Elastic resource utilization framework for high capacity and energy efficiency in cloud RAN

2016 ◽  
Vol 54 (1) ◽  
pp. 26-32 ◽  
Author(s):  
Dario Pompili ◽  
Abolfazl Hajisami ◽  
Tuyen X. Tran
Keyword(s):  
Energy Efficiency ◽  
Resource Utilization ◽  
High Capacity ◽  
Cloud Ran

Elastic Resource Provisioning for Increased Energy Efficiency and Resource Utilization in Cloud-RANs

2020 ◽  
Vol 172 ◽  
pp. 107170
Author(s):  
Abolfazl Hajisami ◽  
Tuyen X. Tran ◽  
Ayman Younis ◽  
Dario Pompili
Keyword(s):  
Energy Efficiency ◽  
Resource Utilization ◽  
Resource Provisioning

scholarly journals Novel Resource Allocation Techniques for Downlink Non-Orthogonal Multiple Access Systems

2020 ◽  
Vol 10 (17) ◽  
pp. 5892 ◽  
Author(s):  
Zuhura J. Ali ◽  
Nor K. Noordin ◽  
Aduwati Sali ◽  
Fazirulhisyam Hashim ◽  
Mohammed Balfaqih
Keyword(s):  
Resource Allocation ◽  
Energy Efficiency ◽  
Power Allocation ◽  
Multiple Access ◽  
High Capacity ◽  
Kkt Conditions ◽  
Optimal Power Allocation ◽  
User Pairing ◽  
Optimal Power ◽  
Sum Rate

Non-orthogonal multiple access (NOMA) plays an important role in achieving high capacity for fifth-generation (5G) networks. Efficient resource allocation is vital for NOMA system performance to maximize the sum rate and energy efficiency. In this context, this paper proposes optimal solutions for user pairing and power allocation to maximize the system sum rate and energy efficiency performance. We identify the power allocation problem as a nonconvex constrained problem for energy efficiency maximization. The closed-form solutions are derived using Karush–Kuhn–Tucker (KKT) conditions for maximizing the system sum rate and the Dinkelbach (DKL) algorithm for maximizing system energy efficiency. Moreover, the Hungarian (HNG) algorithm is utilized for pairing two users with different channel condition circumstances. The results show that with 20 users, the sum rate of the proposed NOMA with optimal power allocation using KKT conditions and HNG (NOMA-PKKT-HNG) is 6.7% higher than that of NOMA with difference of convex programming (NOMA-DC). The energy efficiency with optimal power allocation using DKL and HNG (NOMA-PDKL-HNG) is 66% higher than when using NOMA-DC.

Analysis of Area Spectral & Energy Efficiency in a CoMP-Enabled User-Centric Cloud RAN

2021 ◽  
pp. 1-1
Author(s):  
Shahrukh Khan Kasi ◽  
Umair Sajid Hashmi ◽  
Muhammad Nabeel ◽  
Sabit Ekin ◽  
Ali Imran
Keyword(s):  
Energy Efficiency ◽  
User Centric ◽  
Cloud Ran

scholarly journals Energy efficiency of cooperative D2D communications underlaying LTE-A networks

2018 ◽  
Vol 189 ◽  
pp. 03016 ◽  
Author(s):  
Xiaoying Zhang ◽  
Ahmed Khwaja ◽  
Muhammad Naeem ◽  
Alagan Anpalagan
Keyword(s):  
Energy Efficiency ◽  
Power Allocation ◽  
Resource Utilization ◽  
Energy Efficient ◽  
Average Energy ◽  
D2d Communication ◽  
Cellular Communications ◽  
D2d Communications ◽  
Allocation Algorithm ◽  
Power Resources

Device-to-device (D2D) communications underlaying LTE-A networks is expected to bring significant benefits for resource utilization and energy efficiency (EE) improvement of user equipment (UE). However, the allocation of radio and power resources to D2D communications needs elaborate coordination, because of the interference between D2D communications and cellular communications. In this paper, we propose an energy-efficient cooperative D2D communication (EECD2D) technique using a power allocation algorithm, aiming at maximizing EE introduced by D2D communications in LET-A networks. Specifically, we define four D2D and cellular combinations based on distances, and analyze average EE of EECD2D and that of cooperative D2D communications without optimization. Results show that average EE of our algorithm is much higher than that without optimization, and closer D2D cooperators and distant cellular UEs whose uplink resource is reused, achieve highest average energy efficiency.

