Ai(t)表示从用户设备层的终端设备抵达边缘服务器的数据量，
Figure GDA00038243750900000610
表示从邻居边缘服务器卸载到本地边缘服务器的任务量，
Figure GDA00038243750900000611
表示直接在本地边缘服务器处理的任务量，
Figure GDA00038243750900000612
表示发送到邻居边缘服务器处理的任务量，
Figure GDA00038243750900000613
表示发送到云服务器处理的任务量；
解决边缘服务器之间的任务迁移优化问题包括：
得到了最佳服务器资源分配fi e(t)*和fc(t)*后，贪婪地选取最小的任务迁移到邻居边缘服务器。
与现有技术相比，本发明所达到的有益效果：
本发明的基于移动边缘计算的通信和计算资源联合优化方法，适用于移动边缘计算中联合优化通信和计算资源方法，同时考虑了多个边缘节点之间的协作以及边缘和中心云之间的协作，可以有效地缓解系统开销；考虑设备侧与边缘服务器层的流量负载预测、高效的资源分配调度策略，以最大程度地优化功耗和吞吐量。
附图说明
图1是本发明实施中移动边缘计算系统模型的结构框图；
图2是本发明实施例中预测负载流量框图；
图3是本发明实施例中边缘计算联合优化的效果图；
图4是本发明实施例中移动边缘计算中联合优化通信和计算资源方法流程图。
具体实施方式
下面结合附图对本发明作进一步描述。以下实施例仅用于更加清楚地说明本发明的技术方案，而不能以此来限制本发明的保护范围。
图1为本发明实施中移动边缘计算系统模型的结构框图，具体包括：
移动边缘计算系统模型总共分为三层，第一层是由终端资源请求者构成的用户设备层，由不同的物联网传感器设备组成，如智能手机、环境传感器和可穿戴设备等。第二层是由边缘计算资源提供者构成的边缘计算层，边缘服务器中的虚拟处理单元可以自适应地开启和关闭，可实时感知终端请求，提供设备接入、数据处理等服务，并且不同的边缘节点之间可通过有线链路进行任务传输。第三层是由集中式云服务器构成的中心云，包括存储容量大、计算能力强的服务器集群，提供大量的计算处理服务。边缘层与中心云之间采用无线通信方式OFDM进行数据传输，不同的无线通信链路之间采用正交信道，以避免受到其他通信链路的干扰。
在整个网络架构中，我们假设总共有M个边缘节点，采用Ai(t)来表示在t时刻到达边缘节点i的工作量，每个边缘服务器设置有一个缓冲区以存储外来任务，当任务抵达相应地边缘服务器之后，采用部分卸载方式对任务进行拆分，因此，边缘服务器上的任务处理主要包含三种方式：本地边缘服务器直接处理、发送到邻居边缘服务器进行处理以及发送到云服务器进行处理。
本具体实施例中，有三个边缘服务器，一个云服务器，信道带宽10MHz，信道噪声密度-174dB/Hz，发射功率最大0.5W，与芯片结构相关的有效系数为10-27，时隙长度为1ms，边缘服务器的CPU周期数为600cycles/bit，非负控制参数V为109。
下面介绍任务排队计算模型以及通信模型：
(1)通信模型
边缘服务器与云服务器直接采用无线链路通信方式OFDM，则根据香农定理可知边缘服务器i的节点传输速率表达式如下所示：
Figure GDA0003824375090000081
其中，N0表示高斯白噪声的功率谱密度，pi(t)和hi(t)分别表示边缘服务器的边缘节点i与云服务器之间的发射功率和信道功率，W为边缘服务器与云服务器之间的总信道带宽，ζi(t)表示所分配的带宽资源比例，τ表示时隙，通常设置为1ms。
(2)任务排队计算模型
边缘服务器上的任务队列Qi(t)的更新过程如下：
Figure GDA0003824375090000091
其中，Ai(t)表示从用户设备层的终端设备抵达边缘服务器的数据量，
Figure GDA0003824375090000092
表示从邻居边缘服务器卸载到本地边缘服务器的任务量，
Figure GDA0003824375090000093
表示直接在本地边缘服务器处理的任务量，
Figure GDA0003824375090000094
表示发送到邻居边缘服务器处理的任务量，
Figure GDA0003824375090000095
表示发送到云服务器处理的任务量；
边缘服务器既可以接收和计算移动用户发送的任务，也可以将接收到的数据包重新发送到邻近的边缘服务器或云服务器，考虑到每个边缘节点的计算资源相对有限，另外为了鼓励边缘节点之间的合作，我们需要添加以下约束，
Figure GDA0003824375090000096
其中，
Figure GDA0003824375090000097
表示处理1bit设备任务所需要的CPU周期数，σ表示一个小参数；云服务器上的任务队列G(t)更新如下，
Figure GDA0003824375090000098
其中，w(t)表示云服务器处理的任务量，
Figure GDA0003824375090000099
表示从边缘服务器卸载到云服务器的任务量。
考虑到云服务器上仅接收来自上一层边缘服务器发送过来的任务，建立以下约束：
Figure GDA0003824375090000101
图2是本发明实施例中预测负载流量框图，具体包括：
长期时隙定义为T，利用LSTM进行流量预测首先需要知道之前时刻的边缘节点的流量负载数据，利用这些数据对神经网络进行多次训练，以提高预测数据准确性，然后就可以预测当前T时隙内从边缘侧到达每个边缘节点的数据量。从图中，我们可以看出预测数据于原始数据基本一致，采用LSTM模型可以很好地捕捉到原始数据的整体趋势，预测数据结果具备一定的准确度。
图3是本发明实施例中边缘计算联合优化的效果图，具体包括:
将本发明提出的算法与本地计算、邻居边缘、本地边缘计算三种方法相比较，从图中可以看出，随着V的增大，各算法能耗不断降低，在V值较小时，本发明提出的算法相较于其他算法能耗优化效果更好。
