scholarly journals PE-DCA: Penalty elimination based data center allocation technique using guided local search for IaaS cloud

2021 ◽  
pp. 59-59
Author(s):  
Sasmita Parida ◽  
Bibudhendu Pati ◽  
Suvendu Nayak ◽  
Chhabi Panigrahi ◽  
Tien-Hsiung Weng
Keyword(s):  
Cloud Computing ◽  
Local Search ◽  
Data Center ◽  
Processing Time ◽  
Data Centers ◽  
Iterative Technique ◽  
Guided Local Search ◽  
After Effects ◽  
Time Parameters ◽  
The Cost

In Cloud computing the user requests are passaged to data centers (DCs) to accommodate resources. It is essential to select the suitable DCs as per the user requests so that other requests should not be penalized in terms of time and cost. The searching strategies consider the execution time rather than the related penalties while searching DCs. In this work, we discuss Penalty Elimination-based DC Allocation (PE-DCA) using Guided Local Search (GLS) mechanism to locate suitable DCs with reduced cost, response time, and processing time. The PE-DCA addresses, computes, and eliminates the penalties involved in the cost and time through iterative technique using the defined objective and guide functions. The PE-DCA is implemented using CloudAnalyst with various configurations of user requests and DCs. We examine the PE-DCA and the execution after-effects of various costs and time parameters to eliminate the penalties and observe that the proposed mechanism performs best.

Minimization of Energy Using Heuristic Resource Allocation and Migration for Cloud Computing

2021 ◽  
Vol 12 (1) ◽  
pp. 74-83
Author(s):  
Manjunatha S. ◽  
Suresh L.
Keyword(s):  
Cloud Computing ◽  
Data Center ◽  
Large Scale ◽  
Data Centers ◽  
Service Providers ◽  
Cost Effective ◽  
Cooling Systems ◽  
Operational Costs ◽  
Cloud Computing Service ◽  
And Migration

Data center is a cost-effective infrastructure for storing large volumes of data and hosting large-scale service applications. Cloud computing service providers are rapidly deploying data centers across the world with a huge number of servers and switches. These data centers consume significant amounts of energy, contributing to high operational costs. Thus, optimizing the energy consumption of servers and networks in data centers can reduce operational costs. In a data center, power consumption is mainly due to servers, networking devices, and cooling systems, and an effective energy-saving strategy is to consolidate the computation and communication into a smaller number of servers and network devices and then power off as many unneeded servers and network devices as possible.

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.

Location and Provisioning Problems in Cloud Computing Networks

2013 ◽  
pp. 22-45 ◽  
Author(s):  
Federico Larumbe ◽  
Brunilde Sansò
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Virtual Machine ◽  
Data Centers ◽  
Optimization Problems ◽  
Virtual Machine Placement ◽  
Future Research ◽  
Network Efficiency ◽  
Placement Problem ◽  
The Cost

This chapter addresses a set of optimization problems that arise in cloud computing regarding the location and resource allocation of the cloud computing entities: the data centers, servers, software components, and virtual machines. The first problem is the location of new data centers and the selection of current ones since those decisions have a major impact on the network efficiency, energy consumption, Capital Expenditures (CAPEX), Operational Expenditures (OPEX), and pollution. The chapter also addresses the Virtual Machine Placement Problem: which server should host which virtual machine. The number of servers used, the cost, and energy consumption depend strongly on those decisions. Network traffic between VMs and users, and between VMs themselves, is also an important factor in the Virtual Machine Placement Problem. The third problem presented in this chapter is the dynamic provisioning of VMs to clusters, or auto scaling, to minimize the cost and energy consumption while satisfying the Service Level Agreements (SLAs). This important feature of cloud computing requires predictive models that precisely anticipate workload dimensions. For each problem, the authors describe and analyze models that have been proposed in the literature and in the industry, explain advantages and disadvantages, and present challenging future research directions.

Introducing Energy Efficiency Metrics for Server and Data Centers

2011 ◽  
Author(s):  
Abdlmonem H. Beitelmal ◽  
Drazen Fabris
Keyword(s):  
Energy Efficiency ◽  
Data Center ◽  
Data Centers ◽  
Cooling Efficiency ◽  
Idle Power ◽  
Relevant Variables ◽  
The Cost ◽  
Evaluation Of Data

New servers and data center metrics are introduced to facilitate proper evaluation of data centers power and cooling efficiency. These metrics will be used to help reduce the cost of operation and to provision data centers cooling resources. The most relevant variables for these metrics are identified and they are: the total facility power, the servers’ idle power, the average servers’ utilization, the cooling resources power and the total IT equipment power. These metrics can be used to characterize and classify servers and data centers performance and energy efficiency regardless of their size and location.

Improving Cost for Data Migration in Cloud Computing Using Genetic Algorithm

2020 ◽  
Vol 8 (3) ◽  
pp. 69-81
Author(s):  
Nitin Chawla ◽  
Deepak Kumar ◽  
Dinesh Kumar Sharma
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Data Center ◽  
Data Centers ◽  
Software As A Service ◽  
Data Migration ◽  
Platform As A Service ◽  
Challenging Tasks ◽  
Enterprise Applications ◽  
Client Data

Cloud computing is gradually increasing its popularity in enterprise-wide organizations. Information technology organizations e.g., IBM, Microsoft, and Amazon have already shifted towards Cloud computing. Cloud-based offerings such as Software as a Service, Platform as a Service and Infrastructure as a Service (IAAS) are the most famous offerings. Most of the existing enterprise applications are deployed using an on-premise model. Organizations are looking for Cloud based offerings to deploy or upgrade their existing applications. SAP, Microsoft Dynamics, and Oracle are the most famous ERP or CRM application OEMs. These enterprise applications generate lots of data are hosted in an organization or on client data centers. Moving data from one data center to the Cloud is always a challenging tasks which cost a lot and takes much effort. This study proposes an efficient approach to optimize cost for data migration in cloud computing. This study also proposes the approach to optimize cost for data collection from multiple locations which can be processed centrally and then migrate to Cloud Computing.

