The running generation of world, cloud computing has become the most powerful, chief and also lightning technology. IT based companies has already changed their way to buy and design hardware through this technology. It is a high utility which can also make software more attractive. Load balancing research in
cloud technology is one of the burning technologies in modern time. In this paper, pointing various proposed algorithms, the topic of load balancing in Cloud Computing are researched and compared to provide a gist of the latest way in this research area. By using Genetic Algorithm the balance is most
flexible which is represented here.
Cloud computing & energy efficiency using cloud to decrease the energy use in...Puru Agrawal
Cloud can be used to decrease the energy use in large companies. This presentation deals with a model which explains as how cloud can be used to decrease the energy uses. This is a field related to green computing and minimum use of energy resources.
Cloud computing is a pay-per-use model enabling convenient, on-demand network access to shared pool of configurable computing resources (e.g., networks, services, storage, applications and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction.
Cloud computing & energy efficiency using cloud to decrease the energy use in...Puru Agrawal
Cloud can be used to decrease the energy use in large companies. This presentation deals with a model which explains as how cloud can be used to decrease the energy uses. This is a field related to green computing and minimum use of energy resources.
Cloud computing is a pay-per-use model enabling convenient, on-demand network access to shared pool of configurable computing resources (e.g., networks, services, storage, applications and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction.
Load Balancing In Cloud Computing:A ReviewIOSR Journals
Abstract: As the IT industry is growing day by day, the need of computing and storage is increasing
rapidly. The amount of data exchanged over the network is constantly increasing. Thus the process of this
increasing mass of data requires more computer equipment to meet the various needs of the organizations.
To better capitalize their investment, the over-equipped organizations open their infrastructures to others by
exploiting the Internet and other important technologies such as virtualization by creating a new computing
model: the cloud computing. Cloud computing is one of the significant milestones in recent times in the
history of computers. The basic concept of cloud computing is to provide a platform for sharing of resources
which includes software and infrastructure with the help of virtualization. This paper presents a brief review
of cloud computing. The main emphasize of this paper is on the load balancing technique in cloud
computing.
Keywords: Cloud Computing, Load Balancing, Dynamic Load Balancing, Virtualization, Data Center.
This unit includes the following content :
*Introduction to cloud computing
*Move to cloud computing
*Types of cloud
*Working of cloud computing
*Characteristics of cloud
DDBMS, characteristics, Centralized vs. Distributed Database, Homogeneous DDBMS, Heterogeneous DDBMS, Advantages, Disadvantages, What is parallel database, Data fragmentation, Replication, Distribution Transaction
Middleware and Middleware in distributed applicationRishikese MR
The seminar discuss about the common middleware concept and middleware in distributed applications .Also we discuss about 4 different types of middleware. MOM( Message oriented Middleware), ORB (object request broker), TP Monitors, Request procedure calls RPC.
The slide also gives the advantages and disadvantages of each.
A brief discussion about Cloud computing for a beginner, you can get a clear idea about cloud computing from this slides.Also, discuss cloudsim simulator.
Database systems that were based on the object data model were known originally as object-oriented databases (OODBs).These are mainly used for complex objects
PROPOSED LOAD BALANCING ALGORITHM TO REDUCE RESPONSE TIME AND PROCESSING TIME...IJCNCJournal
Cloud computing is a new technology that brings new challenges to all organizations around the world.
Improving response time for user requests on cloud computing is a critical issue to combat bottlenecks. As
for cloud computing, bandwidth to from cloud service providers is a bottleneck. With the rapid development
of the scale and number of applications, this access is often threatened by overload. Therefore, this paper
our proposed Throttled Modified Algorithm(TMA) for improving the response time of VMs on cloud
computing to improve performance for end-user. We have simulated the proposed algorithm with the
CloudAnalyts simulation tool and this algorithm has improved response times and processing time of the
cloud data center.
Dynamic Cloud Partitioning and Load Balancing in Cloud Shyam Hajare
Cloud computing is the emerging and transformational paradigm in the field of information technology. It mostly focuses in providing various services on demand and resource allocation and secure data storage are some of them. To store huge amount of data and accessing data from such metadata is new challenge. Distributing and balancing of the load over a cloud using cloud partitioning can ease the situation. Implementing load balancing by considering static as well as dynamic parameters can improve the performance cloud service provider and can improve the user satisfaction. Implementation the model can provide dynamic way of resource selection de-pending upon different situation of cloud environment at the time of accessing cloud provisions based on cloud partitioning. This model can provide effective load balancing algorithm over the cloud environment, better refresh time methods and better load status evaluation methods.
Load Balancing In Cloud Computing:A ReviewIOSR Journals
Abstract: As the IT industry is growing day by day, the need of computing and storage is increasing
rapidly. The amount of data exchanged over the network is constantly increasing. Thus the process of this
increasing mass of data requires more computer equipment to meet the various needs of the organizations.
To better capitalize their investment, the over-equipped organizations open their infrastructures to others by
exploiting the Internet and other important technologies such as virtualization by creating a new computing
model: the cloud computing. Cloud computing is one of the significant milestones in recent times in the
history of computers. The basic concept of cloud computing is to provide a platform for sharing of resources
which includes software and infrastructure with the help of virtualization. This paper presents a brief review
of cloud computing. The main emphasize of this paper is on the load balancing technique in cloud
computing.
Keywords: Cloud Computing, Load Balancing, Dynamic Load Balancing, Virtualization, Data Center.