图4为本发明实施例中移动边缘计算中联合优化通信和计算资源方法流程图，具体包括：
先制定一个优化问题，我们的目标是在保障系统能耗和吞吐量的情况下为边缘服务器上的所有任务分配合适的通信和计算资源，任务可以是本地边缘服务器直接处理，可以是发送到邻居边缘服务器进行处理，也可以是发送到云服务器进行远程处理。
优化目标是使得系统总体时间平均功耗和吞吐量最小化，式子类似表示为：
队列稳定性约束：
Figure GDA0003824375090000102
Figure GDA0003824375090000103
服务器计算资源约束：
0≤f(t)≤fmax
发射功率约束：
0≤p(t)≤pmax
通信带宽分配比例约束：
Figure GDA0003824375090000111
将原始优化问题分解为两个子问题：设备端到边缘服务器的流量预测问题以及考虑到功耗和吞吐量的边缘计算联合优化通信资源和计算资源问题包括：
在边缘服务器位置以及设备位置知道的情况下，可以知道任务的数据量，我们可以根据服务器覆盖范围以及用户数利用LSTM预测出抵达每个边缘服务器的工作负载流量。这个数据量受到设备和边缘服务器位置变化的影响，因为边缘服务器通常接收在其覆盖范围内的设备任务，倘若设备不断移动，超出当前本地边缘服务器的覆盖范围，则需要进行服务器切换，设备将会关联到其他的边缘服务器。
上述预测流量的值将会影响边缘服务器队列长度，在所有任务抵达边缘节点后，任务将采用三种方式进行处理，直接在本地边缘服务器处理的任务量与边缘服务器计算能力相关，传输到邻居边缘服务器处理的任务量应尽可能地小以减少时延损失，发送到云服务处理的任务量与通信传输速率相关。
在优化资源分配问题中将引入一个虚拟队
Figure GDA0003824375090000112
转化约束条件，然后采用李雅普诺夫优化方法进行队列稳定性条件转化，构造出李雅普诺夫加罚漂移函数，再结合约束条件去掉其中的常数项，构造新的优化目标函数。
经过李雅普诺夫优化后，基于FDMA的发射功率和带宽优化问题的表达式如下：
Figure GDA0003824375090000121
其中，V表示李雅普诺夫控制优化参数，λ表示一个放大系数，Ri(t)表示传输速率，pi(t)表示发射功率，ω1和ω2表示控制能耗和计算吞吐量的权重系数；
解决基于FDMA的发射功率和带宽优化问题包括：
提取上述表达式中的两个优化变量pi(t)和Ri(t)；
优化变量pi(t)的求解表达式如下：
Figure GDA0003824375090000122
Figure GDA0003824375090000123
通过运算获得pi(t)的最优解pi(t)*的表达式如下：
Figure GDA0003824375090000124
优化变量Ri(t)通过ζi(t)进行表示，ζi(t)的求解表达式如下：
Figure GDA0003824375090000125
Figure GDA0003824375090000126
构造对应的拉格朗日函数：
Figure GDA0003824375090000127
其中，a表示非负拉格朗日乘子；
通过拉格朗日函数对ζi(t)和a求偏导数：
Figure GDA0003824375090000131
最后利用KKT条件求出ζi(t)的最优解，从而获取Ri(t)的最优解。
优选的，边缘服务器和云服务器计算资源优化问题的表达式如下：
Figure GDA0003824375090000132
Figure GDA0003824375090000133
其中，
Figure GDA0003824375090000134
表示构造的一个虚拟队列，
Figure GDA0003824375090000135
Figure GDA0003824375090000136
表示边缘服务器i的计算能力，fc(t)表示云服务器的计算能力，G(t)表示云服务器队列长度，
Figure GDA0003824375090000137
表示处理1bit任务所需的CPU周期数，ke和kc表示和硬件相关的有效系数，σ表示一个小参数，Le表示边缘服务器的CPU频率；
解决边缘服务器和云服务器计算资源优化问题包括：
Figure GDA0003824375090000138
Figure GDA0003824375090000139
其中，fi e(t)*表示fi e(t)的最优解，fc(t)*表示fc(t)的最优解。
优选的，边缘服务器之间的任务迁移优化问题的表达式如下：
Figure GDA00038243750900001310
其中，Ai(t)表示从用户设备层的终端设备抵达边缘服务器的数据量，
Figure GDA00038243750900001311
表示从邻居边缘服务器卸载到本地边缘服务器的任务量，
Figure GDA00038243750900001312
表示直接在本地边缘服务器处理的任务量，
Figure GDA00038243750900001313
表示发送到邻居边缘服务器处理的任务量，
Figure GDA00038243750900001314
表示发送到云服务器处理的任务量；
解决边缘服务器之间的任务迁移优化问题包括：
得到了最佳服务器资源分配fi e(t)*和fc(t)*后，贪婪地选取最小的任务迁移到邻居边缘服务器。
本领域内的技术人员应明白，本申请的实施例可提供为方法、系统、或计算机程序产品。因此，本申请可采用完全硬件实施例、完全软件实施例、或结合软件和硬件方面的实施例的形式。而且，本申请可采用在一个或多个其中包含有计算机可用程序代码的计算机可用存储介质(包括但不限于磁盘存储器、CD-ROM、光学存储器等)上实施的计算机程序产品的形式。
以上所述仅是本发明的优选实施方式，应当指出，对于本技术领域的普通技术人员来说，在不脱离本发明技术原理的前提下，还可以做出若干改进和变形，这些改进和变形也应视为本发明的保护范围。

