检索结果-内蒙古大学图书馆

A fault-tolerant dynamic scheduling method on hierarchical mobile edge cloud computing

COMPUTATIONAL INTELLIGENCE 2019年第3期35卷 577-598页

作者： Meng, Shunmei Li, Qianmu Wu, Taoran Huang, Weijia Zhang, Jing Li, Weimin Nanjing Univ Sci & Technol Dept Comp Sci & Engn Nanjing Jiangsu Peoples R China Nanjing Univ State Key Lab Novel Software Technol Nanjing Jiangsu Peoples R China Northeastern Univ Coll Profess Studies Boston MA 02115 USA Shanghai Univ Sch Comp Engn & Technol Shanghai Peoples R China

mobile edge cloud computing has been a promising computing paradigm, where mobile users could offload their application workloads to low-latency local edge cloud resources. However, compared with remote public cloud resources, conventional local edge cloud resources are limited in computation capacity, especially when serve large number of mobile applications. To deal with this problem, we present a hierarchical edge cloud architecture to integrate the local edge clouds and public clouds so as to improve the performance and scalability of scheduling problem for mobile applications. Besides, to achieve a trade-off between the cost and system delay, a fault-tolerant dynamic resource scheduling method is proposed to address the scheduling problem in mobile edge cloud computing. The optimization problem could be formulated to minimize the application cost with the user-defined deadline satisfied. Specifically, firstly, a game-theoretic scheduling mechanism is adopted for resource provisioning and scheduling for multiprovider mobile applications. Then, a mobility-aware dynamic scheduling strategy is presented to update the scheduling with the consideration of mobility of mobile users. Moreover, a failure recovery mechanism is proposed to deal with the uncertainties during the execution of mobile applications. Finally, experiments are designed and conducted to validate the effectiveness of our proposal. The experimental results show that our method could achieve a trade-off between the cost and system delay.

关键词： dynamic scheduling fault-tolerant scheduling hierarchical cloud mobile edge cloud computing

来源：评论

学校读者我要写书评

暂无评论

A Comparative Study of mobile cloud computing, mobile edge computing, and mobile edge cloud computing

A Comparative Study of Mobile Cloud Computing, Mobile Edge C...

引用

2023 Congress in Computer Science, Computer Engineering, and Applied computing, CSCE 2023

作者： Nam, Deok Hee Wilberforce University Engineering & Computer Science WilberforceOH United States

ISBN: (纸本)9798350327595

mobile computing paradigms have undergone significant transformations with the rise of mobile cloud computing (MCC), mobile edge computing (MEC), and mobile edge cloud computing (MECC). These paradigms offer distinct approaches to address the challenges of mobile application development, resource management, and latency reduction. MECC combines the benefits of MCC and MEC, integrating cloud and edge resources to leverage their respective advantages. MECC offers a hybrid architecture where tasks can be dynamically allocated between cloud servers and edge nodes based on factors such as task requirements, latency constraints, and resource availability. This paper presents a comprehensive review through the comparative analysis of MCC, MEC, and MECC, focusing on key aspects such as architecture, resource allocation, latency reduction, and scalability. © 2023 IEEE.

关键词： latency reduction mobile cloud computing mobile edge cloud computing mobile edge computing resource allocation scalability

来源：评论

学校读者我要写书评

暂无评论

Delay-aware resource allocation for partial computation offloading in mobile edge cloud computing

