Showing papers on "Green computing published in 2020"

An empirical analysis of the non-linear impacts of ICT-trade openness on renewable energy transition, energy efficiency, clean cooking fuel access and environmental sustainability in South Asia

Abstract: Energy security and environmental sustainability have become an integral policy agenda worldwide whereby the global economic growth policies are being restructured to ensure the reliability of energy supply and safeguard environmental well-being as well However, technological inefficiency is one of the major hindrances in attaining these over-arching goals Hence, this paper probed into the non-linear impacts of ICT trade on the prospects of undergoing renewable energy transition, improving energy use efficiencies, enhancing access to cleaner cooking fuels, and mitigating carbon dioxide emissions across selected South Asian economies: Bangladesh, India, Pakistan, Sri Lanka, Nepal, and Maldives The results from the econometric analyses reveal that ICT trade directly increases renewable energy consumption, enhances renewable energy shares, reduces intensity of energy use, facilitates adoption of cleaner cooking fuels, and reduces carbon-dioxide emissions Moreover, ICT trade also indirectly mitigates carbon-dioxide emissions through boosting renewable energy consumption levels, improving energy efficiencies, and enhancing cleaner cooking fuel access Hence, these results, in a nutshell, portray the significance of reducing the barriers to ICT trade with respect to ensuring energy security and environmental sustainability across South Asia Therefore, it is ideal for the government to gradually lessen the trade barriers to boost the volumes of cross-border flows of green ICT commodities Besides, it is also recommended to attract foreign direct investments for the potential development of the respective ICT sectors of the South Asian economies

Chasing Carbon: The Elusive Environmental Footprint of Computing

Abstract: Given recent algorithm, software, and hardware innovation, computing has enabled a plethora of new applications. As computing becomes increasingly ubiquitous, however, so does its environmental impact. This paper brings the issue to the attention of computer-systems researchers. Our analysis, built on industry-reported characterization, quantifies the environmental effects of computing in terms of carbon emissions. Broadly, carbon emissions have two sources: operational energy consumption, and hardware manufacturing and infrastructure. Although carbon emissions from the former are decreasing thanks to algorithmic, software, and hardware innovations that boost performance and power efficiency, the overall carbon footprint of computer systems continues to grow. This work quantifies the carbon output of computer systems to show that most emissions related to modern mobile and data-center equipment come from hardware manufacturing and infrastructure. We therefore outline future directions for minimizing the environmental impact of computing systems.

Secure Automated Forensic Investigation for Sustainable Critical Infrastructures Compliant with Green Computing Requirements

Abstract: SCADA (Supervisory Control and Data Acquisition) networks are built to efficiently provide supervisory and control of national and international critical infrastructures. SCADA networks represent a challenging domain for forensic investigators who have the responsibility to discover the main causes of the catastrophic incidents that could happen in these critical mission systems and provide precise and logical evidences supported with comprehensive technical reports to the legal organizations. They urgently need technological tools and frameworks that enable them to effectively do their mission without affecting the running state of SCADA networks which must be sustainable and robust against technical and disruptive incidents. This paper discusses the challenges and opportunities towards achieving that goal and highlights the emerging technological approaches and paradigms that can be considered as promising for the realization of such a framework taking into account the efficient consumption of computational resources. Further, this paper proposes a conceptual framework for automated and secure forensic investigation in modern complex SCADA networks accompanied with a possible realization architecture based on the Multi-Agent Systems (MAS) and Wireless Sensor Networks (WSN) promising technological paradigms. The proposed framework is intentionally designed to be compliant with the currently active motivation towards promoting green computing requirements.