This unit includes the following content :
*Introduction to cloud computing
*Move to cloud computing
*Types of cloud
*Working of cloud computing
*Characteristics of cloud
DDBMS, characteristics, Centralized vs. Distributed Database, Homogeneous DDBMS, Heterogeneous DDBMS, Advantages, Disadvantages, What is parallel database, Data fragmentation, Replication, Distribution Transaction
Middleware and Middleware in distributed applicationRishikese MR
The seminar discuss about the common middleware concept and middleware in distributed applications .Also we discuss about 4 different types of middleware. MOM( Message oriented Middleware), ORB (object request broker), TP Monitors, Request procedure calls RPC.
The slide also gives the advantages and disadvantages of each.
A brief discussion about Cloud computing for a beginner, you can get a clear idea about cloud computing from this slides.Also, discuss cloudsim simulator.
Database systems that were based on the object data model were known originally as object-oriented databases (OODBs).These are mainly used for complex objects
PROPOSED LOAD BALANCING ALGORITHM TO REDUCE RESPONSE TIME AND PROCESSING TIME...IJCNCJournal
Cloud computing is a new technology that brings new challenges to all organizations around the world.
Improving response time for user requests on cloud computing is a critical issue to combat bottlenecks. As
for cloud computing, bandwidth to from cloud service providers is a bottleneck. With the rapid development
of the scale and number of applications, this access is often threatened by overload. Therefore, this paper
our proposed Throttled Modified Algorithm(TMA) for improving the response time of VMs on cloud
computing to improve performance for end-user. We have simulated the proposed algorithm with the
CloudAnalyts simulation tool and this algorithm has improved response times and processing time of the
cloud data center.
Dynamic Cloud Partitioning and Load Balancing in Cloud Shyam Hajare
Cloud computing is the emerging and transformational paradigm in the field of information technology. It mostly focuses in providing various services on demand and resource allocation and secure data storage are some of them. To store huge amount of data and accessing data from such metadata is new challenge. Distributing and balancing of the load over a cloud using cloud partitioning can ease the situation. Implementing load balancing by considering static as well as dynamic parameters can improve the performance cloud service provider and can improve the user satisfaction. Implementation the model can provide dynamic way of resource selection de-pending upon different situation of cloud environment at the time of accessing cloud provisions based on cloud partitioning. This model can provide effective load balancing algorithm over the cloud environment, better refresh time methods and better load status evaluation methods.
Cloud computing is a new computing paradigm that, just as electricity was firstly generated at home and
evolved to be supplied from a few utility providers, aims to transform computing into a utility. It is a mapping
strategy that efficiently equilibrates the task load into multiple computational resources in the network based on the
system status to improve performance. The objective of this research paper is to show the results of Hybrid DEGA,
in which GA is implemented after DE
STUDY THE EFFECT OF PARAMETERS TO LOAD BALANCING IN CLOUD COMPUTINGIJCNCJournal
The rapid growth of users on the cloud service and number of services to the user increases the load on the
servers at cloud datacenter. This issue is becoming a challenge for the researchers. And requires used
effectively a load balancing technique not only to balance the resources for servers but also reduce the
negative impact to the end-user service. The current, load balancing techniques have solved the various
problems such as: (i) load balancing after a server was overloaded; (ii) load balancing and load forecast
for the allocation of resources; iii) improving the parameters affecting to load balancing in cloud. The
study of improving these parameters have great significance to improving system performance through
load balancing. From there, we can propose more effective methods of load balancing, in order to increase
system performance. Therefore, in this paper we researched some parameters affecting the performance
load balancing on the cloud computing.
A load balancing strategy for reducing data loss risk on cloud using remodif...IJECEIAES
Cloud computing always deals with new problems to fulfill the demand of the challenging organizations around the whole world. Reducing response time without the risk of data loss is a very critical issue for the user requests on cloud computing. Load balancing ensures quick response of virtual machine (VM), proper usage of VMs, throughput, and minimal cost of VMs. This paper introduces a re-modified throttled algorithm (RTMA) that reduces the risk of data hampering and data loss considering the availability of VM which increases system’s performance. Response time of virtual machines have been considered in our work, so that when migration process is running, data will not be overflowed in the VMs. Thus, the data migration process becomes high and reliable. We have completed the overall simulation of our proposed algorithm on the cloud analyst tool and successfully reduced the risk of data loss as well as maintains the response time.
Cloud Computing Load Balancing Algorithms Comparison Based SurveyINFOGAIN PUBLICATION
Cloud computing is an online primarily based computing. This computing paradigm has increased the employment of network wherever the potential of 1 node may be used by alternative node. Cloud provides services on demand to distributive resources like info, servers, software, infrastructure etc. in pay as you go basis. Load reconciliation is one amongst the vexing problems in distributed atmosphere. Resources of service supplier have to be compelled to balance the load of shopper request. Totally different load reconciliation algorithms are planned so as to manage the resources of service supplier with efficiency and effectively. This paper presents a comparison of assorted policies used for load reconciliation.
Cloud computing is a mix of distributed, grid and parallel processing. It is as of late in pattern on account of the
benefits it gives. It gives a pool of resources which are shared among different clients. Alongside its expanding request, it endures
with a few issues. A standout amongst the most vital and testing issue of cloud computing is load balancing. Load balancing
essentially intends to adjust the load similarly among a few hubs so hub is over-burden, under loaded or sitting inactive. Till date
there are numerous calculations proposed to deal with load balancing yet none of them has been demonstrated as productive one.
