• Title/Summary/Keyword: green cloud computing

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A Case Study of Green Ambience through Green Cloud Computing

  • Kumar, Rethina;Kang, Jeong-Jin
    • International journal of advanced smart convergence
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    • v.1 no.2
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    • pp.52-58
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    • 2012
  • Green cloud computing refers to the green ambient benefits that information technology services delivered over the Internet can offer for the society. The green meaning environment friendly and cloud computing is a traditional symbol for the Internet and a type of service provider. Cloud computing has drastically increased the number of datacenters and the energy consumption of data centers and that has become a critical issue which is extremely important in green ambience. These days the cloud data center needs high energy resources that leads to high operational cost and also maximizes CO2 - carbon footprint that pollutes the ambience which is not to be considered as green ambience. So we need to provide a way that leads us to green ambience. Cloud computing for the green ambience should be designed in a way which will utilize less energy resources and to minimize the CO2 -carbon footprint, known as green cloud. In this paper we discuss various elements of Clouds which contributes to minimize the total energy consumption and the carbon emission so as to enable green ambience through green cloud computing.

A Novel Architecture for Mobile Crowd and Cloud computing for Health care

  • kumar, Rethina;Ganapathy, Gopinath;Kang, Jeong-Jin
    • International Journal of Advanced Culture Technology
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    • v.6 no.4
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    • pp.226-232
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    • 2018
  • The rapid pace of growth in internet usage and rich mobile applications and with the advantage of incredible usage of internet enabled mobile devices the Green Mobile Crowd Computing will be the suitable area to research combining with cloud services architecture. Our proposed Framework will deploy the eHealth among various health care sectors and pave a way to create a Green Mobile Application to provide a better and secured way to access the Products/ Information/ Knowledge, eHealth services, experts / doctors globally. This green mobile crowd computing and cloud architecture for healthcare information systems are expected to lower costs, improve efficiency and reduce error by also providing better consumer care and service with great transparency to the patient universally in the field of medical health information technology. Here we introduced novel architecture to use of cloud services with crowd sourcing.

Energy Efficient Software Development Techniques for Cloud based Applications

  • Aeshah A. Alsayyah;Shakeel Ahmed
    • International Journal of Computer Science & Network Security
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    • v.23 no.7
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    • pp.119-130
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    • 2023
  • Worldwide organizations use the benefits offered by Cloud Computing (CC) to store data, software and programs. While running hugely complicated and sophisticated software on cloud requires more energy that causes global warming and affects environment. Most of the time energy consumption is wasted and it is required to explore opportunities to reduce emission of carbon in CC environment to save energy. Many improvements can be done in regard to energy efficiency from the software perspective by considering and paying attention on the energy consumption aspects of software's that run on cloud infrastructure. The aim of the current research is to propose a framework with an additional phase called parameterized development phase to be incorporated along with the traditional Software Development Life cycle (SDLC) where the developers need to consider the suggested techniques during software implementation to utilize low energy for running software on the cloud and contribute in green computing. Experiments have been carried out and the results prove that the suggested techniques and methods has enabled in achieving energy consumption.

Applying Workload Shaping Toward Green Cloud Computing

  • Kim, Woongsup
    • International journal of advanced smart convergence
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    • v.1 no.2
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    • pp.12-15
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    • 2012
  • Energy costs for operating and cooling computing resources in Cloud infrastructure have increased significantly up to the point where they would surpass the hardware purchasing costs. Thus, reducing the energy consumption can save a significant amount of management cost. One of major approach is removing hardware over-provisioning. In this paper, we propose a technique that facilitates power saving through reducing resource over provisioning based on virtualization technology. To this end, we use dynamic workload shaping to reschedule and redistribute job requests considering overall power consumption. In this paper, we present our approach to shape workloads dynamically and distribute them on virtual machines and physical machines through virtualization technology. We generated synthetic workload data and evaluated it in simulating and real implementation. Our simulated results demonstrate our approach outperforms to when not using no workload shaping methodology.

Privacy-preserving and Communication-efficient Convolutional Neural Network Prediction Framework in Mobile Cloud Computing

  • Bai, Yanan;Feng, Yong;Wu, Wenyuan
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.15 no.12
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    • pp.4345-4363
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    • 2021
  • Deep Learning as a Service (DLaaS), utilizing the cloud-based deep neural network models to provide customer prediction services, has been widely deployed on mobile cloud computing (MCC). Such services raise privacy concerns since customers need to send private data to untrusted service providers. In this paper, we devote ourselves to building an efficient protocol to classify users' images using the convolutional neural network (CNN) model trained and held by the server, while keeping both parties' data secure. Most previous solutions commonly employ homomorphic encryption schemes based on Ring Learning with Errors (RLWE) hardness or two-party secure computation protocols to achieve it. However, they have limitations on large communication overheads and costs in MCC. To address this issue, we present LeHE4SCNN, a scalable privacy-preserving and communication-efficient framework for CNN-based DLaaS. Firstly, we design a novel low-expansion rate homomorphic encryption scheme with packing and unpacking methods (LeHE). It supports fast homomorphic operations such as vector-matrix multiplication and addition. Then we propose a secure prediction framework for CNN. It employs the LeHE scheme to compute linear layers while exploiting the data shuffling technique to perform non-linear operations. Finally, we implement and evaluate LeHE4SCNN with various CNN models on a real-world dataset. Experimental results demonstrate the effectiveness and superiority of the LeHE4SCNN framework in terms of response time, usage cost, and communication overhead compared to the state-of-the-art methods in the mobile cloud computing environment.