Claims (6)

1.一种基于移动边缘计算的通信和计算资源联合优化方法，其特征在于，包括以下步骤：
基于移动边缘计算系统模型、任务排队计算模型以及通信模型，制定进行任务卸载时最优化系统功耗和吞吐量的优化问题；
将优化问题分解为设备端到边缘服务器的负载流量预测问题和基于系统功耗和吞吐量的边缘计算联合优化通信资源和计算资源问题；
以最优化的系统功耗和吞吐量为目标，解决上述问题，从而完成资源分配任务；
其中，所述移动边缘计算系统模型基于移动边沿计算的任务调度和资源分配框架建立；
通过李雅普诺夫优化方法将通信资源和计算资源问题分解为多个子问题并逐一进行解决；所述子问题包括基于FDMA的发射功率和带宽优化问题、边缘服务器和云服务器计算资源优化问题、边缘服务器之间的任务迁移优化问题；
所述移动边缘计算系统模型包括：
由终端资源请求者构成的用户设备层，所述用户设备层包括多个不同的物联网传感器设备；
由边缘计算资源提供者构成的边缘计算层，所述边缘计算层包括边缘服务器以及边缘节点；其中，边缘服务器中的虚拟处理单元可以自适应地开启和关闭边缘节点，边缘节点分布在不同区域，可实时感知用户设备层的终端设备请求，提供设备接入、数据处理服务，并且不同的边缘节点之间可通过有线链路进行任务传输；
由集中式云服务器构成的中心云层，所述中心云层包括存储容量大、计算能力强的服务器集群，用于为边缘计算层提供大量的计算处理服务；
所述通信模型包括：
边缘服务器与云服务器直接采用无线链路通信方式OFDM进行任务传输；
根据香农定理，边缘服务器的边缘节点i传输速率Ri(t)表达式如下所示：
Figure FDA0003824375080000021
其中，N0表示高斯白噪声的功率谱密度，pi(t)和hi(t)分别表示边缘服务器的边缘节点i与云服务器之间的发射功率和信道功率，W为边缘服务器与云服务器之间的总信道带宽，ζi(t)表示所分配的带宽资源比例，τ表示时隙；
所述任务排队计算模型包括：
边缘服务器上的任务队列Qi(t)的更新过程的表达式如下所示：
Figure FDA0003824375080000022
其中，Ai(t)表示从用户设备层的终端设备抵达边缘服务器的数据量，
Figure FDA0003824375080000023
表示从邻居边缘服务器卸载到本地边缘服务器的任务量，
Figure FDA0003824375080000024
Figure FDA0003824375080000025
表示直接在本地边缘服务器处理的任务量，
Figure FDA0003824375080000026
表示发送到邻居边缘服务器处理的任务量，
Figure FDA0003824375080000027
表示发送到云服务器处理的任务量；M表示边缘节点的个数；
云服务器上的任务队列G(t)的更新过程的表达式如下所示：
Figure FDA0003824375080000028
其中，w(t)表示云服务器处理的任务量，
Figure FDA0003824375080000029
表示从边缘服务器卸载到云服务器的任务量；
所述负载流量预测问题包括：
根据已知的边缘服务器位置以及终端设备位置，获取任务的数据量；
基于任务的数据量，根据边缘服务器的覆盖范围以及用户数预测出抵达每个边缘服务器的工作负载流量；
所述通信资源和计算资源问题包括：
所有任务抵达边缘服务器的边缘节点后，
直接在本地边缘服务器处理的任务量与边缘服务器计算能力相关；
传输到邻居边缘服务器处理的任务量应尽可能地小以减少时延损失；
发送到云服务处理的任务量与通信传输速率相关；
所述边缘计算联合优化包括：
在优化问题中引入一个虚拟队列进行约束条件转化，采用李雅普诺夫优化方法进行队列稳定性条件转化，构造出李雅普诺夫加罚漂移函数，再结合约束条件，去掉其中的常数项，从而获得新的优化目标函数，通过优化目标函数进行边缘计算联合优化。
2.根据权利要求1所述的一种基于移动边缘计算的通信和计算资源联合优化方法，其特征在于，所述最优化系统功耗和吞吐量包括：队列稳定性约束，服务器计算资源约束，发射功率约束以及通信带宽分配比例约束。
3.根据权利要求1所述的一种基于移动边缘计算的通信和计算资源联合优化方法，其特征在于，解决负载流量预测问题包括：