In this paper a load balancing algorithm is proposed utilizing rule of genetic algorithm. Fitness of assignments is ascertained and
on the premise of fitness load balancing is done. In this algorithm priority is appointed to the wellness computed in like manner
the chromosome with most noteworthy fitness is doled out least priority. Fitness here stands for the aggregate cost needs to
actualize an errand. Increasingly the cost more is the fitness. The entire simulation is performed on cloudsim 3.0 toolbox which is
JAVA based simulator.
Multilevel Hybrid Cognitive Load Balancing Algorithm for Private/Public Cloud...IDES Editor
Cloud computing is an emerging computing
paradigm. It aims to share data, resources and services
transparently among users of a massive grid. Although the
industry has started selling cloud-computing products,
research challenges in various areas, such as architectural
design, task decomposition, task distribution, load
distribution, load scheduling, task coordination, etc. are still
unclear. Therefore, we study the methods to reason and model
cloud computing as a step towards identifying fundamental
research questions in this paradigm. In this paper, we propose
a model for load distribution on cloud computing by modeling
them as cognitive systems and using aspects which not only
depend on the present state of the system, but also, on a set of
predefined transitions and conditions. The entirety of this
model is then bundled to cater the task of job distribution
using the concept of application metadata. Later, we draw a
qualitative and simulation based summarization for the
proposed model. We finally evaluate the results and draw up
a series of key conclusions in cloud computing for future
exploration.
Elastic neural network method for load prediction in cloud computing gridIJECEIAES
Cloud computing still has no standard definition, yet it is concerned with Internet or network on-demand delivery of resources and services. It has gained much popularity in last few years due to rapid growth in technology and the Internet. Many issues yet to be tackled within cloud computing technical challenges, such as Virtual Machine migration, server association, fault tolerance, scalability, and availability. The most we are concerned with in this research is balancing servers load; the way of spreading the load between various nodes exists in any distributed systems that help to utilize resource and job response time, enhance scalability, and user satisfaction. Load rebalancing algorithm with dynamic resource allocation is presented to adapt with changing needs of a cloud environment. This research presents a modified elastic adaptive neural network (EANN) with modified adaptive smoothing errors, to build an evolving system to predict Virtual Machine load. To evaluate the proposed balancing method, we conducted a series of simulation studies using cloud simulator and made comparisons with previously suggested approaches in the previous work. The experimental results show that suggested method betters present approaches significantly and all these approaches.
A Comparative Study of Load Balancing Algorithms for Cloud ComputingIJERA Editor
Cloud Computing is fast growing technology in both industry research and academy. User can access the cloud
service and pay based on the usage of resource. Balancing the load is major task of cloud service provider with
minimum response time, maximum throughput and better resource utilization. There are many load balancing
algorithms proposed to assign a user request to cloud resource in efficient manner. In this paper three load balancing
algorithms are simulated in Cloud Analyst and results are compared.
LOAD BALANCING IN AUTO SCALING-ENABLED CLOUD ENVIRONMENTSijccsa
Cloud computing is growing in popularity and it has been continuously updated with more improvements.
Auto scaling is one of such improvements that help to maintain the availability of customer’s subscribed
cloud system. The appearance of an auto scaling mechanism in the cloud system with many existing system
mechanisms is an issue that needs to be considered. Because, normally, there is no free drawbacks
whenever a new part is added to a certain stable system. In this paper, we consider how existing load
balancing and auto scaling impact on each other. For the purpose, we have modeled a cloud system with
an auto scaler and a load balancer and implementing simulations based on the constructed model. Also
based on the results from the computer simulations we proposed about choosing load balancers for
subscribed cloud system with auto scaling service.
Load Balancing in Auto Scaling Enabled Cloud Environmentsneirew J
Cloud computing is growing in popularity and it has been continuously updated with more improvements.
Auto scaling is one of such improvements that help to maintain the availability of customer’s subscribed
cloud system. The appearance of an auto scaling mechanism in the cloud system with many existing system
mechanisms is an issue that needs to be considered. Because, normally, there is no free drawbacks
whenever a new part is added to a certain stable system. In this paper, we consider how existing load
balancing and auto scaling impact on each other. For the purpose, we have modeled a cloud system with
an auto scaler and a load balancer and implementing simulations based on the constructed model. Also
based on the results from the computer simulations we proposed about choosing load balancers for
subscribed cloud system with auto scaling service.
ITA: THE IMPROVED THROTTLED ALGORITHM OF LOAD BALANCING ON CLOUD COMPUTINGIJCNCJournal
Cloud computing makes the information technology industry boom. It is a great solution for businesses who want to save costs while ensuring the quality of service. One of the key issues that make cloud computing successful is the load balancing technique used in the load balancer to minimize time costs and optimize costs economically. This paper proposes an algorithm to enhance the processing time of tasks so that it can help improve the load balancing capacity on cloud computing. This algorithm, named as Improved Throttled Algorithm (ITA), is an improvement of Throttled Algorithm. The paper uses the Cloud Analyst tool to simulate. The selected algorithms are used to compare: Equally Load, Round Robin, Throttled and TMA. The simulation results show that the proposed algorithm ITA has improved the processing time of tasks, time spent processing requests and reduced the cost of Datacenters compared to the selected popular algorithms as above. The improvement of ITA is because of selecting virtual machines in an index table that is available but in order of priority. It helps response times and processing times remain stable, limits the idling resources, and cloud costs are minimized compared to selected algorithms.
