Collaborate Edge and Cloud Computing With Distributed Deep Learning for Smart City Internet of Things

Wu, Huaming; Zhang, Ziru; Guan, Chunlin; Wolter, Katinka; Xu, Minxian

doi:10.1109/jiot.2020.2996784

Cited by 189 publications

(78 citation statements)

References 43 publications

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“…Wu et al [85] propose a distributed deep learning method for optimizing the weighted sum of total execution time and total energy consumption of all tasks in a collaborate edge and cloud computing environment. They employ parallel deep neural networks (DNN) with the input of task workloads and the output of offloading decisions, and update labelled data with new generated data to update DNN parameters.…”

Section: D: Multi-objective Optimizationmentioning

confidence: 99%

A Survey and Taxonomy on Task Offloading for Edge-Cloud Computing

Wang

Huang

et al. 2020

IEEE Access

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Edge-cloud computing, combining the benefits of both edge computing and cloud computing, is one of the most promising ways to address the resource insufficiency of smart devices. Task offloading is an important challenge must be addressed for edge-cloud computing in practice, which decides the place and the time for performing each task. Even though there is existing research focusing on the task offloading in edge-cloud computing, a lot of problems should be solved before the application of these offloading technologies. Thus, in this article, we first propose a taxonomy of task offloading in edge-cloud environments to investigate and classify related research articles, and then summarize several challenges which have not been addressed for future research directions on this area to promote the development of edge-cloud market.

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Section: D: Multi-objective Optimizationmentioning

confidence: 99%

A Survey and Taxonomy on Task Offloading for Edge-Cloud Computing

Wang

Huang

et al. 2020

IEEE Access

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“…The work in [52] also highlights which computation and communication challenges related to MEC can be solved by different ML solutions. Collaboration between edge and centralized cloud resources using ML is proposed and evaluated in [305]. A distributed DL-based task offloading algorithm is proposed that generates offloading decisions among the end-user device, the edge cloud, and the central cloud servers.…”

Section: B Ml-based Optimization Of the Edge Infrastructurementioning

confidence: 99%

Machine Learning Meets Communication Networks: Current Trends and Future Challenges

et al. 2020

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The growing network density and unprecedented increase in network traffic, caused by the massively expanding number of connected devices and online services, require intelligent network operations. Machine Learning (ML) has been applied in this regard in different types of networks and networking technologies to meet the requirements of future communicating devices and services. In this article, we provide a detailed account of current research on the application of ML in communication networks and shed light on future research challenges. Research on the application of ML in communication networks is described in: i) the three layers, i.e., physical, access, and network layers; and ii) novel computing and networking concepts such as Multi-access Edge Computing (MEC), Software Defined Networking (SDN), Network Functions Virtualization (NFV), and a brief overview of ML-based network security. Important future research challenges are identified and presented to help stir further research in key areas in this direction. INDEX TERMS Communication networks, machine learning, physical layer, MAC layer, network layer, SDN, NFV, MEC, security, artificial intelligence (AI) I. INTRODUCTION T HE security, availability and performance demands of new applications, services and devices are increasing at a pace higher than anticipated. Real-time responsiveness in application areas like e-health, traffic, and industry requires communication networks to make real-time decisions autonomously. Such real-time autonomous decision-making requires that the network must react and learn from the environment, and control itself without human interventions. However, communication networks have until now taken a different path. Traditional networks rely on human involvement to respond manually to changes such as traffic variation, updates in network functions and services, security breaches, and faults. Human-machine interactions have resulted in network downtime [1], have opened the network to security vulnerabilities [2], and lead to many other challenges in current communication networks [3], [4]. The requirement for human interaction or manual configuration constitutes a major hindrance for a network to use its past experiences to adapt to changing requirements. The general idea is to predict the (future) behavior of a service, network segment, user or User Equipment (UE), and tune the network at run-time based on this information. For instance, the movement trajectory of a user can be predicted using

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“…Hu et al [21] proposed an approximately optimal service allocation strategy that meets the constraints of edge server resources and bandwidth to find the tradeoff between average network delay and load balancing. Wu et al [22] formalized the mixed task assignment problem of mobile edge computing as a multi-objective optimization problem, and then proposed an efficient offloading framework with intelligent decision-making ability to jointly minimize system utility and bandwidth allocation of each mobile device. Pallewatta et al [23] proposed an IoT application layout strategy that utilizes micro-service independent deployability and scalability to minimize latency and network utilization.…”

Section: Related Workmentioning

confidence: 99%

“…Guo et al [29] studied the optimal allocation scheme of energy saving resources for multi-user mobile edge computing systems with inelastic computing tasks and non-negligible task execution time, and proposed a low complexity algorithm to solve the suboptimal solution by combining the optimization problem with the three-stage pipeline scheduling problem and using Johnson algorithm and convex optimization technology. The above studies [7,[19][20][21][22][23][24][25][26][27][28][29] all solve the corresponding joint optimization problem through the corresponding calculation offloading algorithm. However, their disadvantage lies in that the designed cost does not take into account the bandwidth resource consumption between tasks deployed on different edge servers and the energy consumption of edge clouds.…”

Section: Related Workmentioning

confidence: 99%

An Application Deployment Approach for City IoT Applications in Resource-constrained Edge Computing Environments

Liu¹,

Huang²

2020

Preprint

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With the development and utilization of more and more city Internet of Things (IoT) applications with high resource requirements, how to reduce the consumption of energy, processor resources and bandwidth resources in resource-constrained edge clouds while ensuring the execution delay of these applications is an urgent problem to be solved. Therefore, an optimal energy-bandwidth tradeoff deployment approach for city IoT application is proposed for resource-constrained edge clouds. In this approach, the city IoT applications are first divided into multiple collaborative tasks and offload to edge clouds. Secondly, a joint optimization model including energy consumption, resource wastage, resource load imbalance and bandwidth resource consumption is established for the task offloading scheme. Thirdly, the city IoT application deployment problem is optimized under the constraints of resource and execution delay. Finally, a comprehensive simulation test is conducted to analyze the deployment approaches from the aspects of performance and effectiveness. The experimental results show that our deployment approach is superior to other related approaches.

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Collaborate Edge and Cloud Computing With Distributed Deep Learning for Smart City Internet of Things

Cited by 189 publications

References 43 publications

A Survey and Taxonomy on Task Offloading for Edge-Cloud Computing

A Survey and Taxonomy on Task Offloading for Edge-Cloud Computing

Machine Learning Meets Communication Networks: Current Trends and Future Challenges

An Application Deployment Approach for City IoT Applications in Resource-constrained Edge Computing Environments

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Collaborate Edge and Cloud Computing With Distributed Deep Learning for Smart City Internet of Things

Cited by 189 publications

References 43 publications

A Survey and Taxonomy on Task Offloading for Edge-Cloud Computing

A Survey and Taxonomy on Task Offloading for Edge-Cloud Computing

Machine Learning Meets Communication Networks: Current Trends and Future Challenges

An Application Deployment Approach for City IoT&nbsp;Applications in Resource-constrained Edge Computing Environments

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An Application Deployment Approach for City IoT Applications in Resource-constrained Edge Computing Environments