scholarly journals High Efficient Virtual Machine Migration Using Glow Worm Swarm Optimization Method for Cloud Computing

2021 ◽  
Vol 26 (6) ◽  
pp. 591-597
Author(s):  
Annabathula Phani Sheetal ◽  
Kongara Ravindranath
Keyword(s):  
Cloud Computing ◽  
Power Consumption ◽  
Virtual Machine ◽  
Fitness Function ◽  
Total Power ◽  
Optimum Number ◽  
Vm Placement ◽  
Swarm Optimization ◽  
Total Power Consumption ◽  
High Efficient

In this paper, high efficient Virtual Machine (VM) migration using GSO algorithm for cloud computing is proposed. This algorithm contains 3 phases: (i) VM selection, (ii) optimum number of VMs selection, (iii) VM placement. In VM selection phase, VMs to be migrated are selected based on their resource utilization and fault probability. In phase-2, optimum number of VMs to be migrated are determined based on the total power consumption. In VM placement phase, Glowworm Swarm Optimization (GSO) is used for finding the target VMs to place the migrated VMs. The fitness function is derived in terms of distance between the main server and the other server, VM capacity and memory size. Then the VMs with best fitness functions are selected as target VMs for placing the migrated VMs. The proposed algorithms are implemented in Cloudsim and performance results show that PEVM-GSO algorithm attains reduced power consumption and response delay with improved CPU utilization.

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.

A Fast Optimization Approach for Multi-Pass Wire Drawing Processes with a Penalty Fitness Function

2013 ◽  
Vol 762 ◽  
pp. 307-312
Author(s):  
Guang Liang Zhang ◽  
Zhang Wei Wang ◽  
Shi Hong Zhang
Keyword(s):  
Power Consumption ◽  
Fitness Function ◽  
Computational Cost ◽  
Wire Drawing ◽  
Total Power ◽  
Optimization Approach ◽  
Multi Objective Genetic Algorithm ◽  
Total Power Consumption ◽  
Reference Design ◽  
Fast Optimization

A fast optimization approach is demonstrated for design optimization of the multi-pass wire drawing process with the multi-objective genetic algorithm, and with the aims at minimizing both power consumption and temperature, via optimizing the process parameters involving pass number, pass schedule, die angle, bearing length and loops on capstan etc. A jump fitness function and a penalty fitness function are proposed for the survival of good designs and killing the bad designs which temperature, die wear factor, delta factor, or ratio of drawing stress to yield stress exceed the limits during optimization. The numerical examples show that the optimizer with the penalty fitness function, when its parameternranges from 1 to 2, presents the best performance in finding the minimum power consumption with a limit in temperature. Compared with a reference design, a significant reduction in the total power consumption about 300W, with the well control in temperature, delta factor and die life, has been achieved by the optimization. The penalty fitness function presents the better performance in the reduction of the iteration generations and computational cost to the jump fitness function.

scholarly journals Power Management as a Service (Pmaas) – An Energy Saving Service by ―Auto-Fit VM Placement‖ Algorithm in Cloud Computing for Maximum Resource Utilization at Minimum Energy Consumption Without Live-Migrating the Virtual Machines by using the Concept of Virtual-Servers

2019 ◽  
Vol 9 (2) ◽  
pp. 1437-1446
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Power Consumption ◽  
Power Management ◽  
Resource Utilization ◽  
Virtual Machine ◽  
Virtual Machines ◽  
Memory Storage ◽  
Vm Placement ◽  
Placement Algorithm

