Green Data Centers Research Articles

An Optimal Task Placement Strategy in Geo-Distributed Data Centers Involving Renewable Energy

Nowadays, modern data centers are seeking for importing renewable energy together with conventional energy in order to be more environment-friendly and to reduce operation expenditures. Meanwhile, considering the fact that electricity prices and renewable energy generations are diverse in time and geography, a task scheduling strategy should be designed to ensure the efficient and economic operations of data centers. In this paper, an optimal task placement strategy is presented for geo-distributed data centers powered by mixed renewable and conventional energies with dynamic voltage and frequency scaling technique. We aim at minimizing the total electricity cost and making full use of the renewable energy so as to construct green and economic data centers. The optimal task placement problem is formulated as a mixed integer nonlinear problem (MINLP), in which the quality-of-service constraint is restricted by an M/G/1 queuing model. To tackle the complexity of the MINLP, we first transform it into a tractable form, and then develop an optimal sever activation configuration and task placement algorithm to solve it. The proposed algorithm can obtain the global optimal solution of the electricity minimization problem and meanwhile dramatically reduce the complexity of the problem solving. Finally, evaluations based on real-world traces exhibit impacts of different system parameters on the electricity cost and sever activation configurations, which prove the superiority of our proposed algorithm and provide us some illuminations on how to build cost-effective and eco-friendly data centers.

A Descriptive Study towards Green Computing Practice Application for Data Centers in IT Based Industries

The progressive upsurge in demand for processing and computing power has led to a subsequent upsurge in data center carbon emissions, cost incurred, unethical waste management, depletion of natural resources and high energy utilization. This raises the issue of the sustainability attainment in data centers of Information Technology (IT) based industries. Green computing practice can be applied to facilitate sustainability attainment as IT based industries utilizes data centers to provide services to staffs, practitioners and end users. But it is a known fact that enterprise servers utilize huge quantity of energy and incur other expenditures in cooling operations and it is difficult to address the needs of accuracy and efficiency in data centers while yet encouraging a greener application practice alongside cost reduction. Thus this research study focus on the practice application of Green computing in data centers which houses servers and as such presents the Green computing life cycle strategies and best practices to be practiced for better management in data centers in IT based industries. Data was collected through questionnaire from 133 respondents in industries that currently operate their in-house data centers. The analysed data was used to verify the Green computing life cycle strategies presented in this study. Findings from the data shows that each of the life cycles strategies is significant in assisting IT based industries apply Green computing practices in their data centers. This study would be of interest to knowledge and data management practitioners as well as environmental manager and academicians in deploying Green data centers in their organizations.

Managing Battery Aging for High Energy Availability in Green Datacenters

Energy storage devices (ESD), such as UPS batteries, have been repurposed in datacenter as a promising tuning knob for peak power shaving and power cost reducing. However, batteries progressively aging due to irregular usage patterns, which result in less effective capacity and even pose serious threat to server availability. Nevertheless, prior proposals largely ignore the aging issues of battery which may lead to low energy availability for datacenter servers. To fill this critical void, we thoroughly investigate battery aging on a heavily instrumented prototype system over an observation period of ten months. We propose Battery Anti-Aging Treatment Plus ( BAAT-P ), a novel power delivery architecture included aging management algorithms from the perspective of computing system to hide, reduce, mitigate and plan the battery aging effects for high energy availability in datacenter. Our techniques exploit diverse battery aging mechanisms and dynamic aging management algorithms to provide system-level availability guarantee for datacenter. We evaluate the BAAT-P design with a real prototype. Compared with a battery powered datacenter without aging management policies, the results show that BAAT-P can extend battery lifetime by 72 percent, reduce battery cost by 33 percent and effectively improve energy availability for datacenter servers while maintaining workload performance for the performance critical workloads.

