Jul 2026· International Conference on Signal Processing and Communications· pp. 1-5· 0 citations· 14 references
Abstract
As network traffic grows, reducing energy costs and $\mathbf{C O}_{2}$ emissions is essential for sustainable telecom operations. Energy efficiency (EE) is therefore a key design objective in 5G Radio Access Networks (RAN), where computing resources in the Central Unit (CU) and Distributed Unit (DU) must be managed while meeting Quality of Service (QoS) requirements. This work focuses on the CU/DU computing platform, a major contributor to RAN power consumption due to continuous baseband processing. We present a measurement-based framework that dynamically adjusts CPU core count and clock frequency to minimize the energy consumption while meeting the data rate requirements of User Equipment (UE). The proposed algorithm selects the most energy-efficient CPU configuration based on experimentally derived power-performance profiles from a practical 5G RAN testbed. Experimental evaluation on two RAN platforms shows power savings of up to 16% and 35%, respectively, compared to a baseline configuration with all CPU cores active at the maximum base clock frequency. The results demonstrate the effectiveness of the proposed framework for energy-efficient operation in practical 5G RAN deployments.
It is argued that energy-efficient algorithms combined with sustainable infrastructure practices provide a viable pathway toward environmentally responsible digital transformation.
Shankar Kumar· International Journal of Cre...· 0 citations
This paper addresses the core challenge of balancing high real-time performance and low energy consumption in practical ternary optical computer (TOC) systems with hardware-induced state-switching overhead including switching delay and switching energy consumption. By introducing the partially synchronous vacation mechanism, we establish a novel TOC task scheduling strategy and corresponding three-stage queuing model. We thoroughly analyze the influences of key system parameters on queueing performance, total energy consumption, and social utility under Poisson traffic, with the no-vacation scheme as benchmark. Numerical results reveal that the proposed model can achieve optimal system social utility under various task arrival rates by tuning the number of vacation-enabled processors and vacation-related parameters. Supplementary bursty traffic experiments further verify its robustness under fluctuating real-world workloads. This work provides a new theoretical basis and practical scheduling guidance for performance-energy bi-objective optimization of TOC systems.
The significant energy consumed by data centers has become a concern both for costs and associated carbon emissions. In particular, the energy efficiency of servers is a key consideration for data center operators, and understanding servers'power consumption under different operating conditions is an important aspect of it. In this paper, we present a measurement-based case study of high-throughput computing. We analyze power usage information of an operational data center, combined with focused measurements of power reduction techniques for a representative high-throughput workload. The study points out the obstacles encountered by data center operators in their efforts to minimize energy consumption and carbon emissions, and discusses the impact of server configuration adjustments on the energy consumption of processing jobs. We offer actionable recommendations for decreasing the energy usage of servers, while considering both performance and carbon emissions.
Damu Ding, Xinpeng Hong, A. Dewhurst et al.· 0 citations
Abstract For delivering the symmetrical upstream and downstream speeds over the existing fiber, 10 Gigabit symmetrical PON (XGS-PON) is a promising solution. In this paper, a traffic-aware energy-efficient XGS-PON system employing adaptive ONU operating states is proposed and analyzed. The system operates at 10 Gbps in the downstream and apply traffic aware scheme to decrease the utility of the ONU during less traffic conditions. The baseline downstream link is designed to operate at a data rate of 10 Gbps, which is further varied as per the different traffic conditions to be analyzed. The system is investigated for the different network utilization, such as 100 %, 75 %, 50 %, 25 %, and 10 %, along with the complete sleep mode of ONU, where it is almost inactive. The performance of the proposed system architecture is evaluated in terms of BER, Q factor, received optical power, and ONU power consumption. The simulated results indicate that the system is providing acceptable BER and Q factor under full load conditions. Furthermore, this traffic-aware architecture attains ONU energy savings of 75 % under 10 % traffic conditions. The obtained results validate that the proposed system decreases the energy consumption at ONU under low traffic conditions while simultaneously maintaining reliable communication.
Rahul Sharma, Kamlajeet Kaur, Ankit Chakrawarti et al.· Journal of optical communica...· 0 citations
The issues of scalability and energy efficiency in mega data centers are growing because the east-west traffic is growing at a very high rate and the traditional electrical packet-switched (EPS) networks are constrained. Optical circuit switching (OCS) has come to be a promising complement to the packet switching because it offers high capacity data transport with improved energy-efficiency to support large and steady data traffic. The paper is a systematically assessed comparison of EPS-only data center architectures, OCS-assisted, and hybrid EPS-OCS data center architectures based on a coherent experimental system. The evaluation of performance is done in terms of throughput, flow completion time, energy per delivered bit, optical circuit utilization, and blocking behavior to real traffic workload. The findings indicate that hybrid EPSOCS architectures achieve much better aggregate throughput and lower energy use without compromising low latency with respect to short and latency-sensitive flows. The optical circuits are utilized highly with little blocking, but the inefficiency is still evident when there is a dynamic traffic. In sum, it can be concluded that optical circuit switching is the most effective when embedded in traffic-conscious hybrid constructions that can provide a viable and scalable approach to next-generation mega data centers networks
Prerana Nilesh Khairnar, R. Mulajkar, Shailesh Kulkarni et al.· Journal of Intelligent Decis...· 0 citations
A new paradigm for satisfying the ever-growing demands of real-time Sixth Generation (6G) applications is Mobile Edge Computing (MEC). Additionally, base stations and Internet of Things devices that incorporate renewable energy harvesting capabilities have the potential to lower grid energy use. To maximize system potential and lower carbon emissions, it is crucial to make effective decisions about job offloading and resource allocation. A carbon-aware MEC architecture that uses both grid and renewable energy sources is proposed in this paper. Our goal is to jointly manage resource allocation and task offloading while monitoring carbon emissions and task queue delays to optimize system behavior under uncertainty, specifically for stochastic workloads and variable renewable generation. To balance these two cost components (emissions and queue length), we create a combined optimization problem. We develop a deep deterministic policy gradient (DDPG)-based joint optimization technique to address this issue in a constantly changing environment. In the optimization, we consider greedy policy (GP) and full offloading (FO), as well as time-average carbon emission (TACE) and time-average queue length (TAQL) as performance metrics, and time-average queue length (TAQL) and full execution (FE) as baseline strategies; we also evaluate normalized time-average cumulative reward (NTACR). This method uses continuous-action reinforcement learning to generate efficient, real-time control policies. For the proposed MEC network, numerical statistics show that our approach can lead to effective offloading and lower carbon emissions.
Mamoon M. Saeed, Rashid A. Saeed, M. A. Ahmed et al.· 2026 6th International Confe...· 0 citations
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