CN111263401A - Multi-user cooperative computing unloading method based on mobile edge computing - Google Patents
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Abstract
Description
Translated fromChinese技术领域technical field
本发明属于无线通信技术领域，尤其是涉及移动边缘计算的通信系统架构下多用户的协作计算卸载。The invention belongs to the technical field of wireless communication, and in particular relates to multi-user cooperative computing offloading under the communication system architecture of mobile edge computing.
背景技术Background technique
随着越来越多的应用例如计算机视觉(CV)、人工智能(AI)等的发展，日常使用的移动设备由于计算能力和电池容量等限制不能提供令人满意的用户体验。移动边缘计算(MEC)作为一种新兴的计算技术，近年来引起了学术界和工业界的广泛关注。MEC能够在移动设备附近的蜂窝网络边缘提供计算资源。但由于MEC服务器是由所有移动设备“共享”的，MEC服务器上有限的计算资源并不总是足以支持其覆盖范围内的所有移动设备。并且虽然将任务卸载到计算能力更强的MEC服务器可以降低任务响应延迟，但数据通过无线信道传输到MEC服务器，这会导致额外的传输延迟和能量消耗。现有的多用户卸载方法，要么是基于设备本地与MEC服务器的协同卸载，要么是引入D2D的协同卸载。这些卸载方法不能很好地利用整个系统内的计算资源、面对基站计算能力较弱而用户的任务计算需求较大的情况会造成较长时延和较差的用户体验。With the development of more and more applications such as computer vision (CV), artificial intelligence (AI), etc., mobile devices used daily cannot provide a satisfactory user experience due to limitations such as computing power and battery capacity. Mobile edge computing (MEC), as an emerging computing technology, has attracted extensive attention from academia and industry in recent years. MEC can provide computing resources at the edge of the cellular network near mobile devices. But since the MEC server is "shared" by all mobile devices, the limited computing resources on the MEC server are not always sufficient to support all mobile devices within its coverage. And although offloading tasks to the MEC server with more computing power can reduce the task response delay, the data is transmitted to the MEC server through the wireless channel, which causes additional transmission delay and energy consumption. The existing multi-user offloading methods are either based on the collaborative offloading between the device and the MEC server, or the collaborative offloading by introducing D2D. These offloading methods cannot make good use of the computing resources in the entire system, and will result in longer delay and poorer user experience when the base station has weak computing power and the user's task computing demand is large.
因此，如何设计高效的计算卸载方法来提高MEC系统在任务执行延迟和能量消耗两方面的性能，是MEC系统的一个关键和基础问题。目前解决上述问题的方法分为三类：1)移动设备独立进行卸载决策，使用该方法一旦计算资源利用率超过MEC服务器的某个阈值，即将任务卸载到远程云。由于主干网传输延迟导致任务的执行延迟增加。2)移动设备协同进行卸载决策，该方法根据优先级提供MEC服务器的计算资源，在这种情况下，只有部分手机设备可以享受计算卸载的好处。3)允许设备到设备(D2D)计算卸载，移动设备可以将任务卸载到MEC服务器，或者通过D2D链路将任务卸载到相邻的计算节点。但是由于D2D通信范围较短，因此D2D通信受限。Therefore, how to design an efficient computational offload method to improve the performance of MEC systems in terms of task execution delay and energy consumption is a key and fundamental issue of MEC systems. The current methods to solve the above problems are divided into three categories: 1) The mobile device makes the offloading decision independently, using this method to offload the task to the remote cloud once the computing resource utilization exceeds a certain threshold of the MEC server. The execution delay of the task increases due to the transmission delay of the backbone network. 2) The mobile devices cooperate to make the offloading decision. This method provides the computing resources of the MEC server according to the priority. In this case, only some mobile devices can enjoy the benefits of computing offloading. 3) To allow device-to-device (D2D) computing offload, mobile devices can offload tasks to the MEC server, or offload tasks to adjacent computing nodes through D2D links. However, D2D communication is limited due to the short range of D2D communication.