引用

PERVASIVE AND mobile computing 2024年 105卷

作者： Yu, Lingfei Xu, Hongliu Zeng, Yunhao Deng, Jiali Zhejiang Gongshang Univ Hangzhou Coll Commerce Hangzhou 310018 Zhejiang Peoples R China Univ Elect Sci & Technol China Yangtze Delta Reg Inst Quzhou Quzhou 324000 Zhejiang Peoples R China Tonglu High level Talent Serv Ctr Tonglu Fuchun Ecommerce Res Inst Hangzhou 311500 Zhejiang Peoples R China

mobile edge cloud computing (MECC), as a promising partial computing offloading solution, has provided new possibilities for compute-intensive and delay-sensitive mobile applications, which can simultaneously leverage edge computing and cloud services. However, designing resource allocation strategies for MECC faces an extremely challenging problem of simultaneously satisfying the end-to-end latency requirements and minimum resource allocation of multiple mobile applications. To address this issue, we comprehensively consider the randomness of computing request arrivals, service time, and dynamic computing resources. We model the MECC network as a two-level tandem queue consisting of two sequential computing processing queues, each with multiple servers. We apply a deep reinforcement learning algorithm called Deep Deterministic Policy Gradient (DDPG) to learn the computing speed adjustment strategy for the tandem queue. This strategy ensures the end-to-end latency requirements of multiple mobile applications while preventing overuse of the total computing resources of edge servers and cloud servers. Numerous simulation experiments demonstrate that our approach is significantly superior to other methods in dynamic network environments.

关键词： mobile edge cloud computing Computation offloading Resource allocation Delay-aware Deep reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Partial Offloading in Energy Harvested mobile edge computing: A Direct Search Approach

引用

IEEE ACCESS 2020年 8卷 36757-36763页

作者： Mahmood, Asad Ahmed, Ashfaq Naeem, Muhammad Hong, Yue Shenzhen Univ Coll Mechatron & Control Engn Shenzhen 518060 Peoples R China COMSATS Univ Islamabad Dept Elect & Comp Engn Wah Campus Wah Cantt 47040 Pakistan

In the next generation wireless communication paradigm, the number of devices are expected to increase exponentially after the concept of Internet of Things (IoT). These devices are power constrained, with limited processing capability. Therefore, in order to get the maximum advantage from these low power IoT sensing devices, it is of utmost need to empower them. Similarly, the devices are not able to process the computationally intensive applications. In this work, Wireless Power mobile edge cloud (WPMEC) is considered, which is an integration of Wireless Power Transfer (WPT) and mobile edge cloud (MEC) to address low power devices battery and computational capabilities. The WPMEC is charging the devices in the first phase using the WPT and in the second phase, the devices are offloading their computational intensive data to the MEC. Partial offloading scheme is first time introduced and analyzed with WPMEC. Performance of proposed solution is evaluated in terms of overall network computational energy efficiency. Extensive simulations have been carried out to validate the proposed solution. It is shown that the proposed partial offloading scheme with WPMEC outperforms the binary and local computational schemes.

关键词： Wireless Power Transfer mobile edge cloud computing Eergy Efficiency

来源：评论

学校读者我要写书评

暂无评论

Optimizing the Micro-Architectural Performance of the Current and Emerging edge Infrastructure

引用

IEEE TRANSACTIONS ON cloud computing 2024年第1期12卷 40-52页

作者： Wang, Jianda Wang, Zhen Wu, Weili Hu, Yang Dalian Maritime Univ Sch Informat Sci & Technol Dalian 116026 Peoples R China Univ Texas Dallas Dept Elect & Comp Engn Richardson TX 75080 USA Univ Texas Dallas Dept Comp Sci Richardson TX 75080 USA Tsinghua Univ Sch Integrated Circuits Beijing 100190 Peoples R China

The Network Function Virtualization (NFV) is the essential technology proposed to tackle the next-generation mobile system's various flexibility features. In this article, we implement a thorough micro-architectural performance investigation on the NFV-enabled edge virtual Radio Access Network (vRAN) and the emerging 5G new-radio (nr) platform to unveil the main micro-architectural bottlenecks of the next-generation network's vRAN system. Based on our experimental results, we find that the high core bound hinders the processing speed of the vRAN and 5G nr platforms. Several solutions alleviating the vRAN's core bound are proposed to accelerate the vRAN system's processing speed. Besides, we observe that the current co-location strategy cannot maximize the COTS servers' CPU utilization and meanwhile eliminate the system hang-up caused by CPU resource contention. We fill this gap by proposing an optimized co-location strategy based on our observed vRAN co-location characterization. Finally, we detect that on the modern hyper-threading-enabled COTS servers, the current pin core policy of 5G nr will cause L3 cache contention, which will lead to severe system hang-up. A novel threads management mechanism is proposed to eliminate this system hang-up on the hyper-threading-enabled COTS servers.

