Markov Decision Policies for Dynamic Video Delivery in Wireless Caching Networks

Choi, Minseok; No, Albert; Ji, Mingyue; Kim, Joongheon

doi:10.1109/twc.2019.2938755

Cited by 56 publications

(22 citation statements)

References 39 publications

(62 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Angelo furfaro et. al., [32] has simulated the bandwidth flooding attacks handled by the hybrid filter mechanism developed involving differentiated service policies integrated with stopIt technique exploiting network filters on routers. An alerts has been sent by the router to the server when the traffic flow exceeds bandwidth and looks for malicious sources by log monitoring but it is not realistic to large scale topology having more response time thus decrease in performance occurred.…”

Section: Related Workmentioning

confidence: 99%

A Comprehensive Analysis On Efficient Multimedia Storage Mechanism In Public Cloud Environment With Secured Access

Megala¹

2021

TURCOMAT

View full text Add to dashboard Cite

Dynamic on-demand service has become the crucial part in cloud computing according to the user’s requirements. Cloud storage is clearly the top choice for archiving media assets, by choosing the public cloud for active, deep archive needs. Video on demand, media distribution system in cloud allowing users to access video, facilitating immediate download and view of data without traditional video playback devices. On demand service of video requires infrastructure resources to handle large load fluctuations. Major challenges of video demand services are delays in simultaneous video uploading, processing, accessing and traffic caused by exhaust services. Also, there are concerns over the security of assets stored in a public cloud remains. In this paper, an ample analysis is presented on file storage encompassing fragmentation, compression, encryption security concerns in cloud storage and access. The limitations in the existing is also provided and hence outlined the research direction towards an efficient storage mechanism with a secured access providing a reliable media data transmission between data owner and users in public cloud environment.

show abstract

Section: Related Workmentioning

confidence: 99%

A Comprehensive Analysis On Efficient Multimedia Storage Mechanism In Public Cloud Environment With Secured Access

Megala¹

2021

TURCOMAT

View full text Add to dashboard Cite

show abstract

“…The Markov decision process (MDP) can be formed as (S, A, p(s), p(s |s, a), r(s, a, s ), γ) where S and A stand for the sets of states and actions, respectively [40], [41], [42]. Here, γ represents the reflection rate of future rewards compared to the current decision.…”

Section: Background a Markov Decision Processmentioning

confidence: 99%

Parallelized and Randomized Adversarial Imitation Learning for Safety-Critical Self-Driving Vehicles

Yun¹,

Shin²,

Jung³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Self-driving cars and autonomous driving research has been receiving considerable attention as major promising prospects in modern artificial intelligence applications. According to the evolution of advanced driver assistance system (ADAS), the design of self-driving vehicle and autonomous driving systems becomes complicated and safety-critical. In general, the intelligent system simultaneously and efficiently activates ADAS functions. Therefore, it is essential to consider reliable ADAS function coordination to control the driving system, safely. In order to deal with this issue, this paper proposes a randomized adversarial imitation learning (RAIL) algorithm. The RAIL is a novel derivative-free imitation learning method for autonomous driving with various ADAS functions coordination; and thus it imitates the operation of decision maker that controls autonomous driving with various ADAS functions. The proposed method is able to train the decision maker that deals with the LIDAR data and controls the autonomous driving in multi-lane complex highway environments. The simulation-based evaluation verifies that the proposed method achieves desired performance.

show abstract

“…The application of deep neural network overcomes the size of memory that has been the limitation of Q-table in Q-learning; and an efficient function output is possible for large amounts of data. The expressions (5) show that it minimizes a difference between the optimal value and real value (i.e., predicted value). The optimal value is a target value that the agent aims to get.…”

Section: Deep Reinforcement Learningmentioning

confidence: 99%

“…Among them, this paper considers reinforcement learning based approaches because this given problem is for stochastic sequential offloading decision-making. Among a lot of deep reinforcement learning (DRL) methodologies such as Q-learning, Markov decision process (MDP) [5], deep Q-network (DQN), and deep deterministic policy gradient (DDPG) [6,7], this paper designs a sequential offloading decision-making algorithm based on DQN. The reason why this paper considers DQN is that it is the function approximation of Q-learning using deep neural network (DNN) in order to take care of large-scale problem setting.…”

mentioning

confidence: 99%

Adaptive Real-Time Offloading Decision-Making for Mobile Edges: Deep Reinforcement Learning Framework and Simulation Results

et al. 2020

Self Cite

View full text Add to dashboard Cite

This paper proposes a novel dynamic offloading decision method which is inspired by deep reinforcement learning (DRL). In order to realize real-time communications in mobile edge computing systems, an efficient task offloading algorithm is required. When the decision of actions (offloading enabled, i.e., computing in clouds or offloading disabled, i.e., computing in local edges) is made by the proposed DRL-based dynamic algorithm in each unit time, it is required to consider real-time/seamless data transmission and energy-efficiency in mobile edge devices. Therefore, our proposed dynamic offloading decision algorithm is designed for the joint optimization of delay and energy-efficient communications based on DRL framework. According to the performance evaluation via data-intensive simulations, this paper verifies that the proposed dynamic algorithm achieves desired performance. IntroductionAccording to the fact that the 5G era has been realized based on networking technology innovation, many new computing and communications paradigms are introduced such as millimeter-wave communications, hyper-dense networks, and device-to-device proximal networking [1-3]. Among them, mobile edge computing (MEC) is one of the major technologies for realizing data/computing distribution in order to improve cellular network performance [4]. Based on the benefits of MEC technologies such as data rate improvement and quality enhancement, mobile cellular users can enjoy high quality, seamless, and real-time communication networking services.Together with the MEC technologies, many networked components are also of interest such as cloud servers and connected devices/vehicles. As the number of connected devices/vehicles increases, the amount of data transmitted to the MEC is also rapidly increasing. This obviously introduces serious network limitations such as data processing performance limits, storage capacity limits, and the battery use of terminal devices. Under the consideration of these limitations and problems, the use of cloud computing server is more efficient to deal with big data (gathered from connected vehicles/devices via MEC edges) than the local computing on terminals. On the other hand, for any cases where the data cannot be handled in the cloud servers due to delay requirements and security reasons, MEC devices should be able to handle or process the data from the connected vehicles/devices. Therefore, we can observe the trade-off between cloud computing servers and MEC servers (local edge computing servers). In summary, cloud servers have more power and centralized computing benefits comparing to MEC servers, whereas, it may have limitations when we have delay requirements and security requirements/regulations. Figure 1 shows the combined architecture of local edge computing and cloud computing. As the transmission from connected vehicles/devices (i.e., data upload and download) delay goes down, it is much more suitable for real-time processing. It enables real-time data processing and transmission using high-quality ...

show abstract

Markov Decision Policies for Dynamic Video Delivery in Wireless Caching Networks

Cited by 56 publications

References 39 publications

A Comprehensive Analysis On Efficient Multimedia Storage Mechanism In Public Cloud Environment With Secured Access

A Comprehensive Analysis On Efficient Multimedia Storage Mechanism In Public Cloud Environment With Secured Access

Parallelized and Randomized Adversarial Imitation Learning for Safety-Critical Self-Driving Vehicles

Adaptive Real-Time Offloading Decision-Making for Mobile Edges: Deep Reinforcement Learning Framework and Simulation Results

Contact Info

Product

Resources

About