Implementation of User-End Broker Policy to Improve the Reliability of Cloud Services

2013 ◽  
Vol 3 (4) ◽  
pp. 13-27 ◽  
Author(s):  
Jitendra Singh ◽  
Vikas Kumar
Keyword(s):  
Data Center ◽  
Data Centers ◽  
Critical Issue ◽  
Cloud Services ◽  
Cloud Data Center ◽  
Cloud Data ◽  
Computing Services ◽  
Multiple Data ◽  
Single Data ◽  
Selection Of

Outage in cloud computing services is a critical issue and is primarily attributed to the single data center connectivity. To address the cloud outage, this work proposes a model for the subscription and selection of more than one data center. Selection of data center can be determined by the usage of broker at the user ends itself. Provision of broker at user's end reduces the overhead at provider's end; as a result performance of cloud data center improves. For the selection of appropriate data center, broker takes the feedback from the available data centers, and select one of them. During the selection of cloud, their status (up/down) at that particular time is also considered. In case of outage at one data center, other can be selected from the available list. Broker also facilitates the homogeneous use of cloud by allotting the load to less congested data centers. Experimental results revealed that multiple data center approach is not only helpful in countering the outage (as other data center can be selected from the broker) but also the usage cost.

scholarly journals Energy-efficient Scheduling of Cloud Data Center Servers

2019 ◽  
Vol 8 (11) ◽  
pp. 1769-1772
Keyword(s):  
Data Center ◽  
Large Scale ◽  
Data Centers ◽  
Large Data ◽  
Cost Effective ◽  
Energy Utilization ◽  
Operating Costs ◽  
Cloud Data Center ◽  
Cloud Data ◽  
Computing Services

Data center is a cost-effective infrastructure to store large data volumes and host large-scale service applications. Providers of cloud computing services are deploying data centers worldwide quickly. With lots of servers and switches. These data centers consume substantial quantities of energy, which contributes to high operating costs. Optimizing server and network energy consumption in information centers can therefore decrease operating costs. In a data center, power utilization is chiefly because of servers, network devices, and cooling systems, an effective energy-saving strategy is to consolidate computing and communication into fewer servers and network devices and then power off as many unneeded servers and network devices as possible. A new method of reducing the energy utilization of computer systems and networks in data centers while meeting the requirements of the cloud tenants for quality of service (QoS) is proposed here in this paper.

scholarly journals A Switch Based Resource Management Method for Energy Optimization in Cloud Data Center

2021 ◽  
pp. 85-91
Author(s):  
Shally Vats ◽  
Sanjay Kumar Sharma ◽  
Sunil Kumar
Keyword(s):  
Data Center ◽  
Data Centers ◽  
Energy Requirement ◽  
Energy Optimization ◽  
Cloud Service ◽  
Optimization Method ◽  
Level Energy ◽  
Cloud Data Center ◽  
Vm Migration ◽  
Cloud Data

Proliferation of large number of cloud users steered the exponential increase in number and size of the data centers. These data centers are energy hungry and put burden for cloud service provider in terms of electricity bills. There is environmental concern too, due to large carbon foot print. A lot of work has been done on reducing the energy requirement of data centers using optimal use of CPUs. Virtualization has been used as the core technology for optimal use of computing resources using VM migration. However, networking devices also contribute significantly to the responsible for the energy dissipation. We have proposed a two level energy optimization method for the data center to reduce energy consumption by keeping SLA. VM migration has been performed for optimal use of physical machines as well as switches used to connect physical machines in data center. Results of experiments conducted in CloudSim on PlanetLab data confirm superiority of the proposed method over existing methods using only single level optimization.

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.

scholarly journals An Energy-Efficient Task Scheduling Mechanism with Switching On/Sleep Mode of Servers in Virtualized Cloud Data Centers

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Chunxia Yin ◽  
Jian Liu ◽  
Shunfu Jin
Keyword(s):  
Energy Consumption ◽  
Task Scheduling ◽  
Data Center ◽  
Energy Efficient ◽  
Data Centers ◽  
Sleep Mode ◽  
Cloud Data Center ◽  
Cloud Data ◽  
Cloud Data Centers ◽  
Response Performance

In recent years, the energy consumption of cloud data centers has continued to increase. A large number of servers run at a low utilization rate, which results in a great waste of power. To save more energy in a cloud data center, we propose an energy-efficient task-scheduling mechanism with switching on/sleep mode of servers in the virtualized cloud data center. The key idea is that when the number of idle VMs reaches a specified threshold, the server with the most idle VMs will be switched to sleep mode after migrating all the running tasks to other servers. From the perspective of the total number of tasks and the number of servers in sleep mode in the system, we establish a two-dimensional Markov chain to analyse the proposed energy-efficient mechanism. By using the method of the matrix-geometric solution, we mathematically estimate the energy consumption and the response performance. Both numerical and simulated experiments show that our proposed energy-efficient mechanism can effectively reduce the energy consumption and guarantee the response performance. Finally, by constructing a cost function, the number of VMs hosted on each server is optimized.

