Balancing QoS and power consumption in programmable 5G infrastructures

Carrega, Alessandro; Portomauro, Giancarlo; Repetto, Matteo; Robino, Giorgio

doi:10.1002/ett.3425

Cited by 3 publications

(2 citation statements)

References 38 publications

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“…The primary purpose of [24] is to use the energy consumption specifications and QoS features and the user's priority in computing at the edge of the mobile objects network. Furthermore, [25]has proposed a method based on workload composition and propagations to existing cloud management software. They have developed a novel technique to energy yield in virtuallized infrastructures, and in this way, they have increased energy yield.…”

Section: Related Workmentioning

confidence: 99%

The Reinforcement Learning Method in Deciding on Task Offloading and Energy Consumption Management at the Edge of IoT Network

Mohammadian

Zarrabi

Jabbehdari

et al. 2021

Preprint

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In this paper, using edge processing in IoT network ,a method to decide on task offloading to edge devices and improve management of consuming energy in network of edge devices is introduced. First, by defining the problem of maximizing utility and then by decomposing it based on the status of task offloading from end devices to smart gateway with the lowest battery consumption and the possible lowest use of the communication bandwidth, independent optimal models are obtained and then combining. Then the general problem of maximizing the usefulness and increasing the lifetime of the end devices with restrictions of processing time and energy resources will be obtained. Due to the unknown environment of the problem and how the end devices and the edge of the network, an iterative reinforcement learning algorithm is used to generate the optimal answer in order to maximize the utility gain.The results show the existence of processing overhead and network load with increasing number of devices. The proposed method, while improving energy consumption in existence of a small number of devices in end of edge, reduces latency and increases processing speed and maximizes system performance compared to the central cloud system. The operating efficiency of the whole system is improved by 36% and the energy consumption at the edge of the network is optimized by 12.5%. It should be noted that, with the addition of a large number of end devices, our method outperforms similar works.

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Section: Related Workmentioning

confidence: 99%

The Reinforcement Learning Method in Deciding on Task Offloading and Energy Consumption Management at the Edge of IoT Network

Mohammadian

Zarrabi

Jabbehdari

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…The centralized‐RAN, shown in Figure 4, optimize the total power consumption using base band centralization technique 31 . In C‐BS, the antenna array along with the RRH unit has a dynamic adaptation to the traffic load.…”

Section: Mathematical Model and Analysis Of Different Ran Architecturmentioning

confidence: 99%

Exploring power consumption reduction in centralized radio access for energy‐efficient centralized‐Internet of Things implementation

Mahapatra

Turuk

Patra

2020

Trans Emerging Tel Tech

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The advent of new wireless and Internet of Things (IoT) technology has increased the dependency on the cellular base stations (BS). The rapid growth in IoT and wireless devices impinges many challenges on cellular services providers and manufacturers. The Centralized Radio Access Network is an energy and cost-efficient solution for the next-generation BS architecture. In this article, we have proposed a centralized-IoT (C-IoT) architecture based on centralized BS (C-BS). We evaluated different base station technologies and compared the C-BS with the traditional distributed BS (D-BS) architecture for power consumption. A mathematical model for power consumption for both types of BS is proposed. We have analyzed the impact of variation in traffic load on power consumption for both types of BS architecture. The simulation result shows that C-BS architecture has the potential to save around 40% to 50% energy compared with D-BS architecture.

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The effect of task processing management on energy consumption at the edge of Internet of things network with using reinforcement learning method

Mohammadian,

Zarrabi,

Jabbehdari

et al. 2024

Computers & Industrial Engineering

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Balancing QoS and power consumption in programmable 5G infrastructures

Cited by 3 publications

References 38 publications

The Reinforcement Learning Method in Deciding on Task Offloading and Energy Consumption Management at the Edge of IoT Network

The Reinforcement Learning Method in Deciding on Task Offloading and Energy Consumption Management at the Edge of IoT Network

Exploring power consumption reduction in centralized radio access for energy‐efficient centralized‐Internet of Things implementation

The effect of task processing management on energy consumption at the edge of Internet of things network with using reinforcement learning method

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