通过训练好的LSTM神经网络进行负载流量预测，从而解决负载流量预测问题；所述LSTM神经网络的训练包括获取之前时刻的边缘节点的负载流量数据，并通过上述负载流量数据对LSTM神经网络进行多次训练。
4.根据权利要求1所述的一种基于移动边缘计算的通信和计算资源联合优化方法，其特征在于，所述基于FDMA的发射功率和带宽优化问题的表达式如下：
min:
Figure FDA0003824375080000041
其中，V表示李雅普诺夫控制优化参数，λ表示一个放大系数，Ri(t)表示传输速率，pi(t)表示发射功率，ω1和ω2表示控制能耗和计算吞吐量的权重系数；
解决所述基于FDMA的发射功率和带宽优化问题包括：
提取上述表达式中的两个优化变量pi(t)和Ri(t)；
优化变量pi(t)的求解表达式如下：
min:
Figure FDA0003824375080000042
Figure FDA0003824375080000043
通过运算获得pi(t)的最优解pi(t)*的表达式如下：
Figure FDA0003824375080000044
优化变量Ri(t)通过ζi(t)进行表示，ζi(t)的求解表达式如下：
min:
Figure FDA0003824375080000045
Figure FDA0003824375080000046
构造对应的拉格朗日函数：
Figure FDA0003824375080000047
其中，a表示非负拉格朗日乘子；
通过拉格朗日函数对ζi(t)和a求偏导数：
Figure FDA0003824375080000051
最后利用KKT条件求出ζi(t)的最优解，从而获取Ri(t)的最优解。
5.根据权利要求4所述的一种基于移动边缘计算的通信和计算资源联合优化方法，其特征在于，所述边缘服务器和云服务器计算资源优化问题的表达式如下：
min:
Figure FDA0003824375080000052
min：
Figure FDA0003824375080000053
其中，
Figure FDA0003824375080000054
表示构造的一个虚拟队列，
Figure FDA0003824375080000055
Figure FDA0003824375080000056
fi e(t)表示边缘服务器i的计算能力，fc(t)表示云服务器的计算能力，G(t)表示云服务器队列长度，
Figure FDA0003824375080000057
表示处理1bit任务所需的CPU周期数，ke和kc表示和硬件相关的有效系数，σ表示一个小参数，Le表示边缘服务器的CPU频率；
解决边缘服务器和云服务器计算资源优化问题包括：
Figure FDA0003824375080000058
Figure FDA0003824375080000059
其中，fi e(t)*表示fi e(t)的最优解，fc(t)*表示fc(t)的最优解。
6.根据权利要求5所述的一种基于移动边缘计算的通信和计算资源联合优化方法，其特征在于，所述边缘服务器之间的任务迁移优化问题的表达式如下：
Figure FDA0003824375080000061
其中，Ai(t)表示从用户设备层的终端设备抵达边缘服务器的数据量，
Figure FDA0003824375080000062
表示从邻居边缘服务器卸载到本地边缘服务器的任务量，
Figure FDA0003824375080000063
表示直接在本地边缘服务器处理的任务量，
Figure FDA0003824375080000064
表示发送到邻居边缘服务器处理的任务量，
Figure FDA0003824375080000065
表示发送到云服务器处理的任务量；
解决边缘服务器之间的任务迁移优化问题包括：
得到了最佳服务器资源分配fi e(t)*和fc(t)*后，贪婪地选取最小的任务迁移到邻居边缘服务器。
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Denomination of invention: Joint optimization of communication and computing resources based on mobile edge computing