Implementation guidelines for green data centres

Increased reliance on technology and online transactions has increased the heat generated in data centres, due to greater access, storage, aggregation and analysis of such data. This paper discusses the downsides of traditional data centres and highlights the importance of applying green IT practices. It explores the benefits and proposes guidelines on shifting to green data centres. The recommendations to go green are to: a) reduce energy by applying green IT practices, b) eliminate redundancies in server systems and cooling modules, c) turn on power management tools on servers and terminals when applicable, d) utilise newer technologies of power consumption and e) minimise internal barriers to establish a good energy management policy. The benefits expected from the proposed approach are minimised pollution levels and lower the cost of activities, among others. The findings of the study will also contribute to face the challenge of global warming and help in better management and control of power consumption.
 Keywords: Traditional data centres, green data centres, green computing, emission.

Message from editor

Dear Readers,
 
 It is a great honor for us to publish Volume 7, Issue 1 of Global Journal of Computer Sciences: Theory and Research. 
 
 Global Journal of Computer Sciences: Theory and Research (GJCS) is an international online journal for scientists and professionals involved in all aspects of computer sciences. This journal serves as an international platform that fosters theoretical and practical issues and new applications for all scientists and professionals from the field of computer sciences including original research and innovative applications from all parts of the world.
 
 Aim of this issue is to give the researchers an opportunity to share the results of their academic studies. There are different research topics discussed in the articles. For example, Musa Peker, Ali Sasar, Osman Ozkaraca and Gurbuz Akcay examined estimation of HbA1c value using artificial neural networks. Fatih Tekmen and Ozgun Tanriover provided an an assessment of a government website by focusing on website usability evaluation with quickly applicable guidelines. In addition, Abdallah Tubaishat and Sara Sumaidaa carried out research on implementation guidelines for green data centres. Mohamed Rahou, Fethi Sebaa and Abdelmadjid Cheikh Sid Ahmed Benmansour discussed error compensation in NC machine tools. Amel Ourici, Wafa Tourab and Merabet Leila examined direct control of active and reactive power of a doubly fed induction machine. Sekhane Hocine and Labed Djamel investigated standard IEEE test systems 57-bus and Algerian 59-bus together and they concluded that the results of power flow compared with the popular MATPOWER software environment show the exactitude of our code calculation, and the enhancement of voltage profile, especially in buses where STATCOM is placed. Finally, Cigdem Bakir proposed a speech recognition system for Turkish language with hybrid method. 
 
 A total number of ten (10) manuscripts were submitted for this issue and each paper has been subjected to double-blind peer review process by the reviewers specialized in the related field. At the end of the review process, a total number of seven (7) high quality research papers were selected and accepted for publication.
 We present many thanks to all the contributors who helped us to publish this issue.
 Best regards
 Prof. Dr. Dogan Ibrahim
 Editor – in Chief

Review on Green Technology Implementation Challenges in University Data Centre

A data centre is a network of computer servers used by an organization to either save, process or distribute large amounts of data. A green data centre is a repository of data where electrical, mechanical, lighting and computer systems are designed for maximum energy efficiency and minimum environmental impact. Due to increase demands of data in the university, data centre not only consume high quantities of energy, but produce carbon dioxide along with other IT inefficiencies. With the dynamic changes in technology and the exponential rise in quantities of data, the need for implementing green data centre have become the need of the hour. Green technology in data centre will not only reduce costs and emissions, but will provide operational benefits as well. However, there are challenges that need to be faced in order to implement green technology in university data centre. This review will explore what is green technology is all about, its challenges that may hampered to the implementation in university Data Centre and solutions that may combat the limits.

On energy consumption of switch-centric data center networks

Data center network (DCN) is the core of cloud computing and accounts for 40% energy spend when compared to cooling system, power distribution and conversion of the whole data center (DC) facility. It is essential to reduce the energy consumption of DCN to ensure energy-efficient (green) data center can be achieved. An analysis of DC performance and efficiency emphasizing the effect of bandwidth provisioning and throughput on energy proportionality of two most common switch-centric DCN topologies: three-tier (3T) and fat tree (FT) based on the amount of actual energy that is turned into computing power are presented. Energy consumption of switch-centric DCNs by realistic simulations is analyzed using GreenCloud simulator. Power-related metrics were derived and adapted for the information technology equipment processes within the DCN. These metrics are acknowledged as subset of the major metrics of power usage effectiveness and data center infrastructure efficiency, known to DCs. This study suggests that although in overall FT consumes more energy, it spends less energy for transmission of a single bit of information, outperforming 3T.

