Q-learning based collaborative cache allocation in mobile edge computing

Chien, Wei; Weng, Hung Yen; Lai, Chin Feng

doi:10.1016/j.future.2019.08.032

Cited by 59 publications

(23 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On the basis of the existing public opinion in China, by analyzing the influencing factors of online public opinion in emergencies, the concept of the number of people who know the information on the Internet is introduced to describe the influence of online public opinion, and a differential equation model of the evolution law of online public opinion in emergencies is established [21,22]. To the spread of the network public opinion after the process as the research object, analyses of the main factors that influence the impact of network public opinion spread link, using system dynamics simulation method simulation evolution law of network public opinion spread, through qualitative and quantitative analyses, show that to improve the government credibility, the government social service efficiency can effectively reduce the degree of spread of public opinion [23].…”

Section: Related Workmentioning

confidence: 99%

Network Public Opinion Prediction and Control Based on Edge Computing and Artificial Intelligence New Paradigm

Zhu

2021

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

In this paper, an adaptive edge service placement mechanism based on online learning and a predictive edge service migration method based on factor graph model are proposed to solve the edge computing service placement problem from the edge computing dimension. First, the time series of the development of online chaotic public opinion is a platform for vectorized collection of keyword index trends using the theory of chaotic phase space reconstruction. Secondly, it is necessary to use the main index method to judge whether the time series has the chaotic characteristics of the network public opinion data. The simulation results show that network public opinion is the development characteristic of chaotic time series. Finally, the prediction model is improved by using complex network topology. Through the simulation experiment of network public opinion and chaotic time series, the results show that the improved model has the advantages of accuracy, rapidity, and self-adaptability and can be applied to other fields.

show abstract

Section: Related Workmentioning

confidence: 99%

Network Public Opinion Prediction and Control Based on Edge Computing and Artificial Intelligence New Paradigm

Zhu

2021

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

show abstract

“…Two works addressed the cache placement problem in 5G environments using deep learning models. In [55], authors proposed a collaborative cache mechanism in multiple RRHs to multiple BBUs based on reinforcement learning. This approach was used because rule-based and metaheuristics methods suffer some limitations and fail to consider all environmental factors.…”

Section: Cache Optimizationmentioning

confidence: 99%

“…In [57], the authors adopted reinforcement learning for network slicing in RAN in an attempt to optimize resource utilization. To handle the cache allocation problem in multiple RRHs and multiple BBU pools, the authors in [55] used reinforcement learning to maximize the cache hit rate and maximize the cache capacity. In [77], reinforcement learning was used to configure indoor small cell networks in order to optimize opinion score (MOS) and user QoE.…”

Section: Supervised Learningmentioning

confidence: 99%

“…The holistic handover cost model takes into account signaling overhead, latency, call dropping, and radio resource wastage [48] LSTM supervised real data a system model that combine mobile edge computing and mobile data offloading was proposed in the paper. In order to improve the system performance, a deep learning model was proposed to predict the traffic and decide if the offloading can be performed on the base station [55] reinforcement learning simulation the authors proposed a network architecture that integrates MEC and C-RAN. In order to reduce the latency, a caching mechanism can be adopted in the MEC.…”

Section: Final Considerationsmentioning

confidence: 99%

See 1 more Smart Citation

When 5G Meets Deep Learning: A Systematic Review

et al. 2020

View full text Add to dashboard Cite

This last decade, the amount of data exchanged on the Internet increased by over a staggering factor of 100, and is expected to exceed well over the 500 exabytes by 2020. This phenomenon is mainly due to the evolution of high-speed broadband Internet and, more specifically, the popularization and wide spread use of smartphones and associated accessible data plans. Although 4G with its long-term evolution (LTE) technology is seen as a mature technology, there is continual improvement to its radio technology and architecture such as in the scope of the LTE Advanced standard, a major enhancement of LTE. However, for the long run, the next generation of telecommunication (5G) is considered and is gaining considerable momentum from both industry and researchers. In addition, with the deployment of the Internet of Things (IoT) applications, smart cities, vehicular networks, e-health systems, and Industry 4.0, a new plethora of 5G services has emerged with very diverging and technologically challenging design requirements. These include high mobile data volume per area, high number of devices connected per area, high data rates, longer battery life for low-power devices, and reduced end-to-end latency. Several technologies are being developed to meet these new requirements, and each of these technologies brings its own design issues and challenges. In this context, deep learning models could be seen as one of the main tools that can be used to process monitoring data and automate decisions. As these models are able to extract relevant features from raw data (images, texts, and other types of unstructured data), the integration between 5G and DL looks promising and one that requires exploring. As main contribution, this paper presents a systematic review about how DL is being applied to solve some 5G issues. Differently from the current literature, we examine data from the last decade and the works that address diverse 5G specific problems, such as physical medium state estimation, network traffic prediction, user device location prediction, self network management, among others. We also discuss the main research challenges when using deep learning models in 5G scenarios and identify several issues that deserve further consideration.

show abstract

“…Currently, big data analytics and machine learning techniques are widely used to evaluate content popularity [32], [97]. In cooperative strategies, caching nodes also consider the popularity of the cached contents at the neighboring nodes [2], [5], [9]. Smart traffic management, temperature monitoring, and industrial automation applications require frequent content update for seamless operations.…”

Section: B Popular Content Selectionmentioning

confidence: 99%

A Review on Green Caching Strategies for Next Generation Communication Networks

et al. 2020

View full text Add to dashboard Cite

In recent years, the ever-increasing demand for networking resources and energy, fueled by the unprecedented upsurge in Internet traffic, has been a cause for concern for many service providers. Content caching, which serves user requests locally, is deemed to be an enabling technology in addressing the challenges offered by the phenomenal growth in Internet traffic. Conventionally, content caching is considered as a viable solution to alleviate the backhaul pressure. However, recently, many studies have reported energy cost reductions contributed by content caching in cache-equipped networks. The hypothesis is that caching shortens content delivery distance and eventually achieves significant reduction in transmission energy consumption. This has motivated us to conduct this study and in this article, a comprehensive survey of the state-of-the-art green caching techniques is provided. This review paper extensively discusses contributions of the existing studies on green caching. In addition, the study explores different cache-equipped network types, solution methods, and application scenarios. We categorically present that the optimal selection of the caching nodes, smart resource management, popular content selection, and renewable energy integration can substantially improve energy efficiency of the cache-equipped systems. In addition, based on the comprehensive analysis, we also highlight some potential research ideas relevant to green content caching.

show abstract

Q-learning based collaborative cache allocation in mobile edge computing

Cited by 59 publications

References 33 publications

Network Public Opinion Prediction and Control Based on Edge Computing and Artificial Intelligence New Paradigm

Network Public Opinion Prediction and Control Based on Edge Computing and Artificial Intelligence New Paradigm

When 5G Meets Deep Learning: A Systematic Review

A Review on Green Caching Strategies for Next Generation Communication Networks

Contact Info

Product

Resources

About