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Denomination of invention: Joint optimization of communication and computing resources based on mobile edge computing

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Denomination of invention: Joint optimization of communication and computing resources based on mobile edge computing

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Denomination of invention: Joint optimization of communication and computing resources based on mobile edge computing

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Assignee: NANJING TIANHUA ZHONGAN COMMUNICATION TECHNOLOGY Co.,Ltd.

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Denomination of invention: Joint optimization of communication and computing resources based on mobile edge computing

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Contract record no.: X2023980051837

Denomination of invention: Joint optimization of communication and computing resources based on mobile edge computing

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Contract record no.: X2023980053773

Denomination of invention: Joint optimization of communication and computing resources based on mobile edge computing

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Assignee: PHOTON COMMUNICATION Corp.

Assignor: NANJING University OF POSTS AND TELECOMMUNICATIONS

Contract record no.: X2023980053419

Denomination of invention: Joint optimization of communication and computing resources based on mobile edge computing

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Assignee: NANJING HUADONG ELECTRONICS VACUUM MATERIAL Co.,Ltd.

Assignor: NANJING University OF POSTS AND TELECOMMUNICATIONS

Contract record no.: X2023980053414

Denomination of invention: Joint optimization of communication and computing resources based on mobile edge computing

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Assignee: Nanjing Hefeng Operation Management Co.,Ltd.

Assignor: NANJING University OF POSTS AND TELECOMMUNICATIONS

Contract record no.: X2023980053384

Denomination of invention: Joint optimization of communication and computing resources based on mobile edge computing

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License type: Common License

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Assignee: NANJING DIXIN COORDINATE INFORMATION TECHNOLOGY CO.,LTD.

Assignor: NANJING University OF POSTS AND TELECOMMUNICATIONS

Contract record no.: X2023980053374

Denomination of invention: Joint optimization of communication and computing resources based on mobile edge computing

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Denomination of invention: Joint optimization of communication and computing resources based on mobile edge computing

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Contract record no.: X2023980054131

Denomination of invention: Joint optimization of communication and computing resources based on mobile edge computing

Granted publication date: 20221209

License type: Common License

Record date: 20231227

Application publication date: 20210820

Assignee: NANJING YIZHIHENG SOFTWARE TECHNOLOGY Co.,Ltd.

Assignor: NANJING University OF POSTS AND TELECOMMUNICATIONS

Contract record no.: X2023980054071

Denomination of invention: Joint optimization of communication and computing resources based on mobile edge computing

Granted publication date: 20221209

License type: Common License

Record date: 20231227