Profit-Sensitive Spatial Scheduling of Multi-Application Tasks in Distributed Green Clouds

Abstract: An increasing number of organizations choose distributed green data centers (DGDCs) and use their infrastructure resources to deploy and manage multiple applications that flexibly provide services to users around the world in a cost-effective way. The dramatic growth of tasks makes it highly challenging to maximize the total profit of a DGDC provider in a market, where the revenue, price of power grid, solar radiation, wind speed, the maximum amount of energy, on-site air density, and the number of servers in DGDCs all vary with geographical sites. Different from existing studies, this paper designs a profit-sensitive spatial scheduling (PS3) approach to maximize the total profit of a DGDC provider by smartly scheduling all tasks of multiple applications to meet their response time constraints. PS3 can well utilize such spatial diversity of the above factors. In each time slot, the profit maximization for the DGDC provider is formulated as a constrained nonlinear program and solved by the proposed genetic-simulated-annealing-based particle swarm optimization. Real-life trace-driven simulation experiments demonstrate that PS3 realizes higher total profit and throughput than two typical task scheduling methods. Note to Practitioners —This paper investigates the profit maximization problem for a DGDC provider, while the average response time of all arriving tasks of each application is within their corresponding constraint. Existing task scheduling approaches fail to jointly consider the spatial variations in many factors, including the revenue, price of power grid, solar radiation, wind speed, the maximum amount of energy, on-site air density, and the number of servers in DGDCs. Consequently, they cannot schedule all tasks of multiple applications within their response time constraints in a profit-sensitive way. In this paper, a profit-sensitive spatial scheduling (PS3) method that tackles the drawbacks of previous approaches is presented. It is achieved by adopting a proposed genetic-simulated-annealing-based particle swarm optimization algorithm that solves a constrained nonlinear program. Simulation experiments prove that compared with two typical scheduling approaches, it increases the total profit and throughput. It can be readily realized and incorporated into real-life industrial DGDCs. The future work should improve the proposed method by analyzing the indeterminacy in green energy and the uncertainty in tasks.

Critical factors in information technology capability for enhancing firm's environmental performance: case of Indonesian ICT sector

Abstract: Information technology (IT) capability can be adopted by organisations to improve their environmental performance in order to meet environmental regulations, improve their profitability and enhance their competitive position in the marketplace The main purpose of this paper is to develop a research framework which can serve as a basis to understand how IT capability leads to improvement of environmental performance and what critical IT capabilities would improve environmental performance in an organisation The hypothesised framework is developed based on a review of the literature on Resource Based View theory and green IT The sampling unit of this study is from managerial levels in selected 1265 Indonesian ICT organisations based on the list provided by the Ministry of Trade, Republic of Indonesia The findings show that environmental performance is influenced by IT infrastructure quality, IT human resources competence, and environmental IT competence

Toward Energy-Oriented Optimization for Green Communication in Sensor Enabled IoT Environments

Abstract: One of the major bottlenecks toward realizing IoT systems is the energy constraint of sensors. Prolonging network lifetime is a fundamental issue for implementing IoT systems. The energy optimization problem, being NP-hard in nature for scalable networks, has been addressed in the literature using traditional metaheuristic techniques. Quantum inspired metaheuristics have shown better performance than their traditional counterparts in solving such optimization problems in different domains. Toward this end, this article proposes a quantum inspired green communication framework for Energy Balancing in sensor enabled IoT systems (Q-EBIoT). First, an energy optimization model for sensor enabled IoT environments is presented, where energy consumption is derived as cost of the energy-oriented paths. Second, a quantum computing oriented solution is developed for the optimization problem focusing on energy centric solution representation, measurement, and rotation angle. The proposed solution is implemented to evaluate the comparative performance with the state-of-the-art techniques. The evaluation demonstrates the benefit of the proposed framework in terms of various energy-related metrics for sensor enabled IoT environments.