关键词： Servers 5G mobile communication cloud computing Downlink Bandwidth Network function virtualization Encoding 5G new radio micro-architecture characterization mobile edge cloud computing radio access network

来源：评论

学校读者我要写书评

暂无评论

Optimal Resource Allocation and Task Segmentation in IoT Enabled mobile edge cloud

引用

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2021年第12期70卷 13294-13303页

作者： Mahmood, Asad Hong, Yue Ehsan, Muhammad Khurram Mumtaz, Shahid Shenzhen Univ Coll Mechatron & Control Engn Shenzhen 518060 Peoples R China Univ Luxembourg Interdisciplinary Ctr Secur Reliabil & Trust SnT L-4365 Luxembourg Luxembourg Bahria Univ Fac Engn Lahore Campus Lahore 54600 Pakistan IT Aveiro Inst Telecomunicacoes P-3810193 Aveiro Portugal Univ Antonio Nebrija ARIES Res Ctr E-28040 Madrid Spain

Recent development toward innovative applications and technologies like self-driving, augmented reality, smart cities, and various other applications leads to excessive growth in the number of devices. These devices have finite computation resources and cannot handle the applications that require extensive computation with minimal delay. To overcome this, the mobile edge cloud (MEC) emerges as a practical solution that allows devices to offload their extensive computation to MEC located in their vicinity;this will lead to succeeding the arduous delay of the millisecond scale: requirement of 5th generation communication system. This work examines the convex optimization problem. The objective is to minimize the task duration by optimal allocation of the resources like local and edge computational capabilities, transmission power, and optimal task segmentation. For optimal allocation of resources, an algorithm name Estimation of Optimal Resource Allocator (EORA) is designed to optimize the function by keeping track of statistics of each candidate of the population. Using EORA, a comparative analysis of the hybrid approach (partial offloading) and edge computation only is performed. Results reveal the fundamental trade-off between both of these models. Simultaneously, the impact of devices' computational capability, data volume, and computational cycles requirement on task segmentation is analyzed. Simulation results demonstrate that the hybrid approach: partial offloading scheme reduces the task's computation time and outperforms edge computing only.

关键词： mobile edge cloud computing partial offloading scheme resource allocation

来源：评论

学校读者我要写书评

暂无评论

edgeDrive: Supporting Advanced Driver Assistance Systems using mobile edge clouds Networks

EdgeDrive: Supporting Advanced Driver Assistance Systems usi...

引用

IEEE Conference on Computer Communications (IEEE INFOCOM)

作者： Maheshwari, Sumit Zhang, Wuyang Seskar, Ivan Zhang, Yanyong Raychaudhuri, Dipankar Rutgers State Univ WINLAB North Brunswick NJ 08901 USA Univ Sci & Technol China Hefei Peoples R China

ISBN: (纸本)9781728118789

In this paper, we present edgeDrive, a networked edge cloud services framework which can support low-latency applications during mobility taking into account needs of the driver, nature of the required service and key network features. We implement head-mounted device (HMD) based Augmented Reality (AR) ADAS applications such as navigation, weather notification and annotation based assistance to drive the evaluation. These services are then coupled with the mobile edge clouds (MECs) wherein the container based service migration is enabled based upon migration cost and required Quality of Experience (QoE) to support mobility. An emulator based evaluation is carried out on the ORBIT testbed using realistic San Francisco taxicab traces running over nine edge cloud nodes and AR HMD being used by drivers. The experiments show that the edgeDrive can support low-latency ADAS applications with an average system latency less than 100 ms for the applications under consideration.

