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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "B: Energy and Environment".

Deadline for manuscript submissions: closed (22 March 2021) | Viewed by 13373
The submission system is still open. Please contact the journal editor Adele Min (adele.min@mdpi.com) before submitting a paper.

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Special Issue Editors

Prof. Antonio Fernández Anta

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Guest Editor

IMDEA Networks Institute, 28918 Madrid, Spain
Interests: networks; distributed computing; algorithms; distributed ledgers; data analysis

Prof. Lin Wang

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Guest Editor

Department of Computer Science, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands
Interests: networked systems; mobile computing; energy-efficient computing; in-network computing; video stream analytics

Special Issue Information

Dear Colleagues,

The last decade has witnessed a rapid evolution of IT infrastructures. Cloud computing has become the norm of computing, with a large number of huge data centers deployed all over the globe. With the fast development of new communication technologies such as 5G, LoRa, or LiFi, cloud computing is now evolving into yet another new paradigm called edge computing, providing better support for emerging applications, especially in the Internet-of-Things regime.

Extensive research has been carried out on system design and optimization for improving the performance of the IT infrastructure. However, most of them put their focus on performance metrics including latency, throughput, reliability, and even security. Yet, another important facet—energy efficiency of the IT infrastructure—has received limited attention in the literature. With the increase of the scale of the IT infrastructure, energy efficiency becomes more relevant and calls for more attention.

Many energy-efficiency-related research problems exist and will become a major obstacle to the development of future computing and communication systems. Therefore, the proposed Special Issue aims to bring together academic researchers, industry practitioners, and individuals working on related areas to share their research ideas, views, latest findings, and state-of-the-art research results. We welcome prospective authors to submit their articles on topics including but not limited to the following:

Energy-efficient data centers;
Energy-efficient data center networks;
Energy-efficient network protocols;
Energy-efficient network architecture design;
Energy-efficient algorithms for scheduling and routing;
Energy-efficient edge computing;
Energy efficiency in 5G networks;
Low-power IoT networks;
Energy efficiency of mobile/wearable devices, sensor systems;
Software engineering for energy efficiency.

Prof. Antonio Fernández Anta
Prof. Lin Wang
Guest Editors

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Published Papers (3 papers)

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20 pages, 702 KiB

Open AccessArticle

Improving Energy Efficiency on SDN Control-Plane Using Multi-Core Controllers

by Tadeu F. Oliveira, Samuel Xavier-de-Souza and Luiz F. Silveira

Energies 2021, 14(11), 3161; https://doi.org/10.3390/en14113161 - 28 May 2021

Cited by 17 | Viewed by 4016

Abstract

Software-defined networks have become more common in data centers. The programmability of these networks is a great feature that allows innovation to be deployed fast, following the increasing number of new applications. This growth comes with a cost of more processing power and energy consumption. Many researchers have tackled this issue using existing routing techniques to dynamically adjust the network forwarding plane to save energy. On the control-plane, researchers have found algorithms for positioning the controller in a way to reduce the number of used links, thus reducing energy. These strategies reduce energy consumption at the expense of processing power of the controllers. This paper proposes a novel approach to energy efficiency focused on the network’s control-plane, which is complementary to the many already existing data-plane solutions. It takes advantage of the parallel processing capabilities of modern off-the-shelf multicore processors to split the many tasks of the controller among the cores. By dividing the tasks among homogeneous cores, one can lower the frequency of operations, lowering the overall energy consumption while keeping the same quality of service level. We show that a multicore controller can use an off-the-shelf multicore processor to save energy while keeping the level of service. We performed experiments based on standard network measures, namely latency and throughput, and standard energy efficiency metrics for data centers such as the Communication Network Energy Efficiency (CNEE) metric. Higher energy efficiency is achieved by a parallel implementation of the controller and lowering each core’s frequency of operation. In our experiments, we achieved a drop of 28% on processor energy use for a constant throughput scenario when comparing with the single-core approach. Full article

(This article belongs to the Special Issue Energy Efficiency in Cloud and Edge Computing)

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26 pages, 678 KiB

Open AccessArticle

Energy-Aware Scheduling Based on Marginal Cost and Task Classification in Heterogeneous Data Centers

by Kaixuan Ji, Ce Chi, Fa Zhang, Antonio Fernández Anta, Penglei Song, Avinab Marahatta, Youshi Wang and Zhiyong Liu

Energies 2021, 14(9), 2382; https://doi.org/10.3390/en14092382 - 22 Apr 2021

Cited by 4 | Viewed by 2504

Abstract

The energy consumption problem has become a bottleneck hindering further development of data centers. However, the heterogeneity of servers, hybrid cooling modes, and extra energy caused by system state transitions increases the complexity of the energy optimization problem. To deal with such challenges, in this paper, an Energy Aware Task Scheduling strategy (EATS) utilizing marginal cost and task classification method is proposed that cooperatively improves the energy efficiency of servers and cooling systems. An energy consumption model for servers, cooling systems, and state transition is developed, and the energy optimization problem in data centers is formulated. The concept of marginal cost is introduced to guide the task scheduling process. The task classification method is incorporated with the idea of marginal cost to further improve resource utilization and reduce the total energy consumption of data centers. Experiments are conducted using real-world traces, and energy reduction results are compared. Results show that EATS achieves more energy-savings of servers, cooling systems, state transition in comparison to the other two techniques under a various number of servers, cooling modules and task arrival intensities. It is validated that EATS is effective at reducing total energy consumption and improving the resource utilization of data centers. Full article

(This article belongs to the Special Issue Energy Efficiency in Cloud and Edge Computing)

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32 pages, 3264 KiB

Open AccessFeature PaperArticle

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

by Ce Chi, Kaixuan Ji, Penglei Song, Avinab Marahatta, Shikui Zhang, Fa Zhang, Dehui Qiu and Zhiyong Liu

Energies 2021, 14(8), 2071; https://doi.org/10.3390/en14082071 - 8 Apr 2021

Cited by 29 | Viewed by 4581

Abstract

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. Full article

(This article belongs to the Special Issue Energy Efficiency in Cloud and Edge Computing)

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