Computing Server Power Modeling in a Data Center: Survey, Taxonomy, and Performance Evaluation

Abstract: Data centers are large-scale, energy-hungry infrastructure serving the increasing computational demands as the world is becoming more connected in smart cities. The emergence of advanced technologies such as cloud-based services, internet of things (IoT), and big data analytics has augmented the growth of global data centers, leading to high energy consumption. This upsurge in energy consumption of the data centers not only incurs the issue of surging high cost (operational and maintenance) but also has an adverse effect on the environment. Dynamic power management in a data center environment requires the cognizance of the correlation between the system and hardware-level performance counters and the power consumption. Power consumption modeling exhibits this correlation and is crucial in designing energy-efficient optimization strategies based on resource utilization. Several works in power modeling are proposed and used in the literature. However, these power models have been evaluated using different benchmarking applications, power-measurement techniques, and error-calculation formulas on different machines. In this work, we present a taxonomy and evaluation of 24 software-based power models using a unified environment, benchmarking applications, power-measurement techniques, and error formulas, with the aim of achieving an objective comparison. We use different server architectures to assess the impact of heterogeneity on the models’ comparison. The performance analysis of these models is elaborated in the article.

An efficient green computing fair resource allocation in cloud computing using modified deep reinforcement learning algorithm

Abstract: Cloud computing provides services and resources in the Internet, and many applications are self-service-supported, on-demand resource allocation-adapted. These dynamic networks allocated necessary resource to the users’ need and they require proper resource allocation scheme. Since various resources are consumed by users if resource allocation is not proper, this leads the system to load imbalance nature. Using Internet-connected devices for storage and computation not only communicates the cloud resources but also connects the devices to network through various protocols. These changes make the network into a complex, dense, heterogeneous system. In this paper, a green computing fair resource allocation through deep reinforcement learning model is proposed to provide efficient resource allocation scheme to the users in the network. Conventional Q-learning model fails in dimensionality problem when the state space increases exponentially. The proposed model is combined with fair resource allocation with deep reinforcement learning to provide better allocation schemes compared to the conventional model.

Green Cloud Multimedia Networking: NFV/SDN Based Energy-Efficient Resource Allocation

Abstract: The rapid growth of communications and multimedia network services such as Voice over Internet Protocol (VoIP) have caused these networks to face a crisis in resources from two perspectives: 1. Lack of resources and, as a result, overload; 2. Redundancy of resources and, as a result, energy loss. Cloud computing allows the scale of resources to be reduced or increased on demand. Many of the gains obtained from the cloud computing come from resource sharing and virtualization technology. On the other hand, the emerging concept of Software-Defined Networking (SDN) can provide a global view of the entire network for integrated resource management. Network Function Virtualization (NFV) can also be used to virtually implement a variety of network devices and functions. In this paper, we present an energy-efficient framework called GreenVoIP to manage the resources of virtualized cloud VoIP centers. By managing the number of VoIP servers and network equipment, such as switches, this framework not only prevents overload but also supports green computing by saving energy. Finally, GreenVoIP is implemented and evaluated on real platforms, including Floodlight, Open vSwitch, and Kamailio. The results suggest that the proposed framework can minimize the number of active devices, prevent overloading, and provide service quality requirements.

Green IS diffusion in organizations: a model and empirical results from Malaysia

Abstract: This article examines the variables that influence information technology (IT) executives’ intention towards diffusing Green Information System (IS) for environmental sustainability attainment in organizations by utilizing IS as a solution to resolve the issues caused by traditional IT usage. Through the review of prior Green IS and Green IT studies, this article proposes a Green IS structural model based on the Unified Theory of Acceptance and Use of Technology, after which survey data were collected from IT executives in various organizations in Malaysia. With 133 valid survey datasets at hand, partial least square-structural equation modelling method was employed to analyse the survey replies. Results show that human infrastructure, administrative policies, IS infrastructure, institutional pressure, IS strategy and knowledge accessibility significantly influence Green IS diffusion. In addition, results disclose that the moderating variable age of IT executives positively influences their behaviour towards adopting Green IS, whereas the gender, education and experience does not influences their behaviour towards Green IS diffusion. Further results reveal that control variables size, sector and revenue of the organization do not influence Green IS diffusion in organizations. Theoretical implication of this research contributes to existing knowledge on Green IS diffusion behaviours of IT executives towards environmental sustainability by offering an agenda and empirically exploring Green IS practice in organizations. Practical implication for this study provides empirical evidence from IT executives emphasizing that Green IS is capable of decreasing the environmental effects of traditional IT infrastructure deployment in organizations.