关键词： ADAS self-driving mobile edge cloud computing Service Migration Augmented Reality cloud computing

来源：评论

学校读者我要写书评

暂无评论

Distributed computing in a cloud of mobile Phones

Distributed Computing in a Cloud of Mobile Phones

引用

作者： Sanches, Pedro Miguel Castanheira Universidade Nova de Lisboa

学位级别：硕士

For the past few years we have seen an exponential growth in the number of mobiledevices and in their computation, storage and communication capabilities. We also have seen an increase in the amount of data generated by mobile devices while performing common tasks. Additionally, the ubiquity associated with these mobile devices, makes it more reasonable to start thinking in a different use for them, where they will begin to act as an important part in the computation of more demanding applications, rather than relying exclusively on external servers to do *** is also possible to observe an increase in the number of resource demanding applications, whereas these resort to the use of services, offered by infrastructure ***, with the use of these cloud services, many problems arise, such as: the considerable use of energy and bandwidth, high latency values, unavailability of connectivity infrastructures, due to the congestion or the non existence of it. Considering all the above, for some applications it starts to make sense to do part or all the computation locally in the mobile devices *** propose a distributed computing framework, able to process a batch or a streamof data, which is being generated by a cloud composed of mobile devices, that doesnot require Internet services. Differently from the current state-of-the-art, where bothcomputation and data are offloaded to mobile devices, our system intends to move thecomputation to where the data is, reducing significantly the amount of data exchangedbetween mobile *** on the evaluation performed, both on a real and simulated environment, ourframework has proved to support scalability, by benefiting significantly from the usageof several devices to handle computation, and by supporting multiple devices submitting computation requests while not having a significant increase in the latency of a request. It also proved that is able to deal with churn without being highly penalized by it.

关键词： mobile edge cloud computing Stream Processing Distributed computing Android

来源：评论

学校读者我要写书评

暂无评论

A decision-making mechanism for task offloading using learning automata and deep learning in mobile edge networks

引用

HELIYON 2024年第1期10卷 e23651页

作者： Tan, Xin Zhao, DongYan Wang, MingWei Wang, Xin Wang, XiangHui Liu, WenYuan Ghobaei-Arani, Mostafa Shaanxi Univ Sci & Technol Sch Elect Informat & Artificial Intelligence Xian 710021 Shaanxi Peoples R China Shaanxi Univ Sci & Technol Shaanxi Artificial Intelligence Joint Lab Xian 710021 Shaanxi Peoples R China Islamic Azad Univ Dept Comp Engn Qom Branch Qom Iran Islamic Azad Univ Prod & Recycling Mat & Energy Res Ctr Qom Branch Qom Iran

The development of mobile networks has led to the emergence of challenges such as high delays in storage, computing and traffic management. To deal with these challenges, fifth-generation networks emphasize the use of technologies such as mobile cloud computing and mobile edge computing. mobile edge cloud computing (MECC) is an emerging distributed computing model that provides access to cloud computing services at the edge of the network and near mobile users. With offloading tasks at the edge of the network instead of transferring them to a remote cloud, MECC can realize flexibility and real-time processing. During computation offloading, the requirements of Internet of Things (IoT) applications may change at different stages, which is ignored in existing works. With this motivation, we propose a task offloading method under dynamic resource requirements during the use of IoT applications, which focuses on the problem of workload fluctuations. The proposed method uses a learning automata-based offload decisionmaker to offload requests to the edge layer. An auto-scaling strategy is then developed using a long short-term memory network which can estimate the expected number of future requests. Finally, an Asynchronous Advantage Actor-Critic algorithm as a deep reinforcement learningbased approach decides to scale down or scale up. The effectiveness of the proposed method has been confirmed through extensive experiments using the iFogSim simulator. The numerical results show that the proposed method has better scalability and performance in terms of delay and energy consumption than the existing state-of-the-art methods.

关键词： mobile edge cloud computing Computation offloading Learning automata Deep reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：