Ultra-low latency service provision in 5G Fog-Radio Access Networks

Chiu, Te-Chuan; Chung, Wei-Ho; Pang, Ai‐Chun; Yu, Ya-Ju; Yen, Pei-Hsuan

doi:10.1109/pimrc.2016.7794956

Cited by 43 publications

(24 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently, a good number of works in the literature focused on achieving low latency for IoT applications in 5G F-RAN. For instance, resource allocation based on cooperative edge computing has been studied in [17], [18], [19], [20], [21] for achieving ultra-low latency in F-RAN. The work in [17] proposed a mesh paradigm for edge computing, where the decision-making tasks are distributed among edge devices instead of utilizing the cloud server.…”

Section: B Literature Reviewmentioning

confidence: 99%

Reinforcement Learning for Adaptive Resource Allocation in Fog RAN for IoT With Heterogeneous Latency Requirements

Nassar

Yılmaz

2019

IEEE Access

View full text Add to dashboard Cite

In light of the quick proliferation of Internet of things (IoT) devices and applications, fog radio access network (Fog-RAN) has been recently proposed for fifth generation (5G) wireless communications to assure the requirements of ultra-reliable low-latency communication (URLLC) for the IoT applications which cannot accommodate large delays. Hence, fog nodes (FNs) are equipped with computing, signal processing and storage capabilities to extend the inherent operations and services of the cloud to the edge. We consider the problem of sequentially allocating the FN's limited resources to the IoT applications of heterogeneous latency requirements. For each access request from an IoT user, the FN needs to decide whether to serve it locally utilizing its own resources or to refer it to the cloud to conserve its valuable resources for future users of potentially higher utility to the system (i.e., lower latency requirement). We formulate the Fog-RAN resource allocation problem in the form of a Markov decision process (MDP), and employ several reinforcement learning (RL) methods, namely Q-learning, SARSA, Expected SARSA, and Monte Carlo, for solving the MDP problem by learning the optimum decision-making policies. We verify the performance and adaptivity of the RL methods and compare it with the performance of a fixed-thresholdbased algorithm. Extensive simulation results considering 19 IoT environments of heterogeneous latency requirements corroborate that RL methods always achieve the best possible performance regardless of the IoT environment.

show abstract

Section: B Literature Reviewmentioning

confidence: 99%

Reinforcement Learning for Adaptive Resource Allocation in Fog RAN for IoT With Heterogeneous Latency Requirements

Nassar

Yılmaz

2019

IEEE Access

View full text Add to dashboard Cite

show abstract

“…We have assumed that the set of FAPs and IoT devices are deployed randomly in the network area of 500 m x 500 m underlying a cellular BS. The bandwidth is considered as 20 MHz accordingly, the maximum number of available PRBs found as 100 [15]. We have assumed that FAP with a radius of 20 m, randomly deployed in the network.…”

Section: Performance Studymentioning

confidence: 99%

“…The BS sends message msg < k, n > to all the k ∈ Z max n for PRB n and update candidat list A n . Upon receiving message msg < k, n > the FAP k adds PRB n into its waiting list (lines [15][16].…”

mentioning

confidence: 99%

On Maximizing Task Throughput in IoT-Enabled 5G Networks Under Latency and Bandwidth Constraints

Pratap

Gupta

Nadendla

et al. 2019

2019 IEEE International Conference on Smart Computing (SMARTCOMP)

View full text Add to dashboard Cite

Fog computing in 5G networks has played a significant role in increasing the number of users in a given network. However, Internet-of-Things (IoT) has driven system designers towards designing heterogeneous networks to support diverse demands (tasks with different priority values) with different latency and data rate constraints. In this paper, our goal is to maximize the total number of tasks served by a heterogeneous network, labeled task throughput, in the presence of data rate and latency constraints and device preferences regarding computational needs. Since our original problem is intractable, we propose an efficient solution based on graph-coloring techniques. We demonstrate the effectiveness of our proposed algorithm using numerical results, real-world experiments on a laboratory testbed and comparing with the state-of-the-art algorithm.

show abstract

“…However, all tasks in this case are performed in the cloud, and the access relationship between the user and the base station is set in advance. References [26] and [27] considered the fog node cooperation mode and selected the appropriate number of fog nodes to perform user computing tasks on the premise of meeting the communication resource constraints. References [28] and [29] considered the optimization problem of minimizing the sum of the energy and delay consumed by offloading in the case of multiple users, a fog node and a cloud server.…”

Section: Introductionmentioning

confidence: 99%

Joint Allocation on Communication and Computing Resources for Fog Radio Access Networks

Wang

Xiong

et al. 2020

IEEE Access

View full text Add to dashboard Cite

To improve the user experience, an increasing number of mobile applications offload their computing tasks to servers with powerful computing capabilities. The fog radio access network (F-RAN) incorporates the concept of "fog computing" into the access network architecture, endowing an edge network with computing, storage, communication and control functions. In this paper, we consider a multiple fog access point (F-AP) and a multiuser F-RAN, where each user generates two different tasks: communication and computation. To satisfy the diverse quality of service requirements of different users, we jointly optimize the spectrum access, computation offloading and radio resource allocation. The problem is modeled as a mixed integer nonlinear programming problem, which is difficult to solve. In view of this, we propose a genetic algorithm based on convex optimization, i.e., the genetic convex optimization algorithm (GCOA), which divides the mixed integer nonlinear programming problem into two parts, i.e., optimization and convex optimization, to solve it in polynomial time. Simulation results are provided to verify the effectiveness of the algorithm.

show abstract

Ultra-low latency service provision in 5G Fog-Radio Access Networks

Cited by 43 publications

References 9 publications

Reinforcement Learning for Adaptive Resource Allocation in Fog RAN for IoT With Heterogeneous Latency Requirements

Reinforcement Learning for Adaptive Resource Allocation in Fog RAN for IoT With Heterogeneous Latency Requirements

On Maximizing Task Throughput in IoT-Enabled 5G Networks Under Latency and Bandwidth Constraints

Joint Allocation on Communication and Computing Resources for Fog Radio Access Networks

Contact Info

Product

Resources

About