As a combination of edge computing and artificial intelligence,edge intelligence has become a promising technique and provided its users with a series of fast,precise,and customized services.In edge intelligence,when ...As a combination of edge computing and artificial intelligence,edge intelligence has become a promising technique and provided its users with a series of fast,precise,and customized services.In edge intelligence,when learning agents are deployed on the edge side,the data aggregation from the end side to the designated edge devices is an important research topic.Considering the various importance of end devices,this paper studies the weighted data aggregation problem in a single hop end-to-edge communication network.Firstly,to make sure all the end devices with various weights are fairly treated in data aggregation,a distributed end-to-edge cooperative scheme is proposed.Then,to handle the massive contention on the wireless channel caused by end devices,a multi-armed bandit(MAB)algorithm is designed to help the end devices find their most appropriate update rates.Diffe-rent from the traditional data aggregation works,combining the MAB enables our algorithm a higher efficiency in data aggregation.With a theoretical analysis,we show that the efficiency of our algorithm is asymptotically optimal.Comparative experiments with previous works are also conducted to show the strength of our algorithm.展开更多
In order to solve the high latency of traditional cloud computing and the processing capacity limitation of Internet of Things(IoT)users,Multi-access Edge Computing(MEC)migrates computing and storage capabilities from...In order to solve the high latency of traditional cloud computing and the processing capacity limitation of Internet of Things(IoT)users,Multi-access Edge Computing(MEC)migrates computing and storage capabilities from the remote data center to the edge of network,providing users with computation services quickly and directly.In this paper,we investigate the impact of the randomness caused by the movement of the IoT user on decision-making for offloading,where the connection between the IoT user and the MEC servers is uncertain.This uncertainty would be the main obstacle to assign the task accurately.Consequently,if the assigned task cannot match well with the real connection time,a migration(connection time is not enough to process)would be caused.In order to address the impact of this uncertainty,we formulate the offloading decision as an optimization problem considering the transmission,computation and migration.With the help of Stochastic Programming(SP),we use the posteriori recourse to compensate for inaccurate predictions.Meanwhile,in heterogeneous networks,considering multiple candidate MEC servers could be selected simultaneously due to overlapping,we also introduce the Multi-Arm Bandit(MAB)theory for MEC selection.The extensive simulations validate the improvement and effectiveness of the proposed SP-based Multi-arm bandit Method(SMM)for offloading in terms of reward,cost,energy consumption and delay.The results showthat SMMcan achieve about 20%improvement compared with the traditional offloading method that does not consider the randomness,and it also outperforms the existing SP/MAB based method for offloading.展开更多
随着电力物联网(electric Internet of Things,eIoT)技术的快速发展,海量电力设备在网络边缘环境中产生了丰富的数据。移动边缘计算(Mobile Edge Computing,MEC)技术在靠近终端设备的位置部署边缘代理可以有效减少数据处理延迟,这使其...随着电力物联网(electric Internet of Things,eIoT)技术的快速发展,海量电力设备在网络边缘环境中产生了丰富的数据。移动边缘计算(Mobile Edge Computing,MEC)技术在靠近终端设备的位置部署边缘代理可以有效减少数据处理延迟,这使其非常适用于延迟敏感的电力物联网场景。然而,目前的大多数研究没有考虑到部分边缘终端设备也可以作为代理设备提供计算服务,造成了资源浪费。为了充分利用移动边缘计算过程中边缘代理以及边缘终端设备的计算能力,提出了一种基于设备聚类的任务卸载方案。首先,基于分层DBSCAN(hierarchical density-based spatial clustering of applications with noise)算法,对系统中的静态和动态边缘设备进行聚类。其次,将任务卸载问题建模为多臂老虎机(Multi-Armed Bandits,MAB)模型,目标为最小化卸载延迟。再次,提出了一种基于自适应置信上限算法的算法来寻找簇内与簇间的卸载策略。最后,仿真结果表明,该方案在平均延迟方面表现出了更好的性能,并且设备簇的存活时间延长了10%~20%。展开更多
基金supported by the National Natural Science Foundation of China(NSFC)(62102232,62122042,61971269)Natural Science Foundation of Shandong Province Under(ZR2021QF064)。
文摘As a combination of edge computing and artificial intelligence,edge intelligence has become a promising technique and provided its users with a series of fast,precise,and customized services.In edge intelligence,when learning agents are deployed on the edge side,the data aggregation from the end side to the designated edge devices is an important research topic.Considering the various importance of end devices,this paper studies the weighted data aggregation problem in a single hop end-to-edge communication network.Firstly,to make sure all the end devices with various weights are fairly treated in data aggregation,a distributed end-to-edge cooperative scheme is proposed.Then,to handle the massive contention on the wireless channel caused by end devices,a multi-armed bandit(MAB)algorithm is designed to help the end devices find their most appropriate update rates.Diffe-rent from the traditional data aggregation works,combining the MAB enables our algorithm a higher efficiency in data aggregation.With a theoretical analysis,we show that the efficiency of our algorithm is asymptotically optimal.Comparative experiments with previous works are also conducted to show the strength of our algorithm.