ITA: The Improved Throttled Algorithm of Load Balancing on Cloud ComputingIJCNCJournal
Cloud computing makes the information technology industry boom. It is a great solution for businesses who want to save costs while ensuring the quality of service. One of the key issues that make cloud computing successful is the load balancing technique used in the load balancer to minimize time costs and optimize costs economically. This paper proposes an algorithm to enhance the processing time of tasks so that it can help improve the load balancing capacity on cloud computing. This algorithm, named as Improved Throttled Algorithm (ITA), is an improvement of Throttled Algorithm. The paper uses the Cloud Analyst tool to simulate. The selected algorithms are used to compare: Equally Load, Round Robin, Throttled and TMA. The simulation results show that the proposed algorithm ITA has improved the processing time of tasks, time spent processing requests and reduced the cost of Datacenters compared to the selected popular algorithms as above. The improvement of ITA is because of selecting virtual machines in an index table that is available but in order of priority. It helps response times and processing times remain stable, limits the idling resources, and cloud costs are minimized compared to selected algorithms.
Task Scheduling using Hybrid Algorithm in Cloud Computing Environmentsiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
ANALYSIS ON LOAD BALANCING ALGORITHMS IMPLEMENTATION ON CLOUD COMPUTING ENVIR...AM Publications
Cloud computing means storing and accessing data and programs over the Internet instead of your computer's hard drive. The cloud is just a metaphor for the Internet. The elements involved in cloud computing are clients, data center and distributed server. One of the main problems in cloud computing is load balancing. Balancing the load means to distribute the workload among several nodes evenly so that no single node will be overloaded. Load can be of any type that is it can be CPU load, memory capacity or network load. In this paper we presented an architecture of load balancing and algorithm which will further improve the load balancing problem by minimizing the response time. In this paper, we have proposed the enhanced version of existing regulated load balancing approach for cloud computing by comping the Randomization and greedy load balancing algorithm. To check the performance of proposed approach, we have used the cloud analyst simulator (Cloud Analyst). Through simulation analysis, it has been found that proposed improved version of regulated load balancing approach has shown better performance in terms of cost, response time and data processing time.
Public Cloud Partition Using Load Status Evaluation and Cloud Division RulesIJSRD
with growth of cloud computing load balancing is important impact on performance. Cloud computing efficiency depends on good load balancer. Many type of situation occur that time cloud partitioning is done by load balancer. Different type of situation needed different type of strategies for public cloud portioning using load balancer.in this paper we work on, partition of public cloud using two type of situation first is load status evaluation and second is cloud division rules. Load status evaluation is measure in number of cloudlets arrives at datacenter and cloud divisions rules are based on cloudlet come from which geographical location. On the basis of geographical location we partition public cloud and improve performance of load balancing in cloud computing. We implement proposed system with help of cloudsim3.0 simulator.
Similar to PROCESS OF LOAD BALANCING IN CLOUD COMPUTING USING GENETIC ALGORITHM (20)
Electrical & Computer Engineering: An International Journal (ECIJ)ecij
Electrical & Computer Engineering: An International Journal (ECIJ) is a peer-reviewed, open access journal that addresses the impacts and challenges of Electrical and Computer Engineering. The journal documents practical and theoretical results which make a fundamental contribution for the development Electrical and Computer Engineering.
Electrical & Computer Engineering: An International Journal (ECIJ)ecij
Electrical & Computer Engineering: An International Journal (ECIJ) is a peer-reviewed,
open access journal that address the impacts and challenges of Electrical and Computer
Engineering. The journal documents practical and theoretical results which make a fundamental
contribution for the development Electrical and Computer Engineering.
Electrical & Computer Engineering: An International Journal (ECIJ)ecij
Electrical & Computer Engineering: An International Journal (ECIJ) is a peer-reviewed,
open access journal that address the impacts and challenges of Electrical and Computer
Engineering. The journal documents practical and theoretical results which make a fundamental
contribution for the development Electrical and Computer Engineering.
Electrical & Computer Engineering: An International Journal (ECIJ)ecij
Electrical & Computer Engineering: An International Journal (ECIJ) is a peer-reviewed,
open access journal that address the impacts and challenges of Electrical and Computer
Engineering. The journal documents practical and theoretical results which make a fundamental
contribution for the development Electrical and Computer Engineering
This work investigates and evaluates the electric energy interruptions to the residential sector resulting from severe power outages. The study results show that this sector will suffer tangible and intangible losses should these outages occur during specific times, seasons, and for prolonged durations. To reduce these power outages and hence mitigate their adverse consequences, the study proposes practical measures that
can be adopted without compromising the consumers’ needs, satisfaction, and convenience.
GRID SIDE CONVERTER CONTROL IN DFIG BASED WIND SYSTEM USING ENHANCED HYSTERES...ecij
The standard grid codes suggested, that the wind generators should stay in connected and reliable active and reactive power should be provided during uncertainties. This paper presents an independent control of Grid Side Converter (GSC) for a doubly fed induction generator (DFIG). A novel GSC controller has
been designed by incorporating a new Enhanced hysteresis comparator (EHC) that utilizes the hysteresis band to produce the suitable switching signal to the GSC to get enhanced controllability during grid unbalance. The EHC produces higher duty-ratio linearity and larger fundamental GSC currents with
lesser harmonics. Thus achieve fast transient response for GSC. All these features are confirmed through
time domain simulation on a 15 KW DFIG Wind Energy Conversion System (WECS).
Electrical & Computer Engineering: An International Journal (ECIJ)ecij
Electrical & Computer Engineering: An International Journal (ECIJ) is a peer-reviewed,
open access journal that address the impacts and challenges of Electrical and Computer
Engineering. The journal documents practical and theoretical results which make a fundamental
contribution for the development Electrical and Computer Engineering.
