scholarly journals POWER SAVING LOAD BALANCING STRATEGY USING DVFS IN CLOUD ENVIRONMENT

2016 ◽  
Vol 15 (13) ◽  
pp. 7333-7341 ◽  
Author(s):  
Sakshi Grover ◽  
Mr. Navtej Singh Ghumman
Keyword(s):  
Cloud Computing ◽  
Power Consumption ◽  
Virtual Machines ◽  
Research Work ◽  
Power Saving ◽  
Reducing Power ◽  
Total Power ◽  
Software Infrastructure ◽  
Other Information ◽  
Living Organisms

Cloud Computing is a technology that provides a platform for the sharing of resources such as software, infrastructure, application and other information. Cloud Computing is being used widely all over the world by many IT companies as it provides benefits to the users like cost saving and ease of use.  However with the growing demands of users for computing services, cloud providers are encouraged to deploy large datacenters which consume very high amount of energy resulting in carbon dioxide emissions.  Power consumption is a key concern in data centers. That type of critical issues not only reduces the profit margin, but also has effect on high carbon production which is harmful for environment and living organisms. Reducing power consumption has been an important requirement for cloud resource providers not only to reduce operating costs, but also to improve system reliability. In research work, we have arranged the virtual machines in ascending order of the load. Cloudlets would be assigned to that virtual machine that has lesser load. Cloudlets are divided into three categories like high, medium and low on the basis of their instruction length. Dvfs approach which has been implemented in the paper would scale the power according to the length of the cloudlets. Three modes of Dvfs have been implemented in the research work. Various parameters like processing time, processing cost and total power consumed by all the cloudlets at the data center have been computed and analyzed. Cloudsim a toolkit for modeling and simulation of cloud computing environment has been used to implement and demonstrate the experimental results.

scholarly journals A NOVEL APPROACH OF TASK CLASSIFICATION AND VM SKEWNESS IN CLOUD ENVIRONMENT

2017 ◽  
Vol 16 (7) ◽  
pp. 6994-7001
Author(s):  
Sukhbhinder Kaur ◽  
Mr. Navtej Singh Ghumman
Keyword(s):  
Cloud Computing ◽  
Virtual Machines ◽  
Electrical Power ◽  
Research Work ◽  
Power Saving ◽  
Ease Of Use ◽  
Software Infrastructure ◽  
Novel Approach ◽  
Task Classification ◽  
Other Information

Cloud Computing is a technology that provides a platform for sharing of resources such as software, infrastructure, application and other information. Cloud Computing is being used widely all over the world, as it provides benefits to the users like cost saving and ease of use. The research work focuses on the study of task scheduling mechanism in cloud. The main goal is to reduce the power consumption by datacenters. Energy efficient scheduling of workload help to reduce the consumption of energy in datacenters thus helps in better usage of resources. An improved power saving algorithm is proposed by combining the task classification along with VM skewness algorithm with different scaling options. Skewness is used to quantify the unevenness in utilization of multiple resources on the server. Our purposed algorithm calculate the skewness factor of all Virtual Machines and based upon its value. The proposed approach is performing and shows a decrease in response time, waiting time, processing cost and overall electrical power consumed. The study can be further extended by applying the proposed algorithm on actual Cloud Computing environment and we can also integrate various energy saving technologies into data centers to reduce energy consumption.

Distance Aware VM allocation process to minimize energy consumption in cloud computing

2019 ◽  
Vol 12 ◽  
Author(s):  
Gurpreet Singh ◽  
Manish Mahajan ◽  
Rajni Mohana
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Energy Consumption ◽  
Virtual Machine ◽  
Virtual Machines ◽  
Research Work ◽  
Service Level Agreement ◽  
Service Level ◽  
Physical Machine ◽  
Allocation Process

BACKGROUND: Cloud computing is considered as an on-demand service resource with the applications towards data center on pay per user basis. For allocating the resources appropriately for the satisfaction of user needs, an effective and reliable resource allocation method is required. Because of the enhanced user demand, the allocation of resources has now considered as a complex and challenging task when a physical machine is overloaded, Virtual Machines share its load by utilizing the physical machine resources. Previous studies lack in energy consumption and time management while keeping the Virtual Machine at the different server in turned on state. AIM AND OBJECTIVE: The main aim of this research work is to propose an effective resource allocation scheme for allocating the Virtual Machine from an ad hoc sub server with Virtual Machines. EXECUTION MODEL: The execution of the research has been carried out into two sections, initially, the location of Virtual Machines and Physical Machine with the server has been taken place and subsequently, the cross-validation of allocation is addressed. For the sorting of Virtual Machines, Modified Best Fit Decreasing algorithm is used and Multi-Machine Job Scheduling is used while the placement process of jobs to an appropriate host. Artificial Neural Network as a classifier, has allocated jobs to the hosts. Measures, viz. Service Level Agreement violation and energy consumption are considered and fruitful results have been obtained with a 37.7 of reduction in energy consumption and 15% improvement in Service Level Agreement violation.

