green cloud computing research papers 2022

Green Cloud Computing: A Sustainable Energy-Efficiency Approach for Business Rapidity and the Environment

• Conference paper
• First Online: 15 December 2023
• Cite this conference paper

• Sadia Sultana Bristy 16 ,
• Tooba Azam 16 ,
• Mukta Marion Islam 16 ,
• Raisur Rahman 16 ,
• Ahmed Wasif Reza 16 &
• Mohammad Shamsul Arefin 17 , 18  

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 854))

Included in the following conference series:

• International Conference on Intelligent Computing & Optimization

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Cloud computing is significantly assisting in addressing IT issues. To lessen the environmental impact, a thorough investigation of cloud computing and energy-efficient algorithms is required. A green cloud computing strategy aims to build an environment for cloud computing that is more energy-efficient, maximizes resource consumption without overprovisioning, leaves fewer carbon footprints, reduces Greenhouse Gas emissions, and minimizes E-wastages. Some features and techniques that Green Cloud Computing focuses on bringing to the cloud environment include containerization, designing green data center facility infrastructure, server consolidation, virtual machine placement, designing green networks and protocols, green data management, sustainable software development, and safe disposal techniques with the three R’s (Reuse, Reduce and Recycle). This paper offers a thorough examination of green cloud computing and its traits. The main objective of this study is to investigate green cloud computing, including its various components and conversion processes, as well as to compare energy-efficient VM placement techniques for green cloud computing.

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Department of Computer Science and Engineering, East West University, Dhaka, Bangladesh

Sadia Sultana Bristy, Tooba Azam, Mukta Marion Islam, Raisur Rahman & Ahmed Wasif Reza

Department of Computer Science and Engineering, Daffodil International University, Dhaka, Bangladesh

Mohammad Shamsul Arefin

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Bristy, S.S., Azam, T., Islam, M.M., Rahman, R., Reza, A.W., Arefin, M.S. (2023). Green Cloud Computing: A Sustainable Energy-Efficiency Approach for Business Rapidity and the Environment. In: Vasant, P., et al. Intelligent Computing and Optimization. ICO 2023. Lecture Notes in Networks and Systems, vol 854. Springer, Cham. https://doi.org/10.1007/978-3-031-50151-7_30

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Shifting from Cloud Computing to Green Cloud and Edge Computing

Proceedings of the International Conference on Innovative Computing & Communication (ICICC) 2022

22 Pages Posted: 14 Mar 2023

Nidhi Chaurasia

Maharaja Agrasen Institute of Technology

Divyansh Goyal

Ashish khanna, piyush pareek.

Nitte Meenakshi Institute of Technology, Bengaluru

Date Written: March 13, 2023

“Cloud” is a consolidated term for a huge variety of practical innovations, research, development, tendencies, and possibilities. Cloud Computing enables substantial changes in the way we live. The cloud represents a major shift away from the traditional way businesses think about IT resources. Cloud Computing offers economical services, which are in demand. The increase in demand and requirements is the cause for huge enhancement in cloud infrastructure causing increase in energy consumption and pollution. Hence, the need of the hour is Green cloud computing, which can save energy, carbon footprints, and reduce enterprises' operational costs. In the proposed exposition, the strategies for transformation to Green Cloud Computing has been done. to achieve efficient replica management, virtualization, resource-scheduling, and power scaling. Several ways of utilizing sustainable resources have been discussed to reuse the heat generated from computing devices by thermal-heat recovery methods and free cooling systems. Another step to achieve further reduction in after effects of high energy consumption is shifting towards Edge computing for less power consumption, low latency, fast real-time responses with speedy processing, reduced processing cost, and enhanced device performance with respect to cloud-based services. In the presented work, to authenticate the shift towards edge computing, load balancing has been discussed. To manage network traffic at edge servers and maximize resource utilization by scheduling user requests with respect to processing time, load balancers should be used. The paper also discusses the longest processing time, a greedy-based scheduling algorithm for load-balancing server placement.

Keywords: Green Cloud Computing, Carbon footprints, Virtualization, Longest Processing Time algorithm , Edge Computing

Suggested Citation: Suggested Citation

Nidhi Chaurasia (Contact Author)

Maharaja agrasen institute of technology ( email ).

New Delhi, 110086 India

Nitte Meenakshi Institute of Technology, Bengaluru ( email )

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Energy Engineering eJournal

• Corpus ID: 254873919

GREEN CLOUD COMPUTING: AN APPROACH TOWARDS SUSTAINABILITY

• Mr. Gopala , Krishna Sriram
• Published 2022
• Environmental Science, Computer Science

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Green Cloud Computing

To reduce the energy consumption and cut down carbon emission in Cloud Computing.

Advances in Information and Communications Technology over the last serval decades have transformed computing into the 5th utility, after water, electricity, gas, and telephony. Nowadays, more and more companies and organizations migrate computation workload to cloud, and modern cloud computing platforms become increasingly large-scale and distributed. With such a massive scale cloud computing system, we not only can provide more cost-effective and reliable services but also open the opportunities of improving energy efficiency and lowering carbon footprint for the whole society. Every small improvement would mean a lot.

While the world will need to reach carbon negative, those of us who can afford to move faster and go further should do so. Following this spirit, we have been actively pursuing a sustainable form of cloud computing, Green Cloud Computing, to reduce the energy consumption and cut down carbon emission accordingly. Our current focus lies in three main areas:

• Carbon-aware workload scheduling by adopting the multi-constraint and multi-objective optimization framework.
• Boost resource utilization efficiency to reduce future purchases of hardware so that much energy can be saved during the hardware manufacturing process.
• Optimize device lifespan and recycling by predicting hardware failures and developing management system to fully utilize hardware with different levels of reliability.
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Proceedings of the 2024 3rd International Conference on Information Economy, Data Modelling and Cloud Computing (ICIDC 2024)

Design and application of a green computing cloud platform based on scalable orchestration

With the gradual development and popularization of cloud computing technology, cloud computing has become an important support for government and enterprise information construction. However, traditional cloud computing platforms have problems such as low resource utilization and poor energy efficiency management, and current energy efficiency research cannot guarantee the parallel application quality and efficiency. Therefore, this article proposes a green computing cloud platform model based on scalable orchestration, and studies the implementation of a polymorphic ant colony algorithm improved on consistency hashing algorithm, which improves the resource utilization of the cloud platform, reduces energy consumption, and realizes the green development of cloud data centers. On the one hand, based on the energy consumption analysis of cloud data centers, models for resource management, prediction, and elastic scaling have been proposed to achieve dynamic switch control of physical machines in cloud data centers, improving the effective resource utilization and energy efficiency management of cloud data centers; On the other hand, in response to the problem of poor performance caused by traditional data center load imbalance, a dynamic feedback mechanism is adopted to calculate the load rate of each node based on the physical machine CPU, memory and other information collected by cluster monitoring. Based on the resource scheduling strategy that integrates the consistent hash load balancing algorithm and the polymorphic ant colony scheduling algorithm, the balanced distribution of scalable scheduling tasks is achieved, improving the efficiency of resource balancing scheduling.

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