Energy-efficient migration techniques for cloud environment: a step toward green computing

Abstract: The technology, cloud computing, in present days, is vastly used due to the services it provides and the ease with which they can be availed. The enormous development of the Internet technology is due to the advent of the concept of cloud. Along with its benefits, cloud computing brings along itself a detrimental side effect, i.e., carbon emission. This is due to the massive energy consumption in the cloud data centers. Reduction in energy consumption in cloud is thus one of the major challenges among the researchers. This work conducts a thorough study of the various techniques that help in minimization of energy consumption in data centers. It also explores and proposes approaches to reduce the same, eventually making the environment greener. In the proposed work, prediction mechanism has been adopted and implemented on the existing Minimization of Migration (MM) policy for large history data set, followed by dynamic thresholding mechanism in place of static thresholds. Rigorous simulations have been conducted, and the results show reduction in cloud data center energy consumption.

Blockchain-Based Cyber Threat Intelligence System Architecture for Sustainable Computing

Abstract: Nowadays, the designing of cyber-physical systems has a significant role and plays a substantial part in developing a sustainable computing ecosystem for secure and scalable network architecture. The introduction of Cyber Threat Intelligence (CTI) has emerged as a new security system to mitigate existing cyber terrorism for advanced applications. CTI demands a lot of requirements at every step. In particular, data collection is a critical source of information for analysis and sharing; it is highly dependent on the reliability of the data. Although many feeds provide information on threats recently, it is essential to collect reliable data, as the data may be of unknown origin and provide information on unverified threats. Additionally, effective resource management needs to be put in place due to the large volume and diversity of the data. In this paper, we propose a blockchain-based cyber threat intelligence system architecture for sustainable computing in order to address issues such as reliability, privacy, scalability, and sustainability. The proposed system model can cooperate with multiple feeds that collect CTI data, create a reliable dataset, reduce network load, and measure organizations’ contributions to motivate participation. To assess the proposed model’s effectiveness, we perform the experimental analysis, taking into account various measures, including reliability, privacy, scalability, and sustainability. Experimental results of evaluation using the IP of 10 open source intelligence (OSINT) CTI feeds show that the proposed model saves about 15% of storage space compared to total network resources in a limited test environment.

Social gamification affordances in the green IT services: perspectives from recognition and social overload

Abstract: Recently, both practitioners and researchers are beginning to recognize the great potential of social gamification in green information technology (IT) services. This study focuses on the roles of three social gamification affordances (interactivity, cooperation and competition) in gamified green IT services use, from the perspectives of recognition and social overload.,An online survey is conducted to examine the research model using structural equation modeling with users of Ant Forest, which is an example of green IT services in China.,Results indicate that interactivity, cooperation and competition can positively affect recognition, which further positively affects green IT services use; however, interactivity and cooperation can increase social overload, which negatively affects green IT services use.,This study provides new insights into the effects of social gamification affordances in green IT services by investigating the effects of interactivity, cooperation and competition on recognition and social overload. In addition, this study highlights the positive effect of recognition and negative effect of social overload on gamified green IT services use, extending the literature reviews surrounding gamified services use.

ChainFaaS: An Open Blockchain-Based Serverless Platform

Abstract: Due to the rapid increase in the total amount of data generated in the world, the need for more computational resources is also increasing dramatically. This trend results in huge data centers and massive server farms being built around the world, which have a negative impact on global carbon emissions. On the other hand, there are many underutilized personal computers around the world that can be used towards distributed computing. To better understand the capacity of personal computers, we have conducted a survey that aims to find their unused computational power. The results indicate that the typical CPU utilization of a personal computer is only 24.5% and, on average, a personal computer is only used 4.5 hours per day. This shows a significant computational potential that can be used towards distributed computing. In this paper, we introduce ChainFaaS with the motivation to use the computational capacity of personal computers as well as to improve developers' experience of internet-based computing services by reducing their costs, enabling transparency, and providing reliability. ChainFaaS is an open, public, blockchain-based serverless platform that takes advantage of personal computers' computational capacity to run serverless tasks. If a substantial number of personal computers were connected to this platform, some tasks could be offloaded from data centers. As a result, the need for building new data centers would be reduced with a positive impact on the environment. We have proposed the design of ChainFaaS, and then implemented and evaluated a prototype of this platform to show its feasibility.

