◾ Simplifying Secure Cloud Computing Environments with Cloud Data Centers

2016 ◽  
pp. 409-422
Keyword(s):  
Cloud Computing ◽  
Data Centers ◽  
Cloud Data ◽  
Cloud Data Centers ◽  
Computing Environments ◽  
Secure Cloud Computing

Energy-Efficiency in Cloud Computing Environments

2013 ◽  
pp. 18-36 ◽  
Author(s):  
Ismael Solis Moreno ◽  
Jie Xu
Keyword(s):  
Cloud Computing ◽  
Energy Efficient ◽  
Data Centers ◽  
Energy Performance ◽  
Cloud Data ◽  
Cloud Data Centers ◽  
Computing Environments ◽  
Computational Resources ◽  
Efficient Mechanisms ◽  
Central Management

Due to all the pollutants generated during its production and the steady increases in its rates, energy consumption is causing serious environmental and economic problems. In this context, the growing use and adoption of ICTs is being highlighted not only as one as the principal problem sources but also as one of the principal areas that could help in the problem’s reduction. Cloud computing is an emerging model for distributed utility computing and is being considered as an attractive opportunity for saving energy through central management of computational resources. To be successful, the design of energy-efficient mechanisms must start playing a mayor role. This paper argues the importance of energy-efficient mechanisms within cloud data centers and remarks on the significance of the “energy-performance” relationship in boosting the adoption of these mechanisms in real scenarios. It provides an analysis of the current approaches and the outline of key opportunities that need to be addressed to improve the “energy-performance” relationship in this promising model.

Energy-Efficiency in Cloud Computing Environments

2012 ◽  
pp. 1943-1961 ◽  
Author(s):  
Ismael Solis Moreno ◽  
Jie Xu
Keyword(s):  
Cloud Computing ◽  
Energy Efficient ◽  
Data Centers ◽  
Energy Performance ◽  
Cloud Data ◽  
Cloud Data Centers ◽  
Computing Environments ◽  
Computational Resources ◽  
Efficient Mechanisms ◽  
Central Management

Due to all the pollutants generated during its production and the steady increases in its rates, energy consumption is causing serious environmental and economic problems. In this context, the growing use and adoption of ICTs is being highlighted not only as one as the principal problem sources but also as one of the principal areas that could help in the problem’s reduction. Cloud computing is an emerging model for distributed utility computing and is being considered as an attractive opportunity for saving energy through central management of computational resources. To be successful, the design of energy-efficient mechanisms must start playing a mayor role. This paper argues the importance of energy-efficient mechanisms within cloud data centers and remarks on the significance of the “energy-performance” relationship in boosting the adoption of these mechanisms in real scenarios. It provides an analysis of the current approaches and the outline of key opportunities that need to be addressed to improve the “energy-performance” relationship in this promising model.

Multidimensional Resource Scheduling in Cloud Data Centers

2014 ◽  
Vol 1008-1009 ◽  
pp. 1513-1516
Author(s):  
Hai Na Song ◽  
Xiao Qing Zhang ◽  
Zhong Tang He
Keyword(s):  
Cloud Computing ◽  
Data Centers ◽  
Bin Packing ◽  
Virtual Machines ◽  
Three Dimensional ◽  
Resource Scheduling ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Cloud Data ◽  
Cloud Data Centers

Cloud computing environment is regarded as a kind of multi-tenant computing mode. With virtulization as a support technology, cloud computing realizes the integration of multiple workloads in one server through the package and seperation of virtual machines. Aiming at the contradiction between the heterogeneous applications and uniform shared resource pool, using the idea of bin packing, the multidimensional resource scheduling problem is analyzed in this paper. We carry out some example analysis in one-dimensional resource scheduling, two-dimensional resource schduling and three-dimensional resource scheduling. The results shows that the resource utilization of cloud data centers will be improved greatly when the resource sheduling is conducted after reorganizing rationally the heterogeneous demands.

scholarly journals Multi-Objective Service Placement Scheme Based on Fuzzy-AHP System for Distributed Cloud Computing