Analysis and Research of Cloud Computing Data Center

2013 ◽  
Vol 427-429 ◽  
pp. 2184-2187
Author(s):  
Le Jiang Guo ◽  
Feng Zheng ◽  
Ya Hui Hu ◽  
Lei Xiao ◽  
Liang Liu
Keyword(s):  
Cloud Computing ◽  
Data Center ◽  
Data Centers ◽  
Computing Technologies ◽  
Data Center Management ◽  
Computing Centers ◽  
The Relationship

Cloud computing data centers can be called cloud computing centers. It has put forward newer and higher demands for data centers with the development of cloud computing technologies. This paper will discuss what are cloud computing data centers, cloud computing data center construction, cloud computing data center architecture, cloud computing data center management and maintenance, and the relationship between cloud computing data centers and clouds.

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.

scholarly journals Increasing Security for Cloud Computing By Steganography in Image Edges

2017 ◽  
Vol 27 (4) ◽  
Author(s):  
Hassan Hadi Saleh
Keyword(s):  
Cloud Computing ◽  
Data Storage ◽  
Data Security ◽  
Data Center ◽  
Data Centers ◽  
Data Access ◽  
Good Method ◽  
Color Images ◽  
Image Edges ◽  
Fundamental Objective

The security of data storage in “cloud” is big challenge because the data keep within resources that may be accessed by particular machines. The managing of these data and services may not be high reliable. Therefore, the security of data is highly challenging. To increase the security of data in data center of cloud, we have introduced good method to ensure data security in “cloud computing” by methods of data hiding using color images which is called steganography. The fundamental objective of this paper is to prevent "Data Access” by unauthorized or opponent users. This scheme stores data at data centers within edges of color images and retrieves data from it when it is wanted.

scholarly journals Proposing a load balancing algorithm with an integrative approach to reduce response time and service process time in data centers

2019 ◽  
Vol 16 (4) ◽  
pp. 627-637
Author(s):  
Sanaz Hosseinzadeh Sabeti ◽  
Maryam Mollabgher
Keyword(s):  
Load Balancing ◽  
Data Processing ◽  
Response Time ◽  
Virtual Machine ◽  
Data Center ◽  
Processing Time ◽  
Data Centers ◽  
Virtual Machines ◽  
Overall Response ◽  
Load Balancing Algorithm

Goal: Load balancing policies often map workloads on virtual machines, and are being sought to achieve their goals by creating an almost equal level of workload on any virtual machine. In this research, a hybrid load balancing algorithm is proposed with the aim of reducing response time and processing time. Design / Methodology / Approach: The proposed algorithm performs load balancing using a table including the status indicators of virtual machines and the task list allocated to each virtual machine. The evaluation results of response time and processing time in data centers from four algorithms, ESCE, Throttled, Round Robin and the proposed algorithm is done. Results: The overall response time and data processing time in the proposed algorithm data center are shorter than other algorithms and improve the response time and data processing time in the data center. The results of the overall response time for all algorithms show that the response time of the proposed algorithm is 12.28%, compared to the Round Robin algorithm, 9.1% compared to the Throttled algorithm, and 4.86% of the ESCE algorithm. Limitations of the investigation: Due to time and technical limitations, load balancing has not been achieved with more goals, such as lowering costs and increasing productivity. Practical implications: The implementation of a hybrid load factor policy can improve the response time and processing time. The use of load balancing will cause the traffic load between virtual machines to be properly distributed and prevent bottlenecks. This will be effective in increasing customer responsiveness. And finally, improving response time increases the satisfaction of cloud users and increases the productivity of computing resources. Originality/Value: This research can be effective in optimizing the existing algorithms and will take a step towards further research in this regard.

scholarly journals Dashboard for Assessing Data Centers Green Compliance

2013 ◽  
Vol 6 (1) ◽  
pp. 719-726
Author(s):  
Fatemeh Binesh ◽  
Saravanan Muthaiyah
Keyword(s):  
Cloud Computing ◽  
Waste Management ◽  
Data Center ◽  
Data Centers ◽  
Environmental Hazard ◽  
The Internet ◽  
Compliance Level ◽  
The World ◽  
New Research ◽  
Near Future

Abstract: Nowadays, ICT sector activities and in particular Data Centers are known as an important environmental hazard. With the increasing popularity of the Internet and cloud computing, this threat seems to even get worse in the near future. Despite this increasing importance, there is still little have been done about data centers environmental affects and in particular measuring their green compliance level including all three Rs of waste management (Reuse, Reuse and Recycle). This paper tries to introduce a dashboard for evaluating data centers level of green compliance regardless of their tier. However, the dashboard is proposed based on Malaysias data centers condition, it still can be beneficial to data center managers in other parts of the world and researchers to open up new research possibilities.  

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