EdgeABC: An architecture for task offloading and resource allocation in the Internet of Things

2020 ◽  
Vol 107 ◽  
pp. 498-508
Author(s):  
Kaile Xiao ◽  
Zhipeng Gao ◽  
Weisong Shi ◽  
Xuesong Qiu ◽  
Yang Yang ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Internet Of Things ◽  
The Internet ◽  
The Internet Of Things ◽  
Task Offloading

scholarly journals Large Scale Resource Allocation for the Internet of Things Network Based on ADMM

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 57192-57203 ◽  
Author(s):  
Yanhua He ◽  
Sunxuan Zhang ◽  
Liangrui Tang ◽  
Yun Ren
Keyword(s):  
Resource Allocation ◽  
Internet Of Things ◽  
Large Scale ◽  
The Internet ◽  
The Internet Of Things

scholarly journals A New Task Offloading Algorithm in Edge Computing

2021 ◽  
Author(s):  
Zhenjiang Zhang ◽  
Chen Li ◽  
ShengLung Peng ◽  
Xintong Pei
Keyword(s):  
Internet Of Things ◽  
User Experience ◽  
Mobile Internet ◽  
Edge Computing ◽  
The Internet ◽  
Disruptive Technology ◽  
Computing Power ◽  
New Applications ◽  
The Internet Of Things ◽  
Task Offloading

Abstract In the last few years, the Internet of Things (IOT), as a new disruptive technology, has gradually changed the world. With the prosperous development of the mobile Internet and the rapid growth of the Internet of Things, various new applications continue to emerge, such as mobile payment, face recognition, wearable devices, driverless, VR/AR, etc. Although the computing power of mobile terminals is getting higher and the traditional cloud computing model has higher computing power, it is often accompanied by higher latency and cannot meet the needs of users. In order to reduce user delay to improve user experience, and at the same time reduce network load to a certain extent, edge computing, as an application of IOT, came into being. In view of the new architecture after dating edge computing, this paper focuses on the task offloading in edge computing, from task migration in multi-user scenarios and edge server resource management expansion, and proposes a multi-agent load balancing distribution based on deep reinforcement learning DTOMALB, a distributed task allocation algorithm, can perform a reasonable offload method for this scenario to improve user experience and balance resource utilization. Simulations show that the algorithm has a certain adaptability compared to the traditional algorithm in the scenario of multi-user single cell, and reduces the complexity of the algorithm compared to the centralized algorithm, and reduces the average response delay of the overall user. And balance the load of each edge computing server, improve the robustness and scalability of the system.

scholarly journals Social Governance Structure Construction and Resource Allocation Methods under the Management Mode of the Internet of Things

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Zonggang Xu
Keyword(s):  
Resource Allocation ◽  
Internet Of Things ◽  
Virtual Machine ◽  
Governance Structure ◽  
Management Mode ◽  
The Internet ◽  
Resource Configuration ◽  
Social Governance ◽  
Global Query ◽  
The Internet Of Things

Aiming at the problems in the social governance structure level under the management mode of the Internet of Things, this paper designs a network agent based on the idea of service grid construction that has the functions of service resource configuration discovery, load balancing, and protocol conversion. Using it as a service governance infrastructure reduces the coupling between business services and service governance, and the network agent will intercept the service call traffic within the platform to achieve cross-protocol and cross-frame service communication. Through the proposed global query decomposition strategy and algorithm, a global query mapping is realized, which solves the problem of global query decomposition based on the global data model. We designed the unified management mechanism of two commonly used IoT databases based on XML. This process involves the realization of heterogeneous Internet of Things database resource configuration, data mode conversion, and global query mapping. Among them, in the process of data schema conversion, the conflict of the data in the schema mapping is resolved, the XML Schema is generated, the schema mapping metadata is established, and the XML document of the data is generated. We build a framework for optimal allocation of cloud computing resources based on load forecasting and propose an adaptive elastic control system for resource allocation based on resource status monitoring and demand forecasting. In view of the low resource utilization problem caused by the resource management mode of single virtual machine serving single user adopted by Internet of Things service providers, this paper constructs a new public cloud architecture with a five-layer structure. On the basis of this architecture, an adaptive configuration mode of virtualized resources based on a single virtual machine serving multiple users is proposed. This mode can automatically search for optimal virtualization resources for application resource requests made by different users and run different applications on the same virtual machine without affecting the quality of service, so that IoT providers can guarantee services. At the same time, it improves the utilization efficiency of IoT resources and reduces energy consumption.

scholarly journals Joint Uplink and Downlink Resource Allocation for the Internet of Things

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 15758-15766 ◽  
Author(s):  
Zhenyu Na ◽  
Mengshu Zhang ◽  
Min Jia ◽  
Mudi Xiong ◽  
Zihe Gao
Keyword(s):  
Resource Allocation ◽  
Internet Of Things ◽  
The Internet ◽  
The Internet Of Things

Energy-Aware Task Offloading in the Internet of Things

2020 ◽  
Vol 27 (5) ◽  
pp. 112-117
Author(s):  
Jiliang Li ◽  
Minghui Dai ◽  
Zhou Su
Keyword(s):  
Internet Of Things ◽  
The Internet ◽  
Energy Aware ◽  
The Internet Of Things ◽  
Task Offloading
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