基金This work was supported in part by the Zhejiang Lab under Grant 20210AB02in part by the Sichuan International Science and Technology Innovation Cooperation/Hong Kong,Macao and Taiwan Science and Technology Innovation Cooperation Project under Grant 2019YFH0163in part by the Key Research and Development Project of Sichuan Provincial Department of Science and Technology under Grant 2018JZ0071.
文摘In order to solve the high latency of traditional cloud computing and the processing capacity limitation of Internet of Things(IoT)users,Multi-access Edge Computing(MEC)migrates computing and storage capabilities from the remote data center to the edge of network,providing users with computation services quickly and directly.In this paper,we investigate the impact of the randomness caused by the movement of the IoT user on decision-making for offloading,where the connection between the IoT user and the MEC servers is uncertain.This uncertainty would be the main obstacle to assign the task accurately.Consequently,if the assigned task cannot match well with the real connection time,a migration(connection time is not enough to process)would be caused.In order to address the impact of this uncertainty,we formulate the offloading decision as an optimization problem considering the transmission,computation and migration.With the help of Stochastic Programming(SP),we use the posteriori recourse to compensate for inaccurate predictions.Meanwhile,in heterogeneous networks,considering multiple candidate MEC servers could be selected simultaneously due to overlapping,we also introduce the Multi-Arm Bandit(MAB)theory for MEC selection.The extensive simulations validate the improvement and effectiveness of the proposed SP-based Multi-arm bandit Method(SMM)for offloading in terms of reward,cost,energy consumption and delay.The results showthat SMMcan achieve about 20%improvement compared with the traditional offloading method that does not consider the randomness,and it also outperforms the existing SP/MAB based method for offloading.
文摘随着电力物联网(electric Internet of Things,eIoT)技术的快速发展,海量电力设备在网络边缘环境中产生了丰富的数据。移动边缘计算(Mobile Edge Computing,MEC)技术在靠近终端设备的位置部署边缘代理可以有效减少数据处理延迟,这使其非常适用于延迟敏感的电力物联网场景。然而,目前的大多数研究没有考虑到部分边缘终端设备也可以作为代理设备提供计算服务,造成了资源浪费。为了充分利用移动边缘计算过程中边缘代理以及边缘终端设备的计算能力,提出了一种基于设备聚类的任务卸载方案。首先,基于分层DBSCAN(hierarchical density-based spatial clustering of applications with noise)算法,对系统中的静态和动态边缘设备进行聚类。其次,将任务卸载问题建模为多臂老虎机(Multi-Armed Bandits,MAB)模型,目标为最小化卸载延迟。再次,提出了一种基于自适应置信上限算法的算法来寻找簇内与簇间的卸载策略。最后,仿真结果表明,该方案在平均延迟方面表现出了更好的性能,并且设备簇的存活时间延长了10%~20%。