Endogenous Trusted DRL-Based Service Function Chain Orchestration for IoT

Guo, Shaoyong; Qi, Yuanyuan; Jin, Yi; Li, Wenjing; Qiu, Xuesong; Li, Meng

doi:10.1109/tc.2021.3051681

Cited by 33 publications

(28 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Since reinforcement learning can dynamically obtain user behavior information, incorporating the latest preference information in real time, more and more reinforcement learning is currently being used in news, e-commerce, medical and other fields. Among them, tourism is one of the entertainment items involved in people's life, and there are few research studies on the recommendation of tourist attractions [14]. e inverse reinforcement learning is applied to the recommendation of tourist attractions, using the user's past selection order of attractions and the current scene context to understand the user's preferences and establishes a preference learning model that takes into account the timing of commodity consumption, then further use the inverse reinforcement learning method for tourist attraction recommendation.…”

Section: Literature Reviewmentioning

confidence: 99%

Recommendation Model of Tourist Attractions Based on Deep Learning

Cheng

2022

Mathematical Problems in Engineering

View full text Add to dashboard Cite

In order to solve the problem of tourism information overload caused by the rapid development of tourism and the Internet era, the author proposes a tourist attraction recommendation model based on deep learning. Convolutional Neural Network (CNN) is used to extract the sentiment of text comments, the Pearson similarity formula is used to calculate similar user groups, and the mean absolute error (MAE) is used to evaluate the resulting error. Compare with traditional collaborative filtering methods. Experimental results show that: the MAE value is smaller than the MAE value of the collaborative filtering method, indicating that considering tourists’ behavioral information, contextual information, and emotional factors in comments can effectively improve the accuracy of recommendation, as the data volume of the test set increased from 250 to 2000; although there was an increase in the MAE value, the overall trend showed a downward trend, indicating that the quality of the model can be more fully verified when the data volume is large. The model proposed by the author can effectively reduce the prediction error and improve the efficiency of tourist attractions recommendation.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Recommendation Model of Tourist Attractions Based on Deep Learning

Cheng

2022

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…Above caching schemes may not always be energy efficient for resource-constrained IoT devices [25,26] like sensors and actuators where energy efficiency is one of the most concerning factors. From an energy-efficient perspective, the reason is that without considering the energy reward of caching decisions, inappropriate routers and content objects may be selected for caching, which may lead to negative energy rewards.…”

Section: Related Workmentioning

confidence: 99%

An Energy Reward-Based Caching Mechanism for Information-Centric Internet of Things

Dinh

Kim

2022

Sensors

View full text Add to dashboard Cite

Existing information-centric networking (ICN) designs for Internet of Things (IoT) mostly make caching decisions based on probability or content popularity. From the energy-efficient perspective, those strategies may not always be energy efficient in resource-constrained IoT because without considering the energy reward of caching decisions, inappropriate routers and content objects may be selected for caching, which may lead to negative energy rewards. In this paper, we analyze the energy consumption of content caching and content retrieval in resource-constrained IoT and calculate caching energy reward as a key metric to measure the energy efficiency of a caching decision. We then propose an efficient cache placement and cache replacement mechanism based on the caching energy reward to improve the energy efficiency of caching decisions. Through analysis and experimental results, we show that the proposed mechanism achieves a significant improvement in terms of energy efficiency, stretch ratio, and cache hit ratio compared to state-of-the-art caching schemes.

show abstract

“…DRL is being a part of AI, applied in the diverse areas likely in the area of edge and fog computing fields such as resource management [20,21], service orchestration [22,23], resource protection [24,25] and energy-efficient resource management [26,12]. However, the proposed work aligns in a similar direction focusing on optimizing the placement of users' serverless application on fog and cloud environments based on real-life parameters, such as priority of the serverless applications, resource constraints of each serverless platform, distance and latency of the users' from nearby fog node, users' priority, and their resource demand, etc.…”

Section: Related Workmentioning

confidence: 99%

DeF-DReL: Systematic Deployment of Serverless Functions in Fog and Cloud environments using Deep Reinforcement Learning

Dehurya¹,

Poojaraa²,

Domanalb³

et al. 2021

Preprint

View full text Add to dashboard Cite

Fog computing is introduced by shifting cloud resources towards the users' proximity to mitigate the limitations possessed by cloud computing. Fog environment made its limited resource available to a large number of users to deploy their serverless applications, composed of several serverless functions. One of the primary intentions behind introducing the fog environment is to fulfil the demand of latency and location-sensitive serverless applications through its limited resources. The recent research mainly focuses on assigning maximum resources to such applications from the fog node and not taking full advantage of the cloud environment. This introduces a negative impact in providing the resources to a maximum number of connected users. To address this issue, in this paper, we investigated the optimum percentage of a user's request that should be fulfilled by fog and cloud. As a result, we proposed DeF-DReL, a Systematic Deployment of Serverless Functions in Fog and Cloud environments using Deep Reinforcement Learning, using several real-life parameters, such as distance and latency of the users from nearby fog node, user's priority, the priority of the serverless applications and their resource demand, etc. The performance of the DeF-DReL algorithm is further compared with recent related algorithms. From the simulation and comparison results, its superiority over other algorithms and its applicability to the real-life scenario can be clearly observed.

show abstract

Endogenous Trusted DRL-Based Service Function Chain Orchestration for IoT

Cited by 33 publications

References 27 publications

Recommendation Model of Tourist Attractions Based on Deep Learning

Recommendation Model of Tourist Attractions Based on Deep Learning

An Energy Reward-Based Caching Mechanism for Information-Centric Internet of Things

DeF-DReL: Systematic Deployment of Serverless Functions in Fog and Cloud environments using Deep Reinforcement Learning

Contact Info

Product

Resources

About