Green Data Center Placement in Optical Cloud Networks

The rapid growth of cloud services has raised concerns on the environmental sustainability of cloud computing, as data centers (DCs) consume a huge amount of brown energy, i.e., energy derived from polluting sources such as coal, oil, natural gas, etc. One way to decrease carbon emissions of DCs is to replace brown energy with green energy, i.e., energy produced by renewable sources, such as wind farms, solar panels, hydroelectric dams, etc. But, to maximize utilization of green energy, effective decisions need to be taken both at the “DC placement” stage (i.e., when placing DCs) and the “DC addition” stage (i.e., when adding new DCs to accommodate traffic growth). The resulting green DC-placement problem is characterized by a tradeoff between brown energy reduction and cost reduction. On one hand, due to geo-diverse locations of renewable energy sources, and due to the need for low latency and high availability for users, a large number of DCs should be placed. On the other hand, capital and operational expenditure would significantly increase if a large number of DC is deployed. In this paper, we propose two solution methods, based on multiobjective optimization, to address the tradeoff between brown energy consumption and cost in cloud networks in both DC placement and DC addition scenarios. We show via simulations how to choose the optimal number of DCs and their locations over two study cases based on NSFNET and USNET topologies.

Oasis: Scaling Out Datacenter Sustainably and Economically

As big data applications proliferate, datacenters today are increasingly looking to adopt a scale-out model. Nevertheless, power capacity has become an important bottleneck that restricts horizontal scaling of servers, especially in datacenters that oversubscribe power infrastructure. When a datacenter hits its ceiling for power provisioning, conventionally the owner has to either build another facility or upgrade existing infrastructure—both approaches add huge cost, require significant time, and can further increase carbon footprint. This paper proposes Oasis, a novel datacenter expansion strategy that enables power-/carbon- constrained servers to scale out economically and sustainably. The basic structure of Oasis, called Oasis Node, naturally supports incremental capacity expansion with near-zero environmental impact since it leverages modular solar panels and distributed battery systems to power newly added servers. To optimize the operation of newly added nodes, we further propose a management framework called Ozone. It allows Oasis to jointly perform power supply switching and server speed scaling to improve efficiency locally and globally. We implement a prototype of Oasis and use it as a research platform for evaluating the design tradeoffs of green scale-out datacenters. With Oasis, a green datacenter could gradually double its capacity with near-oracle performance, extended battery lifetime, and 26 percent cost savings.

Complete tolerance relation based parallel filling for incomplete energy big data

With the approaching of cloud and big data computing era, renewable energy such as solar energy is increasingly integrated into data center power provisioning systems. Nevertheless, the power statistics collection may not be possible or available due to the fact that renewable energy supply exhibits intermittency, time varying behavior (e.g. shortage or failure), resulting in missing data. In this paper, we propose a filling algorithm based on complete tolerance class to solve the missing of energy big data issue. Note that traditional method based on rough sets will likely to fail when there is severe missing data, and its solution on tolerance relation and tolerance class is more complex, which is not suitable for the large scale and the time varying energy big data. Our proposed algorithm expands the tolerance relation into the complete tolerance relation to partition the complete tolerance class. Moreover, our algorithm fills the missing attribute values of the energy big data in data center, which ensures the data integrity and improve the classification accuracy. We further parallelize and optimize our algorithm on state-of-the-art Spark cluster computing framework.In addition, we propose the adaptive management architecture that handles incomplete energy big data in green data centers. Our proposed architecture integrates the techniques for preprocessing energy data, filling incomplete energy data and building decision model. It increases the power assignment efficiency between solar power and utility, while enhancing load performance and service availability. As a result, it can provide better service for green data centers. We perform comprehensive experiments on an energy data set and the results show the Completing Incomplete Big Data (CIBD) algorithm can guarantee the completeness of data while improving the filling accuracy by 10% compared to general filling algorithms such as MEAN or ERS. The proposed algorithm and architecture show more benefit as the data missing rate increases. We further utilize the filled data to establish the random forest model and yield desirable results. Compared to the Hadoop based filling algorithm, the processing speed of the CIBD algorithm improves by 50% on the 4GB data size.