PREPARATION OF POROUS AND RECYCLABLE PVA-TIO2HYBRID HYDROGELecij
Nano TiO2, one of the most effective photocatalysts, has extensive usein fields such as air purification,
sweage treatment, water spitting, reduction of CO2, and solar cells. Nowadays, the most promising method to
recycle nano TiO2during the photocatalysis is to immobilize TiO2onto matrix, such as polyvinyl alcohol
(PVA). However, due to the slow water permeability of PVA after cross-linking, the pollutants could not
contact with nano TiO2photocatalyst in time. To overcome this problem, we dispersed calcium carbonate
particles into a PVA-TiO2 mixture and then filmed the glass. PVA-TiO2-CaCO3 films were obtained by
drying. Through thermal treatment, we obtained the cross-linked PVA-TiO2-CaCO3 films. Finally, the
calcium carbonate in the film was dissolved by hydrochloric acid, and the porous PVA-TiO2 composite
photocatalyst was obtained. The results show the addition of CaCO3 has no obvious effect on PVA
cross-linking and that a large number of cavities have been generated on the surface and inside of porous
PVA-TiO2 hybrid hydrogel film. The size of the holes is about 5-15μm, which is consistent with that of
CaCO3.The photocatalytic rate constant of porous PVA-TiO2 hybrid hydrogel film is 2.49 times higher than
that of nonporous PVA-TiO2 hybrid hydrogel film.
4th International Conference on Electrical Engineering (ELEC 2020)ecij
4th International Conference on Electrical Engineering (ELEC 2020)aims to bring together researchers and practitioners from academia and industry to focus on recent systems and techniques in the broad field of Electrical Engineering. Original research papers, state-of-the-art reviews are invited for publication in all areas of Electrical Engineering.
Electrical & Computer Engineering: An International Journal (ECIJ)ecij
Electrical & Computer Engineering: An International Journal (ECIJ) is a peer-reviewed, open access journal that addresses the impacts and challenges of Electrical and Computer Engineering. The journal documents practical and theoretical results which make a fundamental contribution for the development Electrical and Computer Engineering.
4th International Conference on Bioscience & Engineering (BIEN 2020) ecij
4th International Conference on Bioscience & Engineering (BIEN 2020) will provide an excellent International forum for sharing knowledge and results in theory, methodology and applications impacts and challenges of Bioscience and Engineering. The goal of this Conference is to bring together researchers and practitioners from academia and industry to focus on Bioscience and Engineering advancements and establishing new collaborations in these areas. Original research papers, state-of-the-art reviews are invited for publication in all areas of Bioscience and Engineering.
Electrical & Computer Engineering: An International Journal (ECIJ)ecij
Scope & Topics
Electrical & Computer Engineering: An International Journal (ECIJ) is a peer-reviewed, open access journal that addresses the impacts and challenges of Electrical and Computer Engineering. The journal documents practical and theoretical results which make a fundamental contribution for the development Electrical and Computer Engineering.
Electrical & Computer Engineering: An International Journal (ECIJ)
ISSN: 2201-5957
https://wireilla.com/engg/ecij/index.html
Paper Submission
Authors are invited to submit papers for this journal through E-mail: ecijjournal@wireilla.com .
Important Dates
•Submission Deadline: March 28, 2020
Contact US
Here's where you can reach us: ecijjournal@wireilla.com
GRID SIDE CONVERTER CONTROL IN DFIG BASED WIND SYSTEM USING ENHANCED HYSTERES...ecij
The standard grid codes suggested, that the wind generators should stay in connected and reliable active and reactive power should be provided during uncertainties. This paper presents an independent control of Grid Side Converter (GSC) for a doubly fed induction generator (DFIG). A novel GSC controller has been designed by incorporating a new Enhanced hysteresis comparator (EHC) that utilizes the hysteresis band to produce the suitable switching signal to the GSC to get enhanced controllability during grid unbalance. The EHC produces higher duty-ratio linearity and larger fundamental GSC currents with lesser harmonics. Thus achieve fast transient response for GSC. All these features are confirmed through time domain simulation on a 15 KW DFIG Wind Energy Conversion System (WECS).
UNION OF GRAVITATIONAL AND ELECTROMAGNETIC FIELDS ON THE BASIS OF NONTRADITIO...ecij
The traditional principle of solving the problem of combining the gravitational and electromagnetic fields is associated with the movement of the transformation of parameters from the electromagnetic to the gravitational field on the basis of Maxwell and Lorentz equations. The proposed non-traditional principle
is associated with the movement of the transformation of parameters from the gravitational to the electromagnetic field, which simplifies the process. Nave principle solving this task by using special physical quantities found by M. Planck in 1900: - Planck’s length, time and mass), the uniqueness of which is that they are obtained on the basis of 3 fundamental physical constants: the velocity c of light in vacuum, the Planck’s constant h and the gravitational constant G, which reduces them to the fundamentals of the Universe. Strict physical regularities were obtained for the based on intercommunication of 3-th
fundamental physical constants c, h and G, that allow to single out wave characteristic νG from G which is identified with the frequency of gravitational field. On this base other wave and substance parameters were strictly defined and their numerical values obtained. It was proved that gravitational field with the given wave parameters can be unified only with electromagnetic field having the same wave parameters that’s why it is possible only on Plank’s level of world creation. The solution of given problems is substantiated by well-known physical laws and conformities and not contradiction to modern knowledge about of material world and the Universe on the whole. It is actual for development of physics and other branches of science and technique.