发明内容SUMMARY OF THE INVENTION
针对多用户MEC系统的计算卸载问题这一技术问题，本发明提出了一种基于移动边缘计算的多用户协作计算卸载方法，根据任务到达率的变化将MEC服务覆盖区域内的移动设备分为繁忙设备和空闲设备，利用空闲设备的计算资源协作计算卸载，实现整个移动通信系统的性能提升。Aiming at the technical problem of computing offloading in a multi-user MEC system, the present invention proposes a multi-user collaborative computing offloading method based on mobile edge computing. According to the change of task arrival rate, the mobile devices in the MEC service coverage area are divided into busy ones. The device and the idle device use the computing resources of the idle device to coordinate computing offload, so as to improve the performance of the entire mobile communication system.
本发明的一种基于移动边缘计算的多用户协作计算卸载方法，该方法包括以下步骤：A kind of multi-user collaborative computing offloading method based on mobile edge computing of the present invention, the method comprises the following steps:
步骤1、初始化区域内的设备总数量N，任务在每个时隙的开始到达各个设备，可以在本地执行、或者被卸载到MEC服务器同时在MEC服务器上执行、或者通过MEC服务器被卸载到空闲设备并在空闲设备上执行；Step 1. The total number of devices in the initialization area is N. The task arrives at each device at the beginning of each time slot, and can be executed locally, or offloaded to the MEC server and executed on the MEC server at the same time, or offloaded to idle through the MEC server. device and execute on an idle device;
步骤2、根据任务到达率将设备划分为忙碌设备J、空闲设备K；Step 2. According to the task arrival rate, the equipment is divided into busy equipment J and idle equipment K;
步骤3、初始化设备参数、任务参数及通信链路参数；初始化设备的决策矩阵D为全部为本地执行；
步骤4、确定任务的切片分配给K空闲设备；Step 4. Determine that the slice of the task is allocated to K idle devices;
步骤5、判断设备j在时隙t到达了任务It,j.
步骤6、分别考虑任务在3个目的地的执行情况：处理这个任务所需的总CPU周期为St,j，每处理一个CPU周期消耗的能量为et,j，设备j的完整计算能力为Cj，可利用的比率为βt，j；
情况一、本地执行：Case 1. Local execution:
本地执行所需时间表达式为：The expression for the time required for local execution is:
本地执行能量消耗表达式为：The local execution energy consumption expression is:
本地执行的执行负载表达式为：The execution load expression for local execution is:
其中，λ为权重比例参数；Among them, λ is the weight ratio parameter;
情况二、MEC卸载执行：Situation 2. MEC uninstall execution:
无线网络链路传输速率表达式为：The wireless network link transmission rate is expressed as:
数据传输时间表达式为：The data transfer time expression is:
本地计算时间表达式为：The local calculation time expression is:
MEC卸载执行所需时间的表达式为：The expression for the time required for MEC offload execution is:
其中，Qt,j为MEC服务器任务排队的动态函数；Among them, Qt,j is the dynamic function of MEC server task queuing;
MEC卸载执行能量消耗的表达式为：The expression for the energy consumption of MEC offloading execution is:
MEC卸载执行的执行负载的表达式为：The expression for the execution load of MEC offloading execution is:
情况三、空闲设备卸载执行：Scenario 3: Uninstall the idle device and execute:
任务通过MEC服务器卸载到多个空闲设备，为了保证任务在空闲设备上并行计算的时间最短，确定任务的切片分配给K空闲设备的方式θt,k；The task is unloaded to multiple idle devices through the MEC server. In order to ensure the shortest time for the task to be calculated in parallel on the idle device, determine the way θt,k that the slice of the task is allocated to K idle devices;
任务在MEC服务器被切片分给空闲设备K的时间包括数据传输时间和K计算时间：The time when the task is sliced and distributed to the idle device K on the MEC server includes the data transmission time and the K calculation time:
数据传输时间的表达式为：The expression for data transfer time is:
任务到达多个空闲设备，计算分片任务在设备K上执行的计算时间：The task arrives at multiple idle devices, and the computation time of the sharded task execution on device K is calculated:
K计算时间的表达式为：The expression for K calculation time is:
总时间及分配目标的表达式为：The expressions for the total time and allocation target are:
空闲设备卸载执行所需时间的表达式为：The expression for the time required for the offload execution of the idle device is:
空闲设备卸载执行能量消耗的表达式为：The expression for the energy consumption of idle device offloading execution is:
空闲设备卸载执行的执行负载的表达式为：The expression for the execution load of idle device offload execution is:
步骤6、依据以下条件，比较选择最小系统负载的目的地：
当
当
当
步骤7、此为设备j在时隙t的最优决策
步骤8、如果设备j决定更新决策矩阵，设备j向MEC服务器发送更新决策请求消息；
步骤9、MEC服务器收到更新决策的请求会发回一个确认消息，进行最优决策更新；Step 9. When the MEC server receives the request to update the decision, it will send back a confirmation message to update the optimal decision;
步骤10、其他的繁忙设备在MEC服务器的帮助下也得到设备j更新决策的信息，了解当前系统的计算资源使用情况并以同样的方式根据步骤6更新决策；
当MEC服务器不再接收到更新决策矩阵的请求消息时，达到系统的收敛状态；此时，决策矩阵D为最优的协作计算卸载方法。When the MEC server no longer receives the request message to update the decision matrix, the convergence state of the system is reached; at this time, the decision matrix D is the optimal unloading method for cooperative computing.