Study and Analysis of Energy Efficient Data center for Sustainable Development of ICT

:Data canters are the foundation, of contemporary information Technology. The Cloud services and web applications requires data centres having huge storage, network and computation capacity, has driven increase of extensive complex data centres running many of today’s Internet, financial, commercial and business applications. With continuous increase in the demands of clouds services and web application the need for remote storage and computation will certainly grow. Data centres are large group of servers and consume huge quantity of computation power to drive and run these server farms bringing about numerous difficulties like enormous energy consumption, emanation of greenhouse gasses, security , backup and recovery ; This paper study and analyse the methods to achieve energy efficiency in datacenter for sustainable development of ICT and low carbon green IT structures for these large and complex data centres to save consumption of electricity and reduce the emission of greenhouse gases to lower the effects of global warming. The structure utilizes most recent energy saving techniques like virtualization, cloud computing and green metrics to achieve green data canters. It also explores five stage to legitimately actualize green IT structure strategies to accomplish green data centres

Cloud Computing Cost and Energy Optimization through Federated Cloud SoS

The two most significant differentiators among contemporary cloud computing service providers have increased green energy use and data center resource utilization. This paper addresses these two issues from a system's architectural optimization viewpoint. The proposed approach herein allows multiple cloud providers to utilize their individual computing resources in three ways by: (1) cutting the number of datacenters needed, (2) scheduling available datacenter grid energy via aggregators to reduce costs and power outages, and lastly by (3) utilizing, where appropriate, more renewable, and carbon-free energy sources. Altogether our proposed approach creates an alternative paradigm for a federated cloud system-of-systems (SoS) approach. The proposed paradigm employs a novel control methodology that is tuned to obtain both financial and environmental advantages. It also supports dynamic expansion and contraction of computing capabilities for handling sudden variations in service demand as well as for maximizing usage of time-varying green energy supplies. Herein we analyze the core SoS requirements, concept synthesis, and functional architecture with an eye on avoiding inadvertent cascading conditions. We suggest a physical architecture that diminishes unwanted outcomes while encouraging desirable results. Finally, in our approach, the constituent cloud services retain their independent ownership, objectives, funding, and sustainability means.

Green Data Centres integration in smart grids: New frontiers for ancillary service provision

The paper presents a study, conceived within the GEYSER FP7 co-funded European research project, on the ancillary services that Green Data Centres (GDCs) can provide, focusing, in particular, on spinning reserve.More in detail, the main contribution of the paper is to show how GDCs can be involved in the secondary frequency regulation process of a smart distribution grid, for contrasting the disturbing effects of unexpected variation of the energy production.With this aim, a real data centre, owned by Engineering S.p.A., is simulated as connected to the Medium Voltage IEEE 14-bus test grid where some RES-based generators are supposed to exist.Simulations are carried out in order to evaluate the impact of power management actions to be executed by the data centre during under-frequency transients, due to an event of renewable energy facilities power production decrease. During this event, the effects of the spinning reserve provided by the data centre are deeply analysed.