USING MACHINE LEARNING TO BUILD A SEMI-INTELLIGENT BOT ecij
Nowadays, real-time systems and intelligent systems offer more and more control interface based on voice recognition or human language recognition. Robots and drones will soon be mainly controlled by voice. Other robots will integrate bots to interact with their users, this can be useful both in industry and entertainment. At first, researchers were digging on the side of "ontology reasoning". Given all the technical constraints brought by the treatment of ontologies, an interesting solution has emerged in last years: the construction of a model based on machine learning to connect a human language to a knowledge
base (based for example on RDF). We present in this paper our contribution to build a bot that could be used on real-time systems and drones/robots, using recent machine learning technologies.
MODELING AND SIMULATION OF SOLAR PHOTOVOLTAIC APPLICATION BASED MULTILEVEL IN...ecij
As the solar market is blooming and forecasted to continue this trend in the coming years. The efficiency and reliability of PV based system has always been a contention among researchers. Therefore, multilevel inverters are gaining more assiduity as it has multitude of benefits. It offers high power capability along with low output harmonics. The main disadvantage of MLI is its complexity and requirement of large
number of power devices and passive components. This paper presents a topology that achieves 37.5% reduction in number of passive components and power devices for five-level inverter. This topology is basically based on H-bridge with bi-directional auxiliary switch. This paper includes a stand-alone PV system in which designing and simulation of Boost converter connected with multilevel inverter for ac load is presented. Perturb and observe MPPT algorithm has been implemented to extract maximum power. The premier objective is to obtain Voltage with less harmonic distortion economically. Multicarrier Sinusoidal
PWM techniques have been implemented and analysed for modulation scheme. The Proposed system is simulated n MATLAB/Simulink platform.
Investigation of Interleaved Boost Converter with Voltage multiplier for PV w...ecij
This paper depicts the significance of Interleaved Boost Converter (IBC) with diode-capacitor multiplierwith PV as the input source. Maximum Power Point Tracking (MPPT) was used to obtain maximum power from the PV system. In this, interleaving topology is used to reduce the input current ripple, output voltage ripple, power loss and to suppress the ripple in battery current in the case of Plugin Hybrid Electric Vehicle (PHEV). Moreover, voltage multiplier cells are added in the IBC configuration to reduce the narrow turn-off periods. Two MPPT techniques are compared in this paper: i) Perturb and Observe (P&O) algorithm ii) Fuzzy Logic . The two algorithms are simulated using MATLAB and the comparison of performance parameters like the ripple is done and the results are verified.
A COMPARISON BETWEEN SWARM INTELLIGENCE ALGORITHMS FOR ROUTING PROBLEMSecij
Travelling salesman problem (TSP) is a most popular combinatorial routing problem, belongs to the class of NP-hard problems. Many approacheshave been proposed for TSP.Among them, swarm intelligence (SI) algorithms can effectively achieve optimal tours with the minimum lengths and attempt to avoid trapping in local minima points. The transcendence of each SI is depended on the nature of the problem. In our studies, there has been yet no any article, which had compared the performance of SI algorithms for TSP perfectly. In this paper,four common SI algorithms are used to solve TSP, in order to compare the performance of SI algorithms for the TSP problem. These algorithms include genetic algorithm, particle swarm optimization, ant colony optimization, and artificial bee colony. For each SI, the various parameters and operators were tested, and the best values were selected for it. Experiments oversome benchmarks fromTSPLIBshow that
artificial bee colony algorithm is the best one among the fourSI-basedmethods to solverouting problems like TSP.
How to Create Map Views in the Odoo 17 ERPCeline George
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PROCESS OF LOAD BALANCING IN CLOUD COMPUTING USING GENETIC ALGORITHM
1. Electrical & Computer Engineering: An International Journal (ECIJ) Volume 4, Number 2, June 2015
DOI : 10.14810/ecij.2015.4206 57
PROCESS OF LOAD BALANCING IN CLOUD
COMPUTING USING GENETIC ALGORITHM
Md. Shahjahan Kabir 1
, Kh. Mohaimenul Kabir 2
and Dr. Rabiul Islam 3
1
Dept. of CSE, Dhaka International University, Dhaka, Bangladesh
2
Dept. of CSE, Dhaka International University, Dhaka, Bangladesh
3
Associate Professor, Dept. of CSE, RUET, Rajshahi, Bangladesh
ABSTRACT
The running generation of world, cloud computing has become the most powerful, chief and also lightning
technology. IT based companies has already changed their way to buy and design hardware through this
technology. It is a high utility which can also make software more attractive. Load balancing research in
cloud technology is one of the burning technologies in modern time. In this paper, pointing various
proposed algorithms, the topic of load balancing in Cloud Computing are researched and compared to
provide a gist of the latest way in this research area. By using Genetic Algorithm the balance is most
flexible which is represented here.
KEYWORDS
Genetic Algorithm, Load balancing, Cloud Computing.