与现有技术相比，本发明可以在单个小型基站为区域内用户提供计算资源的情况下扩大系统的计算能力，降低平均任务响应时延，使用户得到更良好的用户体验，可以更好地提升整个系统的性能Compared with the prior art, the present invention can expand the computing capability of the system under the condition that a single small base station provides computing resources for users in the area, reduce the average task response delay, enable users to obtain better user experience, and can better Improve overall system performance
附图说明Description of drawings
图1为本发明的一种基于移动边缘计算的多用户协作计算卸载方法流程示意图；1 is a schematic flowchart of a method for offloading multi-user collaborative computing based on mobile edge computing according to the present invention;
图2为移动设备分布示例(繁忙设备数量为35、空闲设备数量为15)。Figure 2 is an example of the distribution of mobile devices (the number of busy devices is 35 and the number of idle devices is 15).
具体实施方式Detailed ways
下面结合附图和实施例对本发明作进一步的说明，但并不作为对本发明限制的依据。The present invention will be further described below in conjunction with the accompanying drawings and embodiments, but not as a basis for limiting the present invention.
如图1所示，为本发明的一种基于移动边缘计算的多用户协作计算卸载方法流程示意图，具体操作步骤如下：As shown in FIG. 1, it is a schematic flowchart of a method for unloading multi-user collaborative computing based on mobile edge computing according to the present invention, and the specific operation steps are as follows:
步骤1、初始化区域内的设备总数量N，任务在每个时隙的开始到达各个设备，可以在本地执行、或者被卸载到MEC服务器同时在MEC服务器上执行、或者通过MEC服务器被卸载到空闲设备并在空闲设备上执行；Step 1. The total number of devices in the initialization area is N. The task arrives at each device at the beginning of each time slot, and can be executed locally, or offloaded to the MEC server and executed on the MEC server at the same time, or offloaded to idle through the MEC server. device and execute on an idle device;
步骤2、根据任务到达率将设备划分为忙碌设备J、空闲设备K(N＝J+K)；Step 2. According to the task arrival rate, the devices are divided into busy devices J and idle devices K (N=J+K);
步骤3、初始化设备参数(包括设备位置、设备计算能力CN、MEC服务器计算能力Cm、设备功率P)、任务参数(包括计算输入数据的大小It,j、处理任务所需的总CPU周期St，j＝αIt，j、部分卸载的比例系数为ω)及通信链路参数(包括信道带宽B、信道增益GN、噪声功率N0)，初始化设备的决策矩阵D为全部为本地执行；
步骤4、任务可以通过MEC服务器卸载到多个空闲设备，为了保证并行计算的时间相同，确定任务的切片分配给K空闲设备；Step 4. The task can be offloaded to multiple idle devices through the MEC server. In order to ensure that the parallel computing time is the same, it is determined that the slice of the task is allocated to K idle devices;
步骤5、判断设备j在时隙t到达了任务It,j？
步骤6、分别考虑任务在3个目的地的执行情况：处理这个任务所需的总CPU周期为St,j，每处理一个CPU周期消耗的能量为et,j(取决于设备的芯片架构的有效开关电容系数η)。设备j的完整计算能力为Cj，可利用的比率为βt，j；
情况一、本地执行：Case 1. Local execution:
本地执行所需时间表达式为：The expression for the time required for local execution is:
本地执行能量消耗表达式为：The local execution energy consumption expression is:
本地执行的执行负载表达式为：The execution load expression for local execution is:
其中，λ为权重比例参数；Among them, λ is the weight ratio parameter;
情况二、MEC卸载执行：Situation 2. MEC uninstall execution:
无线网络链路传输速率表达式为：The wireless network link transmission rate is expressed as:
数据传输时间表达式为：The data transfer time expression is:
本地计算时间表达式为：The local calculation time expression is:
MEC卸载执行所需时间的表达式为：The expression for the time required for MEC offload execution is:
其中，Qt,j为MEC服务器任务排队的动态函数；Among them, Qt,j is the dynamic function of MEC server task queuing;
MEC卸载执行能量消耗的表达式为：The expression for the energy consumption of MEC offloading execution is:
MEC卸载执行的执行负载的表达式为：The expression for the execution load of MEC offloading execution is:
情况三、空闲设备卸载执行：Scenario 3: Uninstall the idle device and execute:
任务可以通过MEC服务器卸载到多个空闲设备，为了保证任务在空闲设备上并行计算的时间最短，确定任务的切片分配给K空闲设备的方式θt,k。Tasks can be offloaded to multiple idle devices through the MEC server. In order to ensure the shortest time for tasks to be calculated in parallel on idle devices, the method θt,k of assigning task slices to K idle devices is determined.