Green IoT: Advancements and Sustainability with Environment by 2050

Abstract: Green IoT is eco-friendly technology evolves from IoT, in today’s scenario each and every device connected over internet and store on cloud and these devices are known as “Smart Devices”. At present 31 billion devices are connected as IoT devices and by 2050 it surge pass 170 billion limit so on average Iot devices are increases 12% annually as per the reports in proportionally the carbon footprint percentage and GHG(green-house gas) emission percentage increases and enhance the overall pollution percentage on earth, so to look after this major environmental issues and for proper initiatives against these issues technically as well as environmental prospective first objective of this paper is analyze the carbon footprints of smart devices with IoT for sustainability with environment and provide major steps to minimize it and emphasize the use of “Green IoT” instead of IoT for environment safety perspectives and second objective is improve the LCA assessment model with Deep Learning and Data Mining techniques with various impact factors for better and efficient results.The proposed method cut down the production and manufacturing cycle rate and promotes the recyclability and usability of devices from environmental point of view and from technical point of view it cut down the overall cost and save energy and time during installing phase of smart devices with updating service packs and in overall estimation of GHG(green-house gas)emission rate and carbon footprint rate

Power consumption model based on feature selection and deep learning in cloud computing scenarios

Abstract: High power consumption of cloud data centres is a crucial challenge in modern cloud computing. To comply with the conceptions of green computing, power consumption prediction of the computing cluster has a major role to play in these energy conservation efforts. However, due to complexity and heterogeneity in cloud computing scenarios, it is difficult to accurately predict the power consumption using conventional approaches. To this end, this study presents a power consumption model based on feature selection and deep learning to powerfully cope with low energy efficiency. Different from other methods focusing on only a few performance attributes, the proposed method takes into account up to 12 energy-related features and introduces deep neural network architecture, aiming at making full use of massive data to train model completely. In particular, this approach is composed of three main phases including (i) performance monitoring and energy-related feature acquisition, (ii) essential feature selection, and (iii) model establishment and optimisation. Representative results of comprehensive experiments, in terms of the relative error, reveal that the proposed power consumption model can undoubtedly achieve state-of-the-art predictive capability when compared with other models in most cases.

Interpretable Machine Learning In Sustainable Edge Computing: A Case Study of Short-Term Photovoltaic Power Output Prediction

Abstract: With the Internet of Things continuously penetrating into all spheres of our daily lives, the increasing use of smart devices enabled the emergence of the edge computing paradigm. To meet the needs of saving energy and reducing electricity bills for each household, solar energy is exploited by using photovoltaic (PV) panels that can be integrated into an edge computing platform based on a cost-effective scheduling scheme. However, it is still a major challenge to determine the optimal energy allocation of renewable energy due to the intermittent nature of renewable energy generation. In this paper, we propose a unified clustering-based prediction framework with two tree-based algorithms to provide short-term prediction of PV power output. We also provide the in-terpretability analysis for our approach to reveal the features that are important for the prediction. The experimental results show our proposed framework is superior to other benchmark machine learning algorithms.

A survey: energy-efficient sensor and VM selection approaches in green computing for X-IoT applications

Abstract: Cloud computing (CC) enables enumerable services to manipulate sensor data generated from X-internet of things (IoT) applications. It is accomplished by selecting an accurate decision-makin...