2019 ◽  
Vol 9 (17) ◽  
pp. 3550 ◽  
Author(s):  
A-Young Son ◽  
Eui-Nam Huh
Keyword(s):  
Energy Efficiency ◽  
Cloud Computing ◽  
Response Time ◽  
Data Centers ◽  
Cloud Data ◽  
Multi Objective ◽  
Service Placement ◽  
Service Response Time ◽  
Cloud Data Centers ◽  
Distributed Cloud

With the rapid increase in the development of the cloud data centers, it is expected that massive data will be generated, which will decrease service response time for the cloud data centers. To improve the service response time, distributed cloud computing has been designed and researched for placement and migration from mobile devices close to edge servers that have secure resource computing. However, most of the related studies did not provide sufficient service efficiency for multi-objective factors such as energy efficiency, resource efficiency, and performance improvement. In addition, most of the existing approaches did not consider various metrics. Thus, to maximize energy efficiency, maximize performance, and reduce costs, we consider multi-metric factors by combining decision methods, according to user requirements. In order to satisfy the user’s requirements based on service, we propose an efficient service placement system named fuzzy- analytical hierarchical process and then analyze the metric that enables the decision and selection of a machine in the distributed cloud environment. Lastly, using different placement schemes, we demonstrate the performance of the proposed scheme.

Cloud Load Balancing Algorithms: A Comparative Assessment

2019 ◽  
Vol 16 (9) ◽  
pp. 3989-3994
Author(s):  
Jaspreet Singh ◽  
Deepali Gupta ◽  
Neha Sharma
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Data Centers ◽  
Research Area ◽  
Comparative Assessment ◽  
Cloud Services ◽  
Cloud Data ◽  
Cloud Data Centers ◽  
Cloud Users

Nowadays, Cloud computing is developing quickly and customers are requesting more administrations and superior outcomes. In the cloud domain, load balancing has turned into an extremely intriguing and crucial research area. Numbers of algorithms were recommended to give proficient mechanism for distributing the cloud user’s requests for accessing pool cloud resources. Also load balancing in cloud should provide notable functional benefits to cloud users and at the same time should prove out to be eminent for cloud services providers. In this paper, the pre-existing load balancing techniques are explored. The paper intends to provide landscape for classification of distinct load balancing algorithms based upon the several parameters and also address performance assessment bound to various load balancing algorithms. The comparative assessment of various load balancing algorithms will helps in proposing a competent load balancing technique for intensify the performance of cloud data centers.

scholarly journals Power consumption prediction in cloud data center using machine learning

2020 ◽  
Vol 10 (2) ◽  
pp. 1524 ◽  
Author(s):  
Deepika T. ◽  
Prakash P.
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Power Consumption ◽  
Data Center ◽  
Data Centers ◽  
Virtual Machines ◽  
Service Providers ◽  
Business World ◽  
Cloud Data ◽  
Cloud Data Centers

The flourishing development of the cloud computing paradigm provides several services in the industrial business world. Power consumption by cloud data centers is one of the crucial issues for service providers in the domain of cloud computing. Pursuant to the rapid technology enhancements in cloud environments and data centers augmentations, power utilization in data centers is expected to grow unabated. A diverse set of numerous connected devices, engaged with the ubiquitous cloud, results in unprecedented power utilization by the data centers, accompanied by increased carbon footprints. Nearly a million physical machines (PM) are running all over the data centers, along with (5 – 6) million virtual machines (VM). In the next five years, the power needs of this domain are expected to spiral up to 5% of global power production. The virtual machine power consumption reduction impacts the diminishing of the PM’s power, however further changing in power consumption of data center year by year, to aid the cloud vendors using prediction methods. The sudden fluctuation in power utilization will cause power outage in the cloud data centers. This paper aims to forecast the VM power consumption with the help of regressive predictive analysis, one of the Machine Learning (ML) techniques. The potency of this approach to make better predictions of future value, using Multi-layer Perceptron (MLP) regressor which provides 91% of accuracy during the prediction process.

