Proceedings of the 5th International Workshop on Energy Efficient Data Centres最新文献

Evaluation process of demand response compensation models for data centers 数据中心需求响应补偿模型的评价过程

Proceedings of the 5th International Workshop on Energy Efficient Data Centres Pub Date : 2016-06-21 DOI: 10.1145/2940679.2940683

Benedikt Kirpes, Sonja Klingert

引用次数: 5

Energy supply aware power planning for flexible loads 灵活负载下的供电感知电力规划

Proceedings of the 5th International Workshop on Energy Efficient Data Centres Pub Date : 2016-06-21 DOI: 10.1145/2940679.2940681

F. Niedermeier, Fiodar Kazhamiaka, H. Meer

引用次数: 3

Energy effciency and performance of cloud data centers: which role can modeling play? 云数据中心的能源效率和性能:建模可以发挥什么作用?

Proceedings of the 5th International Workshop on Energy Efficient Data Centres Pub Date : 2016-06-21 DOI: 10.1145/2940679.2940688

P. Kühn

引用次数: 6

Reducing energy costs in data centres using renewable energy sources and energy storage 使用可再生能源和能源存储降低数据中心的能源成本

Proceedings of the 5th International Workshop on Energy Efficient Data Centres Pub Date : 2016-06-21 DOI: 10.1145/2940679.2940684

Ariel Oleksiak, Wojciech Piątek, K. Kuczynski, Franciszek Sidorski

引用次数: 7

Competitive online algorithms for geographical load balancing in data centers with energy storage 具有能量存储的数据中心地理负载平衡的竞争性在线算法

Proceedings of the 5th International Workshop on Energy Efficient Data Centres Pub Date : 2016-06-21 DOI: 10.1145/2940679.2940680

Chi-Kin Chau, L. Yang

引用次数: 6

Optimizing the power factor of data centers connected to the smart grid 优化接入智能电网的数据中心的功率因数

Proceedings of the 5th International Workshop on Energy Efficient Data Centres Pub Date : 2016-06-21 DOI: 10.1145/2940679.2940682

T. Cioara, I. Anghel, I. Salomie, Marcel Antal, M. Bertoncini, D. Arnone

{"title":"Optimizing the power factor of data centers connected to the smart grid","authors":"T. Cioara, I. Anghel, I. Salomie, Marcel Antal, M. Bertoncini, D. Arnone","doi":"10.1145/2940679.2940682","DOIUrl":"https://doi.org/10.1145/2940679.2940682","url":null,"abstract":"The Data Centre (DC) services business is blooming increasing their energy demand and making them important players in the safe operation of the smart grid. The DC IT servers have a dynamic power factor varying from 0.98 for high density workloads and 0.8 for 20% server usage. A sub unitary power factor means that the voltage and current waveforms are not in phase making the electrical grid less efficient thus increasing the network loses. To make things even more challenging lately the grid operators have started to charge penalties if DC power factor is sub unitary. To improve their power factor with no extra hardware and no retrofitting, we propose an innovative method based on scheduling delay tolerant workload to achieve high servers' level usage thus increasing the leading factor and dynamically usage of nonelectrical cooling mechanisms such as the Thermal Energy Storage (TES) and this way increasing the lagging power factor. Simulation results are promising showing that a power factor with a value close to 1 can be achieved and maintained by proper planning the DC flexible power resources operation.","PeriodicalId":268208,"journal":{"name":"Proceedings of the 5th International Workshop on Energy Efficient Data Centres","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124242779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

DC4Cities power planning: sensitivity to renewable energy forecasting errors 城市电力规划:对可再生能源预测误差的敏感性

Proceedings of the 5th International Workshop on Energy Efficient Data Centres Pub Date : 2016-06-21 DOI: 10.1145/2940679.2940686

A. Alyousef, F. Niedermeier, H. Meer

{"title":"DC4Cities power planning: sensitivity to renewable energy forecasting errors","authors":"A. Alyousef, F. Niedermeier, H. Meer","doi":"10.1145/2940679.2940686","DOIUrl":"https://doi.org/10.1145/2940679.2940686","url":null,"abstract":"Data centers are among the largest and fastest growing consumers of electricity in the world. Furthermore, the rapid growth of digital content, big data, e-commerce, and internet traffic will create the need for an even higher number of DCs. On other side, in spite of the variability of renewable resources, due to characteristic weather fluctuations, the significant progress has been made in the renewable energy generation industry in terms of reducing installation cost and increasing integration into the power grid represents a good motive to tune data center software execution load in such a way that power consumption matches renewable energy availability (about 5%--30% of total DC load can be shifted [20]). This is especially viable in the context of smart cities, where the existence of a demand side management scheme can be assumed. In the context of the European project \"DC4Cities\", a similar scheme has been developed which consists of two phases. In the first phase, a concrete guidelines on power use for participating consumers to be followed is calculated. In the second phase, the control systems should find using this guidelines the best desired power values in terms of renewable percentage and SLAs. In this paper, an algorithm to calculate the aforementioned concrete guidelines by a component named \"Max/Ideal Power Planner\", based on smart city goals and renewable power availability forecasts, is proposed. In addition, the robustness of complete control system, particularly the Max/Ideal Power Planner, is estimated by evaluating the impact of renewable forecast accuracy on the scheduling of jobs in the data center via the proposed control system. Two types of errors in renewable forecasting are discussed: constant error and random error.","PeriodicalId":268208,"journal":{"name":"Proceedings of the 5th International Workshop on Energy Efficient Data Centres","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114727289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Learning-based power prediction for data centre operations via deep neural networks 基于学习的深度神经网络数据中心运行功率预测

Proceedings of the 5th International Workshop on Energy Efficient Data Centres Pub Date : 2016-06-21 DOI: 10.1145/2940679.2940685

Yuanlong Li, Han Hu, Yonggang Wen, Jun Zhang

{"title":"Learning-based power prediction for data centre operations via deep neural networks","authors":"Yuanlong Li, Han Hu, Yonggang Wen, Jun Zhang","doi":"10.1145/2940679.2940685","DOIUrl":"https://doi.org/10.1145/2940679.2940685","url":null,"abstract":"Modelling and analyzing power consumption for data centres can diagnose potential energy-hungry components and applications, and facilitate in-time control, benefiting the energy efficiency of data centers. However, solutions to this problem, including static power models and canonical prediction models, either aim to build a static relationship between power consumption and hardware/application configurations without considering the dynamic fluctuation of power; or simply treat it as time series, ignoring the inherit power data characteristics. To tackle these issues, in this paper, we present a systematic power prediction framework based on extensive power dynamic profiling and deep learning models. In particular, we first analyse different power series samples to illustrate their noise patterns; accordingly we propose a power data de-noising method, which lowers noise interference to the modelling. With the pretreated data, we propose two deep learning based prediction models, including a fine-grained model and a coarse-grained model, which are suitable for different time scales. In the fine-grained prediction model, a recursive autoencoder (AE) is employed for short-duration prediction; in the coarse-grained model, an AE is used to encode massive fine-grained historical data as a further data pretreatment for long-duration prediction. Experimental results show that our proposed models outperform canonical prediction methods with higher accuracy, up to 79% error reduction for certain cases.","PeriodicalId":268208,"journal":{"name":"Proceedings of the 5th International Workshop on Energy Efficient Data Centres","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114777380","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 31

Proceedings of the 5th International Workshop on Energy Efficient Data Centres 第五届能源效率数据中心国际研讨会论文集

Proceedings of the 5th International Workshop on Energy Efficient Data Centres Pub Date : 1900-01-01 DOI: 10.1145/2940679

引用次数: 0