A Holistic Study on Green IT/IS Practices in ICT Departments of Collaborative Enterprise: A Managerial and Practitioners Perspective

Abstract: The purpose of this article is to identify various drivers which influence the information communication technology (ICT) departments of collaborative enterprises (CEs) to infuse green information technology (Green IT) and green information systems (Green IS's) as well as the Green IT/IS practices into their organizations. The identified Green IT/IS drivers and practices were derived from an extensive literature review and later validated based on a multi-case study conducted in four enterprises. Findings from the case study support management and practitioner understanding and initiate environmentally friendly practices for sustainability attainment. Respectively, the Green IT/IS drivers and practices presented in this study adds to the body of knowledge by associating and structuring both managerial and practitioner implications for Green IT/IS infusion and assimilating into ICT departments of CE.

Green computing for Internet of Things

Abstract: Cloud computing services are used to meet the ever-growing demand for IoT. Data centers are increasingly becoming one of the largest consumers of energy to provide the infrastructure for the IoT paradigm. The demand for energy increases in the future as more innovations emerge, and technology follows new practices resulting in green computing being adopted. Green computing strategies reduce energy consumption by IoT devices without degrading their performance. This paper will evaluate numerous aspects of green computing for IoT computing analyzing critical concepts, challenges, and remediation.

Cost-Efficient Outsourced Decryption of Attribute-Based Encryption Schemes for Both Users and Cloud Server in Green Cloud Computing

Abstract: To reduce a user’s decryption cost and protect the private information from being leaked, Green et al. proposed an approach oursourcing the decryption of the attribute based encryption (ABE) scheme to the cloud server. Later, almost all ABE schemes with outsourced decryption (ABE-OD) used their model or approach. However, the cloud server needs to repeat the outsourced decryption service of the same ciphertext for distinct users satisfying the same access policy in these schemes. Green computing is the atmosphere conscientious and recyclable utilization of resources. The green cloud networks can reduce their cost or energy requirements by adapting its performance, optimizing resources management and services. The method is not efficient for the cloud server in the green cloud networks. In this article, to take into account recyclable utilization of resources for the cloud server, we put forward a new and secure approach to reduce total overhead of the cloud server when many users satisfying an access policy require the outsourced decryptions for the same ciphertext besides decreasing the decryption computation cost for users. Compared with the existing ABE-OD schemes, our total overhead of the cloud server is independent of the number of the users who satisfy an access policy and request the outsourcing decryption service. Finally, we extend our approach to a RCCA-secure ABE-OD scheme.

Optimizing server refresh cycles: The case for circular economy with an aging Moore's Law

Abstract: Demand for digital services is increasing significantly. Addressing energy efficiency at the data center mechanical and electrical infrastructure level is starting to suffer from the law of diminishing returns. IT equipment, specifically servers, account for a significant part of the overall facility energy consumption and environmental impact, and thus, present a major opportunity, not the least from a circular economy perspective. To reduce the environmental impact of servers, it is important to realize the effect of manufacturing, operating, and disposing of servers on the environment. This work presents new insights into the effect of refreshing servers with remanufactured and refurbished servers on energy efficiency and the environment. The research takes into consideration the latest changes in CPU design trends and Moore’s law. The study measures and analyzes the use phase energy consumption of remanufactured servers vs new servers with various hardware configurations. Case studies are used to evaluate the potential impact of refurbished server refresh from an economic as well as environmental perspectives.

Security, Privacy, and Efficiency of Sustainable Computing for Future Smart Cities

Abstract: Sustainable computing is a rapidly expanding field of research covering the fields of multidisciplinary engineering. With the rapid adoption of Internet of Things (IoT) devices, issues such as security, privacy, efficiency, and green computing infrastructure are increasing day by day. To achieve a sustainable computing ecosystem for future smart cities, it is important to take into account their entire life cycle from design and manufacturing to recycling and disposal as well as their wider impact on humans and the places around them. The energy efficiency aspects of the computing system range from electronic circuits to applications for systems covering small IoT devices up to large data centers. This editorial focuses on the security, privacy, and efficiency of sustainable computing for future smart cities. This issue accepted 17 articles after a rigorous review process.