On time-sensitive revenue management in green data centers

In this paper, we design an analytically and experimentally better online energy and job scheduling algorithm with the objective of maximizing net profit for service providers in green data centers. We first study the previously known algorithms and conclude that these online algorithms have provable poor performance in their worst-case scenarios. To guarantee an online algorithm's performance in hindsight, we design a randomized algorithm to schedule energy and jobs in the data centers and prove the algorithm's expected competitive ratio in a special setting. Our algorithm is theoretical-sound and it outperforms the previously known algorithms in many settings using both real traces and simulated data. An optimal offline algorithm is also provided as an empirical benchmark.

Design and Operational Analysis of a Green Data Center

Data centers are at the heart of the IT-driven economy. Power consumption of a single data center can range from tens to a hundred megawatts, and operational costs can run into millions of dollars a month. Data center operators incorporate careful design and optimizations to reduce large-scale data centers energy consumption. Because data center design and operations are a source of competitive advantage, insight into modern data centers is scarce. This article describes the design and analysis of a state-of-the-art green university data center, and provides several insights into its operational and efficiency characteristics.

Probability Based Virtual Machines Placement for Green Data Center

Probability Based Virtual Machines Placement for Green Data Center

Green Data Center Design of Udayana University

Green Data Center Design of Udayana University

Bilateral Electricity Trade Between Smart Grids and Green Datacenters: Pricing Models and Performance Evaluation

Datacenter demand response is a promising approach for mitigating operational instability faced by smart grids. It enables significant potentials in peak load shedding and facilitates the incorporation of distributed generation and intermittent energy sources. This paper considers two key aspects toward real-time electricity pricing for eliciting demand response: 1) two-way electricity flow between smart grids and large datacenters with hybrid green generation capabilities and 2) the geo-distributed nature of large cloud systems, and hence the potential competition among smart grids that serve different datacenters of the cloud. We propose a pricing scheme tailored for geo-distributed green datacenters, from a multi-leader (smart grids) single-follower (cloud) game point of view. At the cloud side, in quest for scalability, robustness, and performance, the energy cost minimization problem is solved in a distributed manner, based on the technique of alternating direction method of multipliers. At the smart grid side, a practical equilibrium of the multi-leader single-follower pricing game is desired. To this end, we employ the technique of equilibrium problem with equilibrium constraints and exact linearization, to accurately transform the multi-leader single-follower pricing game, which is non-convex into a mixed integer linear system that can be readily solved. The effectiveness of the proposed solutions is evaluated based on the real datacenter workload traces and the IEEE 14-bus test systems with real generation and demand data.

Adaptive TrimTree: Green Data Center Networks through Resource Consolidation, Selective Connectedness and Energy Proportional Computing

A data center is a facility with a group of networked servers used by an organization for storage, management and dissemination of its data. The increase in data center energy consumption over the past several years is staggering, therefore efforts are being initiated to achieve energy efficiency of various components of data centers. One of the main reasons data centers have high energy inefficiency is largely due to the fact that most organizations run their data centers at full capacity 24/7. This results into a number of servers and switches being underutilized or even unutilized, yet working and consuming electricity around the clock. In this paper, we present Adaptive TrimTree; a mechanism that employs a combination of resource consolidation, selective connectedness and energy proportional computing for optimizing energy consumption in a Data Center Network (DCN). Adaptive TrimTree adopts a simple traffic-and-topology-based heuristic to find a minimum power network subset called ‘active network subset’ that satisfies the existing network traffic conditions while switching off the residual unused network components. A ‘passive network subset’ is also identified for redundancy which consists of links and switches that can be required in future and this subset is toggled to sleep state. An energy proportional computing technique is applied to the active network subset for adapting link data rates to workload thus maximizing energy optimization. We have compared our proposed mechanism with fat-tree topology and ElasticTree; a scheme based on resource consolidation. Our simulation results show that our mechanism saves 50%–70% more energy as compared to fat-tree and 19.6% as compared to ElasticTree, with minimal impact on packet loss percentage and delay. Additionally, our mechanism copes better with traffic anomalies and surges due to passive network provision.

Session details: Special Issue on the 2016 Greenmetrics Workshop

Session details: Special Issue on the 2016 Greenmetrics Workshop