Energy Efficient Load Balancing in Cloud Data Center Using Clustering Technique

2019 ◽  
Vol 15 (1) ◽  
pp. 84-100 ◽  
Author(s):  
N. Thilagavathi ◽  
D. Divya Dharani ◽  
R. Sasilekha ◽  
Vasundhara Suruliandi ◽  
V. Rhymend Uthariaraj
Keyword(s):  
Response Time ◽  
Energy Saving ◽  
Data Center ◽  
Data Centers ◽  
Virtual Machines ◽  
Cloud Data Center ◽  
Resource Requirement ◽  
Server Consolidation ◽  
Cloud Data ◽  
Initial Allocation

Cloud computing has seen tremendous growth in recent days. As a result of this, there has been a great increase in the growth of data centers all over the world. These data centers consume a lot of energy, resulting in high operating costs. The imbalance in load distribution among the servers in the data center results in increased energy consumption. Server consolidation can be handled by migrating all virtual machines in those underutilized servers. Migration causes performance degradation of the job, based on the migration time and number of migrations. Considering these aspects, the proposed clustering agent-based model improves energy saving by efficient allocation of the VMs to the hosting servers, which reduces the response time for initial allocation. Middle VM migration (MVM) strategy for server consolidation minimizes the number of VM migrations. Further, randomization of extra resource requirement done to cater to real-time scenarios needs more resource requirements than the initial requirement. Simulation results show that the proposed approach reduces the number of migrations and response time for user request and improves energy saving in the cloud environment.

scholarly journals Effectual Approach for Cloud Data Center Security using Metaheuristic based Global Optimization

2018 ◽  
Vol 1 (29) ◽  
pp. 490-497
Author(s):  
L.A Abdul Rasul A. AL WAILI
Keyword(s):  
Cloud Computing ◽  
High Performance ◽  
Data Centers ◽  
Cloud Services ◽  
Cloud Data Center ◽  
Cloud Data ◽  
Technology Products ◽  
Novel Approach ◽  
Formation Dynamics ◽  
River Formation Dynamics

Cloud Computing is emerging as one of the high performance and integrity aware area in the distributed and grid based computing environment. Enormous computing and technology based services are delivered and disseminated throughout the globe using cloud implementations because of increasing usage of technology products. As these products and devices are quite costly to purchase, cloud computing gives the option to hire the computing infrastructure with per usage base. As cloud computing is escalating by number of services, there are lots of issues regarding vulnerability and integrity in the data centers from where these cloud services are disseminated. This research manuscript presents and implements a unique and effectual approach for security of data centers using dynamic approach for encryption during communication and accessing the cloud services. The results in the projected novel approach are effective in terms of cost, complexity and overall performance. The projected novel approach is using nature inspired approach River formation dynamics Metaheuristic Approach for the enhancement of results and performance. Keywords – Cloud Computing, Cloud of Things, Nature Inspired Approach, Network Security, River formation dynamics Metaheuristic Approach

Resource clustering algorithm for cloud data centers

2021 ◽  
pp. 1-11
Author(s):  
Cail Song ◽  
Bin Liang ◽  
Jiao Li
Keyword(s):  
Energy Consumption ◽  
Data Center ◽  
Clustering Algorithm ◽  
Data Centers ◽  
Virtual Machines ◽  
Good Effect ◽  
Cloud Data Center ◽  
Cloud Data ◽  
Physical Machine ◽  
Cloud Data Centers

Recently, the virtual machine deployment algorithm uses physical machine less or consumes higher energy in data centers, resulting in declined service quality of cloud data centers or rising operational costs, which leads to a decrease in cloud service provider’s earnings finally. According to this situation, a resource clustering algorithm for cloud data centers is proposed. This algorithm systematically analyzes the cloud data center model and physical machine’s use ratio, establishes the dynamic resource clustering rules through k-means clustering algorithm, and deploys the virtual machines based on clustering results, so as to promote the use ratio of physical machine and bring down energy consumption in cloud data centers. The experimental results indicate that, regarding the compute-intensive virtual machines in cloud data centers, compared to contrast algorithm, the physical machine’s use ratio of this algorithm is improved by 12% on average, and its energy consumption in cloud data center is lowered by 15% on average. Regarding the general-purpose virtual machines in cloud data center, compared to contrast algorithm, the physical machine’s use ratio is improved by 14% on average, and its energy consumption in cloud data centers is lowered by 12% on average. Above results demonstrate that this method shows a good effect in the resource management of cloud data centers, which may provide reference to some extent.

scholarly journals SDN Based Energy Efficient Cloud Data Center Networks

2019 ◽  
Vol 8 (12) ◽  
pp. 4250-4256
Keyword(s):  
Energy Consumption ◽  
Data Center ◽  
Energy Efficient ◽  
Data Centers ◽  
Easy Access ◽  
Primary Concern ◽  
Data Center Networks ◽  
Cloud Data Center ◽  
Cloud Data ◽  
Cloud Data Centers

Cloud computing has led to the tremendous growth of IT organizations, which serves as the means of delivering services to large number of consumers globally, by providing anywhere, anytime easy access to resources and services. The primary concern over the increasing energy consumption by cloud data centers is mainly due to the massive emission of greenhouse gases, which contaminate the atmosphere and tend to worsen the environmental conditions. The major part of huge energy consumption comes from large servers, high speed storage devices and cooling equipment, present in cloud data centers. These serve as the basis for fulfilling the increasing need for computing resources. These in turn bestow additional cost of resources. The goal is to focus on energy savings through effective utilization of resources. This necessitates the need for developing a green-aware, energy-efficient framework for cloud data center networks. The Software Defined Networking (SDN) are chosen as they aid in studying the behaviour of networks from the overall perspective of software layer, rather than decisions from each individual device, as in case of conventional networks. The central objective of this paper is dedicated to survey on various existing SDN based energy efficient cloud data center networks.

Sign in / Sign up

Export Citation Format

Share Document