scholarly journals SNMP for Cloud Environment Energy Efficiency

2021 ◽  
Author(s):  
Marta Chinnici ◽  
Asif Iqbal ◽  
ah lian kor ◽  
colin pattinson ◽  
eric rondeau
Keyword(s):  
Energy Efficiency ◽  
Cloud Computing ◽  
Power Consumption ◽  
Empirical Evidence ◽  
Rapid Growth ◽  
Virtual Machines ◽  
Cloud Environment ◽  
Second Stage ◽  
The Core ◽  
Snmp Protocol

Abstract Cloud computing has seen rapid growth and environments are now providing multiple physical servers with several virtual machines running on those servers. Networks have grown larger and have become more powerful in recent years. A vital problem related to this advancement is that it has become increasingly complex to manage networks. SNMP is one standard which is applied as a solution to this management of networks problem. This work utilizes SNMP to explore the capabilities of SNMP protocol and its features for monitoring, control and automation of virtual machines and hypervisors. For this target, a stage-wise solution has been formed that obtains results of experiments from the first stage uses SNMPv3 and feed to the second stage for further processing and advancement. The target of the controlling experiments is to explore the extent of SNMP capability in the control of virtual machines running in a hypervisor, also in terms of energy efficiency. The core contribution based on real experiments is conducted to provide empirical evidence for the relation between power consumption and virtual machines.

An Optimal Way of VM Placement Strategy in Cloud Computing Platform Using ABCS Algorithm

2021 ◽  
Vol 12 (3) ◽  
pp. 16-38
Author(s):  
Pushpa R. ◽  
M. Siddappa
Keyword(s):  
Cloud Computing ◽  
Power Consumption ◽  
Virtual Machines ◽  
Fitness Function ◽  
Vm Placement ◽  
Migration Strategy ◽  
Swarm Optimization ◽  
Bee Colony ◽  
Migration Cost ◽  
Chicken Swarm Optimization

In this paper, VM replacement strategy is developed using the optimization algorithm, namely artificial bee chicken swarm optimization (ABCSO), in cloud computing model. The ABCSO algorithm is the integration of the artificial bee colony (ABC) in chicken swarm optimization (CSO). This method employed VM placement based on the requirement of the VM for the completion of the particular task using the service provider. Initially, the cloud system is designed, and the proposed ABCSO-based VM placement approach is employed for handling the factors, such as load, CPU usage, memory, and power by moving the virtual machines optimally. The best VM migration strategy is determined using the fitness function by considering the factors, like migration cost, load, and power consumption. The proposed ABCSO method achieved a minimal load of 0.1688, minimal power consumption of 0.0419, and minimal migration cost of 0.0567, respectively.

scholarly journals Energy-Efficient Resource Provisioning Using Adaptive Harmony Search Algorithm for Compute-Intensive Workloads with Load Balancing in Datacenters

2020 ◽  
Vol 10 (7) ◽  
pp. 2323
Author(s):  
T. Renugadevi ◽  
K. Geetha ◽  
K. Muthukumar ◽  
Zong Woo Geem
Keyword(s):  
Energy Consumption ◽  
Load Balancing ◽  
Power Consumption ◽  
Virtual Machine ◽  
Virtual Machines ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Resource Provisioning ◽  
Total Power

Drastic variations in high-performance computing workloads lead to the commencement of large number of datacenters. To revolutionize themselves as green datacenters, these data centers are assured to reduce their energy consumption without compromising the performance. The energy consumption of the processor is considered as an important metric for power reduction in servers as it accounts to 60% of the total power consumption. In this research work, a power-aware algorithm (PA) and an adaptive harmony search algorithm (AHSA) are proposed for the placement of reserved virtual machines in the datacenters to reduce the power consumption of servers. Modification of the standard harmony search algorithm is inevitable to suit this specific problem with varying global search space in each allocation interval. A task distribution algorithm is also proposed to distribute and balance the workload among the servers to evade over-utilization of servers which is unique of its kind against traditional virtual machine consolidation approaches that intend to restrain the number of powered on servers to the minimum as possible. Different policies for overload host selection and virtual machine selection are discussed for load balancing. The observations endorse that the AHSA outperforms, and yields better results towards the objective than, the PA algorithm and the existing counterparts.