1. INTRODUCTION
The latest vision of large distributed computing is “Cloud”. Cloud based multimedia system
(CMS) gained momentum as there is large number of users. Cloud computing is internet based
computing, whereby shared resources, software and information are provided to computers and
other devices on-demand, like a public utility. [1] Recent the most burning topic is Cloud storage
for the IT user. Because when a user uses a personal or professional computer for a great purpose,
then they must have some precious files, for which that man is ready to invest more invest to
protect the file. As the scope of cloud scales up, cloud computing service suppliers needs
handling of gigantic requests. The principal challenge converts to keep the performance same or
better whenever such an outbreak happens. Thus in spite of glorious future of Cloud Computing,
many actual problems still essential to be explored for its perfect awareness. One of these
concerns is Load balancing. [2] The following network resources can be load balanced:
• Network edges and facilities such as DNS,FTP, and HTTP
• Making Connections through intelligent switches
• Processing through computer system task
• Storage resources right of entry to application instances.
Without balancing load it is too much hard to manage this cloud computing. As a soft computing
approach Genetic Algorithm has been used in this paper. Cloud Analyst - A Cloud Sims built
Visual Modeler has been used for simulation and analysis of the algorithm. The performance of
2. Electrical & Computer Engineering: An International Journal (ECIJ) Volume 4, Number 2, June 2015
58
Genetic Algorithm is related with two commonly used scheduling algorithm FCFS and RR and a
local search algorithm stochastic hill climbing. [3] The GA operates on a pool of chromosomes
which denote candidate results to the problem. Chromosomes are selected following "survival of
the fittest" and are approved on to the next generation in a development called "reproduction". An
objective function is supplied and used to weigh the relative facts (the so-called "fitness value")
of the chromosomes in the pool, and the reproduction is understood by the genetic operators, such
as selection, crossover and mutation, to generate new points in the search space. [4] Genetic
Algorithm system performs better than greedy heuristic system. This algorithm is also suitable for
the load balancing system. But there is some trouble too such as the GA is quite complicated and
time consuming. But time can be reduced through the proposed method. Due to the best balancing
proposed system performs so much highly and perfectly in cloud network.
2. DESCRIPTION OF CLOUD SYSTEM, LOAD BALANCING & GENETIC
ALGORITHM
2.1. Cloud System
Mainly, cloud computing means storing and accessing data and programs over the internet as an
alternative of the computer’s hard drive. The cloud is just a comparison for the Internet. It goes
back to the days of flowcharts and presentations that would represent the gigantic server-farm
infrastructure of the Internet as unknown but a puffy, white cumulonimbus cloud, accepting
connections and doling out info as it floats.
Cloud computing characterizes a real model shift in the way in which systems are arranged. The
gigantic scale of cloud computing organizations was enabled by the popularization of the Internet
and the enlargement of some large service companies. Cloud computing makes the long-held
dream of utility computing possible with a pay-as-you-go, infinitely scalable, universally
available system. With cloud computing, anybody can start very small and become big very fast.
That's why cloud computing is radical, even if the technology it is constructed on is evolutionary.
[6]
2.2. Scheduling Server for Load balancing
Load Balancing is firstly distributed by cost. Because Cloud computing is costly. When someone
pays, then the scheduling System gives high priority to the most paid user.
3. Electrical & Computer Engineering: An International Journal (ECIJ) Volume 4, Number 2, June 2015
59
Figure 1. Scheduling server for balancing load in cloud computing.
Then it gives importance to 2nd
maximum paid user. That means who pays low cost, his file
distributes with low priority. In the Figure 1, this is labeled that the priority for $4 is more than $2
and $2 is more earlier than $1. This is called the scheduling in cloud computing.
2.3. Genetic Algorithm & Equation
The load balancing abides by three rules such as the location rule, the distribution rule, and the
selection rule. Here the work will process through a dynamic process after doing scheduling
server. Firstly the tasks will be fixed a number. Afterward it will auto execute task number and
size randomly. Then the task handled from a task slot, where the randomly generator are
deposited for processed.
Mainly the development of cloud computing is dynamic but for the arrangement purpose it can be
expressed as allocating N number of jobs applied by the cloud customers to M number of
processing unit in cloud. Every processing unit has a processing unit vector (PUV), where vector
consists of MIPMS that mean how many million instructions can be processed by the machine per
second. R is indicated as delay cost and x is cost of execution of instruction. If these indicated, the
equation will be as below.
PUV = f (MIPMS, x, R) (i)
Same formula for each job submitted by the cloud user can be defined by job unit vector (JUV),
Which is characterized by JUV= f(t,NIC,AT,wc) (ii)
Here t defines the types of the jobs, Software as a Service (SAAS), Infrastructure as a Service
(IAAS) and Platform-as-a-Service (PAAS). NIC represents the number of instruction which is
present. After this term costing is calculated for N jobs among M number of processors
representing in equation (iii).
ζ = W1 ∗ x (NIC ÷ MIPMS ) + W2 ∗ R (iii)
In the equation (iii) W1 and W2 are weights. It is very problematic to decide or optimize the
weights, one criterion could be that the factor is larger is the weight. The Logic is that cloud users
preference or importance of user given to a certain factor over the other. The weight are defined
as W1= 0.8 and W2=0.2 that means the sum is 1. So the load balancing is hard to do. Because the
mechanism of a simple GA are amazingly simple, involving nothing more complex than copying
strings and exchange partial strings. Simplicity of action and power of effect are two main
4. Electrical & Computer Engineering: An International Journal (ECIJ) Volume 4, Number 2, June 2015
60
magnetisms of the GA approach. The efficiency of the GA depends upon an suitable mix of
exploration and exploitation. Three operators to achieve this are: selection, crossover, and
mutation. [4] So this paper is going to reduce the problem of balancing load with simplifying the
GA in the section of cloud computing. Genetic Algorithm mainly works with genesis, crossover
and mutation. The progress of working method of GA is shown in the Figure 2.