任务在MEC服务器被切片分给空闲设备K的时间包括数据传输时间和K计算时间：The time when the task is sliced and distributed to the idle device K on the MEC server includes the data transmission time and the K calculation time:
数据传输时间的表达式为：The expression for data transfer time is:
任务到达多个空闲设备，计算分片任务在设备K上执行的计算时间：The task arrives at multiple idle devices, and the computation time of the sharded task execution on device K is calculated:
K计算时间的表达式为：The expression for K calculation time is:
总时间及分配目标的表达式为：The expressions for the total time and allocation target are:
空闲设备卸载执行所需时间的表达式为：The expression for the time required for the offload execution of the idle device is:
空闲设备卸载执行能量消耗的表达式为：The expression for the energy consumption of idle device offloading execution is:
空闲设备卸载执行的执行负载的表达式为：The expression for the execution load of idle device offload execution is:
步骤6、依据以下条件，比较选择最小系统负载的目的地：
当
当
当
步骤7、此为设备j在时隙t的最优决策
步骤8、如果设备j决定更新决策矩阵，设备j向MEC服务器发送更新决策请求消息；
步骤9、MEC服务器收到更新决策的请求会发回一个确认消息，进行最优决策更新；Step 9. When the MEC server receives the request to update the decision, it will send back a confirmation message to update the optimal decision;
步骤10、其他的繁忙设备在MEC服务器的帮助下也得到设备j更新决策的信息，了解当前系统的计算资源使用情况并以同样的方式根据步骤6更新决策；
当MEC服务器不再接收到更新决策矩阵的请求消息时，达到系统的收敛状态；此时，决策矩阵D为最优的协作计算卸载方法。When the MEC server no longer receives the request message to update the decision matrix, the convergence state of the system is reached; at this time, the decision matrix D is the optimal unloading method for cooperative computing.
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| CN112600869A (en)* | 2020-11-11 | 2021-04-02 | 南京邮电大学 | Calculation unloading distribution method and device based on TD3 algorithm |
| CN113342409A (en)* | 2021-04-25 | 2021-09-03 | 山东师范大学 | Delay sensitive task unloading decision method and system for multi-access edge computing system |
| CN114189521A (en)* | 2021-12-15 | 2022-03-15 | 福州大学 | A method for collaborative computing offload in F-RAN architecture |
| CN114189521B (en)* | 2021-12-15 | 2024-01-26 | 福州大学 | Method for collaborative computing offloading in F-RAN architecture |
| CN114900860A (en)* | 2022-05-05 | 2022-08-12 | 中国联合网络通信集团有限公司 | Mobile terminal edge calculation method, device, edge calculation server and medium |
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| CN115314942A (en)* | 2022-07-11 | 2022-11-08 | 中国科学院深圳先进技术研究院 | Load balancing method of unmanned aerial vehicle-assisted energy transmission mobile edge computing system |
| CN115314942B (en)* | 2022-07-11 | 2024-08-09 | 中国科学院深圳先进技术研究院 | Load balancing method for UAV-assisted energy transmission mobile edge computing system |
| CN118972903A (en)* | 2024-10-18 | 2024-11-15 | 浙江理工大学 | A novel IIoT-MEC task offloading method for joint optimization of latency and energy consumption |
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