Now a day Energy Consumption is one of the most promising fields amongst several computing services of cloud computing. A maximum amount of Power resources are absorbed by the data centre because of huge amount of data processing which is increased abnormally. So it’s the time to think about the energy consumption in cloud environment. Existing Energy Consumption systems are limited in terms of virtualization because improper virtualization leads to loads imbalance and excessive power consumption and inefficiency in terms of computational power. Billing[1,2 ] is another exciting feature that is closely related to energy consumption, because higher or lesser billing depends on energy consumption somehow-as we know that cloud providers allow cloud users to access resources as pay-per-use, so these resources need to be optimally selected to process the user request to maximize user satisfaction in the distributed virtualized environment. There may be an inequity between the actual power consumption by the users and the provided billing records by the providers, So any false accusation that may claimed by each other to get illegal compensations. To avoid such accusation, we propose a work to consolidate the VMs using the Power Management as a Service (PMaaS) model in such a way, to reduce power consumption by maximum resource utilization without live-migration of the virtual machines by using the concept of Virtual Servers. The proposed PMaaS model uses a new “Auto-fit VM placement algorithm”, which computes tasks resource demands, models a Virtual Machine that fits those demands, and places the Virtual Machines on a Virtual server made by the collective resources (CPU, Memory, Storage and Bandwidth) from the respective schedulers directly connected to the actual physical servers and that has the minimum remaining resources which is large enough to accommodate such a Virtual Machine.

scholarly journals Efficient Operation Method of Aquifer Thermal Energy Storage System Using Demand Response

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3129
Author(s):  
Jewon Oh ◽  
Daisuke Sumiyoshi ◽  
Masatoshi Nishioka ◽  
Hyunbae Kim
Keyword(s):  
Energy Storage ◽  
Power Consumption ◽  
Thermal Energy ◽  
Demand Response ◽  
Thermal Energy Storage ◽  
Heat Storage ◽  
Storage System ◽  
Total Power ◽  
Operation Method ◽  
Total Power Consumption

The mass introduction of renewable energy is essential to reduce carbon dioxide emissions. We examined an operation method that combines the surplus energy of photovoltaic power generation using demand response (DR), which recognizes the balance between power supply and demand, with an aquifer heat storage system. In the case that predicts the occurrence of DR and performs DR storage and heat dissipation operation, the result was an operation that can suppress daytime power consumption without increasing total power consumption. Case 1-2, which performs nighttime heat storage operation for about 6 h, has become an operation that suppresses daytime power consumption by more than 60%. Furthermore, the increase in total power consumption was suppressed by combining DR heat storage operation. The long night heat storage operation did not use up the heat storage amount. Therefore, it is recommended to the heat storage operation at night as much as possible before DR occurs. In the target area of this study, the underground temperature was 19.1 °C, the room temperature during cooling was about 25 °C and groundwater could be used as the heat source. The aquifer thermal energy storage (ATES) system in this study uses three wells, and consists of a well that pumps groundwater, a heat storage well that stores heat and a well that used heat and then returns it. Care must be taken using such an operation method depending on the layer configuration.

scholarly journals A Novel Differential Log-Companding Amplifier for Biosignal Sensing

2016 ◽  
Vol 2016 ◽  
pp. 1-7
Author(s):  
Zigang Dong ◽  
Xiaolin Zhou ◽  
Yuanting Zhang
Keyword(s):  
Power Consumption ◽  
Supply Voltage ◽  
Nonlinear Function ◽  
New Method ◽  
Total Power ◽  
Maximum Output ◽  
Output Current ◽  
Current Range ◽  
Symmetrical Configuration ◽  
Total Power Consumption

We proposed a new method for designing the CMOS differential log-companding amplifier which achieves significant improvements in linearity, common-mode rejection ratio (CMRR), and output range. With the new nonlinear function used in the log-companding technology, this proposed amplifier has a very small total harmonic distortion (THD) and simultaneously a wide output current range. Furthermore, a differential structure with conventionally symmetrical configuration has been adopted in this novel method in order to obtain a high CMRR. Because all transistors in this amplifier operate in the weak inversion, the supply voltage and the total power consumption are significantly reduced. The novel log-companding amplifier was designed using a 0.18 μm CMOS technology. Improvements in THD, output current range, noise, and CMRR are verified using simulation data. The proposed amplifier operates from a 0.8 V supply voltage, shows a 6.3 μA maximum output current range, and has a 6 μW power consumption. The THD is less than 0.03%, the CMRR of this circuit is 74 dB, and the input referred current noise density is166.1 fA/Hz. This new method is suitable for biomedical applications such as electrocardiogram (ECG) signal acquisition.