A Hybrid Cloud Testing System Based on Virtual Machines and Networks

  • Chen, Jing;Yan, Honghua;Wang, Chunxiao;Liu, Xuyan
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.14 no.4
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    • pp.1520-1542
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    • 2020
  • Traditional software testing typically uses many physical resources to manually build various test environments, resulting in high resource costs and long test time due to limited resources, especially for small enterprises. Cloud computing can provide sufficient low-cost virtual resources to alleviate these problems through the virtualization of physical resources. However, the provision of various test environments and services for implementing software testing rapidly and conveniently based on cloud computing is challenging. This paper proposes a multilayer cloud testing model based on cloud computing and implements a hybrid cloud testing system based on virtual machines (VMs) and networks. This system realizes the automatic and rapid creation of test environments and the remote use of test tools and test services. We conduct experiments on this system and evaluate its applicability in terms of the VM provision time, VM performance and virtual network performance. The experimental results demonstrate that the performance of the VMs and virtual networks is satisfactory and that this system can improve the test efficiency and reduce test costs through rapid virtual resource provision and convenient test services.

Energy Consumption and Reliable Communications for Green IoT

  • Singh, Saurabh;Moon, Seo Yeon;Yi, Gangman;Park, Jong Hyuk
    • Proceedings of the Korea Information Processing Society Conference
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    • 2016.10a
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    • pp.309-312
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    • 2016
  • Green Internet of Things (IoT) is the study and practice of eco-friendly sustainable computing. The basic goal of green computing is to reduce the use of materials and maximize energy efficiency with reliable and secure communications. The paper presents various technologies and issues regarding green IoT. It also studies the green Information and Communication Technology (ICT) such as green M2M, green Cloud Computing (CC), and green Data Center (DC). In addition, this paper mentions about the reliability in IoT Communication and and issues to achieve green IoT communication by applying efficient activity scheduling technique for energy saving. Finally, we propose the green IoT-Home Service (GIHS) model which provides efficient energy management in home automation system.

Goal-driven Optimization Strategy for Energy and Performance-Aware Data Centers for Cloud-Based Wind Farm CMS

  • Elijorde, Frank;Kim, Sungho;Lee, Jaewan
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.10 no.3
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    • pp.1362-1376
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    • 2016
  • A cloud computing system can be characterized by the provision of resources in the form of services to third parties on a leased, usage-based basis, as well as the private infrastructures maintained and utilized by individual organizations. To attain the desired reliability and energy efficiency in a cloud data center, trade-offs need to be carried out between system performance and power consumption. Resolving these conflicting goals is often the major challenge encountered in the design of optimization strategies for cloud data centers. The work presented in this paper is directed towards the development of an Energy-efficient and Performance-aware Cloud System equipped with strategies for dynamic switching of optimization approach. Moreover, a platform is also provided for the deployment of a Wind Farm CMS (Condition Monitoring System) which allows ubiquitous access. Due to the geographically-dispersed nature of wind farms, the CMS can take advantage of the cloud's highly scalable architecture in order to keep a reliable and efficient operation capable of handling multiple simultaneous users and huge amount of monitoring data. Using the proposed cloud architecture, a Wind Farm CMS is deployed in a virtual platform to monitor and evaluate the aging conditions of the turbine's major components in concurrent, yet isolated working environments.

CloudSwitch: A State-aware Monitoring Strategy Towards Energy-efficient and Performance-aware Cloud Data Centers

  • Elijorde, Frank;Lee, Jaewan
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.9 no.12
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    • pp.4759-4775
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    • 2015
  • The reduction of power consumption in large-scale datacenters is highly-dependent on the use of virtualization to consolidate multiple workloads. However, these consolidation strategies must also take into account additional important parameters such as performance, reliability, and profitability. Resolving these conflicting goals is often the major challenge encountered in the design of optimization strategies for cloud data centers. In this paper, we put forward a data center monitoring strategy which dynamically alters its approach depending on the cloud system's current state. Results show that our proposed scheme outperformed strategies which only focus on a single metric such as SLA-Awareness and Energy Efficiency.

Efficient Resource Recommendation System for Cloud Market Computing (클라우드 마켓 컴퓨팅을 위한 효율적인 리소스 추천시스템)

  • Han, Seung-Min;Huh, Eui-Nam;Youn, Chang-Woo
    • Journal of Internet Computing and Services
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    • v.11 no.3
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    • pp.121-129
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    • 2010
  • In recent years, Cloud computing is gaining much popularity as it can efficiently utilize the computing resources and hence can contribute to the issue of green IT. So to make the Cloud services commercialized, Cloud markets are necessary and are being developed. As the increasing numbers of various Cloud services are rapidly evolving in the Cloud market, how to select the best and optimal services will be a great challenge. In this paper we present a Cloud service selection framework in the Cloud market that uses a recommender system (RS) which helps a user to select the best services from different Cloud providers (CP) that matches his/her requirements. The RS recommends a service based on the QoS and Virtual Machine (VM) factors of difference CPs. The experimental results show that our Cloud service recommender system (CSRS) can effectively recommend a good combination of Cloud services to consumers.