scholarly journals An Event Diminishment Model to Optimize Cloud Environment

2020 ◽  
Vol 9 (4) ◽  
pp. 744-749
Keyword(s):  
Sustainable Development ◽  
Cloud Computing ◽  
Data Centers ◽  
Scale Up ◽  
Critical Issue ◽  
It Services ◽  
Cloud Infrastructure ◽  
Cloud Data ◽  
Carbon Dioxide Gas ◽  
Cloud Data Centers

Cloud computing is to compute a task assigned to a set of connections, software and services that can be utilized by the user over a network. The trending need of Cloud infrastructure has drastically scale up the energy need of data centers, which has become a critical issue. In the row also lead to high carbon emission which is not environment friendly so there is a need of energy efficient approach in cloud computing The research paper aims to reach a theoretical notion of sustainable development with proposing an incentive for reducing global warming through effective clustering techniques and methods. This paper aims to reduce cloud events by applying map reduce on large event clusters formed in cloud. The purpose of the paper is to develop a better methodology for handling the events of cloud computing and possibly clustering and reducing the similar types of events. This approach might lead to the reduction of carbon-dioxide gas (which is a greenhouse gas) by less usage of servers in cloud data centers. With the advent of IT services in cloud computing energy consumption it is necessary for the developing technology to progress towards sustainable development rather thrashing and harnessing energy from every possible means.

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.

Energy-Efficiency in Cloud Data Centers

2013 ◽  
pp. 241-263
Author(s):  
Burak Kantarci ◽  
Hussein T. Mouftah
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Data Center ◽  
Energy Efficient ◽  
Large Scale ◽  
Data Centers ◽  
High Energy ◽  
It Services ◽  
Data Center Network ◽  
Cloud Data

Cloud computing aims to migrate IT services to distant data centers in order to reduce the dependency of the services on the limited local resources. Cloud computing provides access to distant computing resources via Web services while the end user is not aware of how the IT infrastructure is managed. Besides the novelties and advantages of cloud computing, deployment of a large number of servers and data centers introduces the challenge of high energy consumption. Additionally, transportation of IT services over the Internet backbone accumulates the energy consumption problem of the backbone infrastructure. In this chapter, the authors cover energy-efficient cloud computing studies in the data center involving various aspects such as: reduction of processing, storage, and data center network-related power consumption. They first provide a brief overview of the existing approaches on cool data centers that can be mainly grouped as studies on virtualization techniques, energy-efficient data center network design schemes, and studies that monitor the data center thermal activity by Wireless Sensor Networks (WSNs). The authors also present solutions that aim to reduce energy consumption in data centers by considering the communications aspects over the backbone of large-scale cloud systems.

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.

scholarly journals The Placement of Virtual Machines Under Optimal Conditions in Cloud Datacenter

2019 ◽  
Vol 48 (4) ◽  
pp. 545-556
Author(s):  
Sasan Gharehpasha ◽  
Mohammad Masdari ◽  
Ahmad Jafarian
Keyword(s):  
Cloud Computing ◽  
Data Centers ◽  
Virtual Machines ◽  
Optimal Placement ◽  
Ant Colony System ◽  
Optimal Conditions ◽  
Cloud Data ◽  
Business Goals ◽  
Virtualization Technology ◽  
Cloud Data Centers

Nowadays cloud computing is progressing very fast and has resulted in advances in other technologies too. Cloud computing provides quite a convenient platform for millions of users to use computing resources through the internet. Cloud computing provides the possibility of only concentrating on business goals instead of expanding hardware resources for users. Using virtualization technology in computing resources results in the efficient use of resources. A challenging work in virtualization technology is the placement of virtual machines under optimal conditions on physical machines in cloud data centers. Optimal placement of virtual machines on physical machines in cloud data centers can lead to the management of resources and prevention of the resources waste. In this paper, a new method is proffered based on the combination of hybrid discrete multi-object sine cosine algorithm and multi-verse optimizer for optimal placement. The first goal of the proposed approach is to decrease the power consumption which is consumed in cloud data centers by reducing active physical machines. The second goal is to cut in resource wastage and managing resources using the optimal placement of virtual machines on physical machines in cloud data centers. With this approach, the increasing rate of virtual migration to physical machines is prevented. Finally, the results gained from our proposed algorithm are compared to some algorithms like the first fit (FF), virtual machine placement ant colony system (VMPACS), modified best fit decreasing (MBFD).

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