Exergy Analysis of a Novel Cryogenic Concept for the Liquefaction of Natural Gas Integrated Into an Air Separation Process

2016 ◽  
Author(s):  
S. Tesch ◽  
T. Morosuk ◽  
G. Tsatsaronis
Keyword(s):  
Natural Gas ◽  
Power Consumption ◽  
Exergy Analysis ◽  
Primary Energy ◽  
Separation Process ◽  
Air Separation ◽  
Total Power ◽  
Separation Unit ◽  
Total Power Consumption ◽  
Liquefaction Temperature

The increasing demand for primary energy leads to a growing market of natural gas and the associated market for liquefied natural gas (LNG) increases, too. The liquefaction of natural gas is an energy- and cost-intensive process. After exploration, natural gas, is pretreated and cooled to the liquefaction temperature of around −160°C. In this paper, a novel concept for the integration of the liquefaction of natural gas into an air separation process is introduced. The system is evaluated from the energetic and exergetic points of view. Additionally, an advanced exergy analysis is conducted. The analysis of the concepts shows the effect of important parameters regarding the maximum amount of liquefiable of natural gas and the total power consumption. Comparing the different cases, the amount of LNG production could be increased by two thirds, while the power consumption is doubled. The results of the exergy analysis show, that the introduction of the liquefaction of natural gas has a positive effect on the exergetic efficiency of a convetional air separation unit, which increases from 38% to 49%.

scholarly journals Optimization of Power Consumption for the Design of 802.11n MIMO_OFDM System

2018 ◽  
Vol 26 (4) ◽  
pp. 172-184
Author(s):  
Muthna Jasim Fadhil
Keyword(s):  
Power Consumption ◽  
Orthogonal Frequency Division Multiplexing ◽  
High Speed ◽  
Mimo Systems ◽  
Total Power ◽  
Wireless System ◽  
Generator System ◽  
Total Power Consumption ◽  
Consumption Energy ◽  
High Reduction

In modern systems communication, different methods have been improved to change the prior imitative techniques that process communication data with high speed. It is necessary to improve (OFDM) Orthogonal Frequency Division Multiplexing technique because the development in the guideline communication of wireless system which include security data and transmission data reliability. The applications communications of wireless is important to develop in order to optimize the process of communication leads to reduce the level consumption energy of the output level signal. The architecture of VLSI is used to optimize the performance transceiver in 802.11 n OFDM-MIMO systems, this idea concentrate on the design of 6x6 MIMO_OFDM system in software simulink of MATLAB then using generator system for transfer to code of VHDL and applying in FPGA Xilinx Spartan 3 XC3S200 . The modelsim used to get the simulation while Xilinx power estimator is used to calculate power. The results registered total power consumption about 94mW while compared with previous work  was 136mW which means a high reduction of about 30.8% .

scholarly journals Modified Flower Pollination based Task Scheduling in Cloud Environment using Virtual Machine Migration

2019 ◽  
Vol 8 (9) ◽  
pp. 1856-1860
Keyword(s):  
Cloud Computing ◽  
Virtual Machine ◽  
Resource Selection ◽  
Heuristic Algorithms ◽  
Past History ◽  
Fitness Function ◽  
Task Type ◽  
Flower Pollination Algorithm ◽  
Flower Pollination ◽  
Selection Of

Cloud computing allows users to use resources pay per use model by the help of internet. Users are able to do computation dynamically from different location by using internet resources. The major challenging task in cloud computing is efficient selection of resources for the tasks submitted by users. A number of heuristics and meta-heuristics algorithms are designed by different researchers. The most critical phase is the selection of appropriate resource and its management. The selection of resource include to identify list of authenticated available resources in the cloud for job submission and to choose the best resource. The best resource selection is done by the analysis of several factors like expected time to execute a task by user, access restriction to resources, and expected cost to use resources. In this paper, cloud architecture for resource selection is proposed which combines these factors and make the effective resource selection. In this paper a modified flower pollination algorithm is proposed to migrate the task on efficient virtual machine. The selection of the efficient virtual machine is calculated by the fitness function. By calculating the fitness function, the modified FPA algorithm is used to take the decision regarding VM migration is required to improve the resource efficiency or not. In this paper Virtual machine mapper maps the task as per knowledge base i.e. past history of the virtual machine, task type whether computational or communicational based. The results are compared with the existing meta-heuristic algorithms.

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