Adaptive Request Scheduling and Service Caching for MEC-Assisted IoT Networks: An Online Learning Approach

Ren, Dewang; Gui, Xiaolin; Zhang, Kaiyuan

doi:10.1109/jiot.2022.3157677

Cited by 43 publications

(7 citation statements)

References 39 publications

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“…When serving requests in a device with a buffer memory of infinite capacity, arbitrary service disciplines can, in principle, give the same results [4,14]. However, with a limited lifetime of packets T live , a necessary condition for their preservation should be compliance with the inequality T serv ≤ T live , where T serv is the service time (or the average time in the case of a service model with a random length of time) [6].…”

Section: Researches Methodologymentioning

confidence: 99%

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Development of a Modified Method of Network Traffic Forming

Kozlovskyi

Yakymchuk

Selepyna

et al. 2023

IAPGOS

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Based on the analysis of the statistical characteristics of heterogeneous network traffic of "Quadruple Play" mobile subscribers, it is shown that it cannot be represented by Poisson or Erlang distributions. It is shown that for such similar traffic, the rate of growth of the required buffer volume increases as the Hurst parameter increases. A method of adaptive traffic formation using the control of the length of intervals of the intensity of the arrival of data packets has been developed. Congestion management is carried out by changing the frequency of the marker generator (GM) on the basis of the results of forecasting the required bandwidth of the system and the required buffer size. The shaper adapts to changes in the length and instantaneous intensity of packet input in real-time.

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Section: Researches Methodologymentioning

confidence: 99%

“…With lower user activity, which results in a lower average speed, behavior is observed that is typically modeled by Poisson processes. Aggregate WWW traffic at low network load (without losses in buffers) is also well modeled by Poisson processes [14].…”

Section: Literature Reviewmentioning

confidence: 99%

Development of a Modified Method of Network Traffic Forming

Kozlovskyi

Yakymchuk

Selepyna

et al. 2023

IAPGOS

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“…In [28], the offloading and caching decisions, uplink power and edge computing resources were jointly optimized to minimize the sum of weighted local processing time and energy consumption in two-tier cache-assisted MEC networks, and a distributed collaborative iterative algorithm was proposed. In [29], a problem of adaptive request scheduling and cooperative service caching was studied in cache-assisted MEC networks. After formulating the optimization problems as partially observable Markov decision process (MDP) problems, an online DRL algorithm was proposed to improve the service hitting ratio and latency reduction rate.…”

Section: Related Workmentioning

confidence: 99%

Computing Offloading Based on TD3 Algorithm in Cache-Assisted Vehicular NOMA–MEC Networks

Zhou,

Xu,

Qin

et al. 2023

Sensors

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In this paper, in order to reduce the energy consumption and time of data transmission, the non-orthogonal multiple access (NOMA) and mobile edge caching technologies are jointly considered in mobile edge computing (MEC) networks. As for the cache-assisted vehicular NOMA–MEC networks, a problem of minimizing the energy consumed by vehicles (mobile devices, MDs) is formulated under time and resource constraints, which jointly optimize the computing resource allocation, subchannel selection, device association, offloading and caching decisions. To solve the formulated problem, we develop an effective joint computation offloading and task-caching algorithm based on the twin-delayed deep deterministic policy gradient (TD3) algorithm. Such a TD3-based offloading (TD3O) algorithm includes a designed action transformation (AT) algorithm used for transforming continuous action space into a discrete one. In addition, to solve the formulated problem in a non-iterative manner, an effective heuristic algorithm (HA) is also designed. As for the designed algorithms, we provide some detailed analyses of computation complexity and convergence, and give some meaningful insights through simulation. Simulation results show that the TD3O algorithm could achieve lower local energy consumption than several benchmark algorithms, and HA could achieve lower consumption than the completely offloading algorithm and local execution algorithm.

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“…2) Online Learning: Online learning is a method of ML where data becomes available in a sequential order and is used to update the best predictor for future data at each step, as opposed to batch learning techniques which generate the best predictor by learning on the entire training data set at once [174]. To cope with the time-varying problems with unknown statistics on-the-go, the online learning algorithm has been applied to the task offloading decisions, server selection, and service caching in MEC [175,176]. Besides, in [177], stochastic online learning, and its promising applications to MEC are discussed.…”

Section: Other Learning Techniquesmentioning

confidence: 99%