Power Consumption Saving of Chiller Water System for Semiconductor Factory in Taiwan

2011 ◽  
Vol 314-316 ◽  
pp. 1492-1501
Author(s):  
Ching Liang Chen ◽  
Yung Chung Chang
Keyword(s):  
Power Consumption ◽  
Water Supply ◽  
Water Temperature ◽  
Cooling Tower ◽  
Reducing Power ◽  
Total Power ◽  
Water Cooling ◽  
Cooling Temperature ◽  
Total Power Consumption ◽  
Temperature Water

Recently, the semiconductor manufacturing industry has exhibited not only fast growth, but intense power consumption. Consequently, reducing power consumption is critical for running reliability. A view of literature reveals that the power consumption of facility system is 56.6 % in the fabs. Among all facility systems, chiller plants are the largest energy users, consuming 27.2 % of the total power consumption. Therefore, saving power consumption for chiller plants involves a considerable economic benefit. In addition, cooling the water temperature further improves the efficiency of chillers. Hence, this report analyzes the optimal temperature between the chiller and cooling tower. Currently, controlling the chiller and cooling tower are separate processes, though, in fact, they should not be. This is because the water cooling temperature affects the efficiency of the chiller. Each reduced degree of the chiller condenser temperature reduces the electrical power by approximately 2 % in the cooling tower, in contrast to the chiller. Therefore, the optimal water cooling water temperature must be analyzed. The analysis method in this report is linear regression. First, determine the equations of power consumption for the chiller and cooling tower with variables representing the water cooling temperature, water supply temperature of the chiller, and outdoor loading and wet-bulb temperatures. Second, add the coefficient of the same variable to obtain the total power consumption equation for the chiller and cooling tower. The result shows the relationships of power consumption with water cooling temperature under identical conditions of the water cooling temperature, water supply temperature of chiller, and outdoor loading and wet-bulb temperatures. Finally, use the differential method to determine the optimal water cooling temperature.

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 Design of Low Power Transceiver on Spartan-3 and Spartan-6 FPGA

2019 ◽  
Vol 8 (12S2) ◽  
pp. 27-30
Keyword(s):  
Power Consumption ◽  
Low Power ◽  
Power Dissipation ◽  
Power Analysis ◽  
Research Work ◽  
Leakage Power ◽  
Total Power ◽  
Voltage Supply ◽  
Field Programmable ◽  
Eda Tools

In this research work, a low power transceiver is designed using Spartan-3 and Spartan-6 Field-Programmable Gate Array (FPGA). In this work, a Universal Asynchronous Receiver Transmitter (UART) device is used as a transceiver. The implementation of UART is possible with EDA tools called Xilinx 14.1 and the results of the power analysis are targeted on Spartan-3 and Spartan-6 FPGA. The variation of different power of chips that are fabricated on FPGA for e.g., Input/Output (I/O) power consumption, Leakage power dissipation, Signal power utilization, Logic power usage, and the use of Total power, is observed by changing the voltage supply. This research work shows how the change in voltage influence the power consumption of UART on Spartan-3 and Spartan-6 FPGA devices. It is observed that Spartan-6 is found to be more powerefficient as voltage supply increases.

Data Center Enhancement by Server Resources Utilization

2021 ◽  
Vol 39 (1B) ◽  
pp. 203-208
Author(s):  
Haider A. Ghanem ◽  
Rana F. Ghani ◽  
Maha J. Abbas
Keyword(s):  
Cloud Computing ◽  
Power Consumption ◽  
Resource Utilization ◽  
Virtual Machine ◽  
Data Center ◽  
Data Centers ◽  
Virtual Machines ◽  
The Internet ◽  
Memory Resources

Data centers are the main nerve of the Internet because of its hosting, storage, cloud computing and other services. All these services require a lot of work and resources, such as energy and cooling. The main problem is how to improve the work of data centers through increased resource utilization by using virtual host simulations and exploiting all server resources. In this paper, we have considered memory resources, where Virtual machines were distributed to hosts after comparing the virtual machines with the host from where the memory and putting the virtual machine on the appropriate host, this will reduce the host machines in the data centers and this will improve the performance of the data centers, in terms of power consumption and the number of servers used and cost.

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