scholarly journals Cooperatively Improving Data Center Energy Efficiency Based on Multi-Agent Deep Reinforcement Learning

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2071
Author(s):  
Ce Chi ◽  
Kaixuan Ji ◽  
Penglei Song ◽  
Avinab Marahatta ◽  
Shikui Zhang ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
Reinforcement Learning ◽  
Resource Utilization ◽  
Data Center ◽  
Data Centers ◽  
Optimization Method ◽  
Comparison Method ◽  
Cooling Systems ◽  
Model Free ◽  
Multi Agent

The problem of high power consumption in data centers is becoming more and more prominent. In order to improve the energy efficiency of data centers, cooperatively optimizing the energy of IT systems and cooling systems has become an effective way. In this paper, a model-free deep reinforcement learning (DRL)-based joint optimization method MAD3C is developed to overcome the high-dimensional state and action space problems of the data center energy optimization. A hybrid AC-DDPG cooperative multi-agent framework is devised for the improvement of the cooperation between the IT and cooling systems for further energy efficiency improvement. In the framework, a scheduling baseline comparison method is presented to enhance the stability of the framework. Meanwhile, an adaptive score is designed for the architecture in consideration of multi-dimensional resources and resource utilization improvement. Experiments show that our proposed approach can effectively reduce energy for data centers through the cooperative optimization while guaranteeing training stability and improving resource utilization.

scholarly journals Tetramethylpyrazine: an electrolyte additive for high capacity and energy efficiency lithium–oxygen batteries

RSC Advances ◽  
2021 ◽  
Vol 11 (39) ◽  
pp. 24320-24325
Author(s):  
Mengyuan Song ◽  
Chunguang Chen ◽  
Tao Huang ◽  
Aishui Yu
Keyword(s):  
Energy Efficiency ◽  
Discharge Capacity ◽  
Strong Interaction ◽  
High Capacity ◽  
Electrolyte Additive ◽  
Lithium Oxygen Batteries ◽  
Solution Mechanism

TMP has a strong interaction with Li+, which promotes the solution mechanism of Li2O2, thereby increasing the discharge capacity.

scholarly journals Performance Evaluation of Publish-Subscribe Systems in IoT Using Energy-Efficient and Context-Aware Secure Messages

2021 ◽  
Author(s):  
Norisvaldo Ferraz Junior ◽  
Anderson AA Silva ◽  
Adilson E Guelfi ◽  
Sergio T Kofuji
Keyword(s):  
Energy Efficiency ◽  
Performance Evaluation ◽  
Energy Efficient ◽  
High Capacity ◽  
Context Aware ◽  
Cloud Platform ◽  
Ultra Low Power ◽  
Message Structure ◽  
End To End ◽  
Iot Devices

Abstract Background: The Internet of Things (IoT) enables the development of innovative applications in various domains such as healthcare, transportation, and Industry 4.0. Publish-subscribe systems enable IoT devices to communicate with the cloud platform. However, IoT applications need context-aware messages to translate the data into contextual information, allowing the applications to act cognitively. Besides, end-to-end security of publish-subscribe messages on both ends (devices and cloud) is essential. However, achieving security on constrained IoT devices with memory, payload, and energy restrictions is a challenge. Contribution: Messages in IoT need to achieve both energy efficiency and secure delivery. Thus, the main contribution of this paper refers to a performance evaluation of a message structure that standardizes the publish-subscribe topic and payload used by the cloud platform and the IoT devices. We also propose a standardization for the topic and payload for publish-subscribe systems. Conclusion: The messages promote energy efficiency, enabling ultra-low-power and high-capacity devices and reducing the bytes transmitted in the IoT domain. The performance evaluation demonstrates that publish-subscribe systems (namely, AMQP, DDS, and MQTT) can use our proposed energy-efficient message structure on IoT. Additionally, the message system provides end-to-end confidentiality, integrity, and authenticity between IoT devices and the cloud platform.

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