Energy and Carbon Footprint-Aware Management of Geo-Distributed Cloud Data Centers

2017 ◽  
pp. 27-46 ◽  
Author(s):  
Atefeh Khosravi ◽  
Rajkumar Buyya
Keyword(s):  
Cloud Computing ◽  
Resource Management ◽  
Carbon Footprint ◽  
Data Centers ◽  
Target System ◽  
Cloud Data ◽  
Carbon Efficiency ◽  
Cloud Data Centers ◽  
Distributed Cloud ◽  
System Objective

Cloud computing provides on-demand access to computing resources for users across the world. It offers services on a pay-as-you-go model through data center sites that are scattered across diverse geographies. However, cloud data centers consume huge amount of electricity and leave high amount of carbon footprint in the ecosystem. This makes data centers responsible for 2% of the global CO2 emission. Therefore, having energy and carbon-efficient techniques for resource management in distributed cloud data centers is inevitable. This chapter presents a taxonomy and classifies the existing research works based on their target system, objective, and the technique they use for resource management in achieving a green cloud computing environment. Finally, it discusses how each work addresses the issue of energy and carbon-efficiency and also provides an insight into future directions.

scholarly journals Server consolidation: A technique to enhance cloud data center power efficiency and overall cost of ownership

2021 ◽  
Vol 17 (3) ◽  
pp. 155014772199721
Author(s):  
Mueen Uddin ◽  
Mohammed Hamdi ◽  
Abdullah Alghamdi ◽  
Mesfer Alrizq ◽  
Mohammad Sulleman Memon ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Data Center ◽  
Data Centers ◽  
Scheduling Algorithm ◽  
Cloud Data Center ◽  
Server Consolidation ◽  
Cloud Data ◽  
Task Scheduling Algorithm ◽  
Cloud Data Centers

Cloud computing is a well-known technology that provides flexible, efficient, and cost-effective information technology solutions for multinationals to offer improved and enhanced quality of business services to end-users. The cloud computing paradigm is instigated from grid and parallel computing models as it uses virtualization, server consolidation, utility computing, and other computing technologies and models for providing better information technology solutions for large-scale computational data centers. The recent intensifying computational demands from multinationals enterprises have motivated the magnification for large complicated cloud data centers to handle business, monetary, Internet, and commercial applications of different enterprises. A cloud data center encompasses thousands of millions of physical server machines arranged in racks along with network, storage, and other equipment that entails an extensive amount of power to process different processes and amenities required by business firms to run their business applications. This data center infrastructure leads to different challenges like enormous power consumption, underutilization of installed equipment especially physical server machines, CO2 emission causing global warming, and so on. In this article, we highlight the data center issues in the context of Pakistan where the data center industry is facing huge power deficits and shortcomings to fulfill the power demands to provide data and operational services to business enterprises. The research investigates these challenges and provides solutions to reduce the number of installed physical server machines and their related device equipment. In this article, we proposed server consolidation technique to increase the utilization of already existing server machines and their workloads by migrating them to virtual server machines to implement green energy-efficient cloud data centers. To achieve this objective, we also introduced a novel Virtualized Task Scheduling Algorithm to manage and properly distribute the physical server machine workloads onto virtual server machines. The results are generated from a case study performed in Pakistan where the proposed server consolidation technique and virtualized task scheduling algorithm are applied on a tier-level data center. The results obtained from the case study demonstrate that there are annual power savings of 23,600 W and overall cost savings of US$78,362. The results also highlight that the utilization ratio of already existing physical server machines has increased to 30% compared to 10%, whereas the number of server machines has reduced to 50% contributing enormously toward huge power savings.

Power Reduction Techniques in Cloud Data Centers

2014 ◽  
Vol 1061-1062 ◽  
pp. 1070-1073
Author(s):  
Lei Tang ◽  
Zheng Ce Cai ◽  
Guo Long Chen ◽  
Xian Wei Li
Keyword(s):  
Cloud Computing ◽  
Data Centers ◽  
Large Fraction ◽  
Power Reduction ◽  
Cloud Services ◽  
Total Power ◽  
Cloud Data ◽  
Reduction Techniques ◽  
Total Power Consumption ◽  
Cloud Data Centers

In recent years, cloud computing has received much attention from both academia and engineering areas. With more and more companies beginning to provide cloud services, more and more data centers are being built. Recent studies show that the energy consumed by cloud data centers accounts for a large fraction of the total power consumption today. This motivates us to survey power reduction techniques in cloud data centers.

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