Figure 2. The flow chart of Genetic Algorithm
3. PROPOSED ALGORITHM
A method with GA is simplified by three operations. Where it can be justified the load balance,
these are selection, operation and replacement. Processor mainly processes the balance through as
below Figure 3.
5. Electrical & Computer Engineering: An International Journal (ECIJ) Volume 4, Number 2, June 2015
61
Figure 3. Load balancing through processor.
Where P1, P2…………………………………..P5 are the number of processors. The proposed
balancing technique is given below.
Step 1: At the starting point, randomly initialize a population of processing unit after coding them
into binary strings.
Step 2: Calculate the main value of each population for the fitness part.
Step 3: If the maximum number of iteration or optimum solution is found then do:
Step 3(a): Consider chromosome with lowest fitness twice and delete the chromosome
with maximum fitness value to build the mating pool.
Step 3(b): Use single point crossover by randomly picking the crossover point to make
new offspring.
Step 3(c): With a mutation probability of (0.05), mutate new offspring.
Step 3(d): As new population place new offspring and use this population for following
round of iteration. This is called the accepting part.
Step 3(e): Finally test for the end condition.
Step 4: End.
4. EXECUTION
After performed the basic operation that means when fitness calculation, selection
implementation, crossover operation and mutation operation are completed then the execution
will end and the results found. . In GA, the population initialization is well-thought-out to be the
preprocessing hence its thickness is not considered for review. For programming into binary
string a time density of at maximum n1, for estimation of cost function 3 it is at maximum (c × k)
for testing cost c of k number of chromosomes. Selection process has a time complexity of at
most m, for single point crossover the time complexity is at most m, where m is chromosome
6. Electrical & Computer Engineering: An International Journal (ECIJ) Volume 4, Number 2, June 2015
62
length and for mutation at any place it is again m. The three action of GA are frequent iteratively
till the discontinuing norms is met so the total time complexity G is given by equation (iv),
G = O{n1 + (c × k) + (n2 + 1)(m + m + m)} (iv)
For more smooth working the equation can be more effective in this case.
4.1 Results Using One Data Centre
Table 1: Calculation of total average response rate in milliseconds & Model setup
4.2 Results Using Two Data Centre
Table 2: Model Setup & Calculation of total response rate in milliseconds
Formation
of Cloud
1 DC
With No.
of VMs
RR
Response
time
SHC
Response
time
FCFS
response
time
GA Response
time
FC1 30 330.50 330.24 330.84 320.03
FC2 60 330.20 329.64 331.07 330.88
FC3 90 331.01 329.94 329.98 323.66
FC4 120 329.84 329.55 330.02 324.21
Formation
of Cloud
2 DCs
With No. of
VMs
RR
Response
time
SHC
Response
time
FCFS
response
time
GA Response
time
FC1 30 373.22 371.17 382.42 364.31
FC2 60 371.13 368.42 382.11 363.68
FC3 90 370.01 369.51 385.98 363.65
FC4 120 370.07 368.11 381.78 364.26
FC5 30,60 371.03 369.32 380.20 363.22
FC6 30,90 372.02 368.11 380.19 364.23
FC7 30,120 372.33 367.45 379.56 363.45
FC8 60,90 371.11 369.55 378.65 363.77
FC9 60,120 370.27 367.22 379.88 361.77
FC10 90,120 369.92 368.88 377.02 360.02
7. Electrical & Computer Engineering: An International Journal (ECIJ) Volume 4, Number 2, June 2015
63
4.3 Results Using Three Data Centre
Table 3: Model Setup & Calculation of total response rate in milliseconds
4.4 Results Using Four Data Centre
Table 4: Model Setup & Calculation of total response rate in milliseconds
Formation
of Cloud
DC With
No. of VMs
RR
Response
time
SHC
Response
time
FCFS
response
time
GA Response
time
FC1 30 340.40 345.51 339.96 339.03
FC2 60 342.21 342.78 349.18 338.54
FC3 90 339.13 343.82 348.12 333.45
FC4 120 337.45 342.96 352.02 333.11
FC5 30,60,90,12
0
335.22 339.11 341.18 331.11
Formation
of Cloud
DC With
No. of
VMs
RR
Response
time
SHC
Response
time
FCFS
Response
time
GA Response
time
FC1 30 333.77 346.42 342.45 333.38
FC2 60 334.88 345.64 341.65 329.76
FC3 90 337.76 342.17 343.78 327.66
FC4 120 336.91 341.88 341.76 323.11
FC4 30,60,90,
120
335.84 341.59 340.28 321.77
8. Electrical & Computer Engineering: An International Journal (ECIJ) Volume 4, Number 2, June 2015
64
4.5 Results Using Five Data Centre
Table 5: Model Setup & Calculation of total response rate in milliseconds
5. CONCLUSIONS
From the graph shown above it can be conclude that the proposed system is better for the latest
technology. This paper can do better load balancing with the help of Genetic Algorithm in the
cloud network.
REFERENCES
[1] Manisha B. Jadhav, Vishnu J. Gaikwad, C. V. Patil, G. S. Deshpande,2010. CLOUD COMPUTING
APPLICATIONS IN COMPUTATIONAL SCIENCE, International Journal of Advanced Computer
and Mathematical Sciences.. Vol 1, Issue 1. Pp. 1-6.
[2] A Genetic Algorithm (GA) based Load Balancing Strategy for Cloud Computing, Kousik Dasguptaa,
Brototi Mandalb, Paramartha Duttac, Jyotsna Kumar Mondald, Santanu Dame ,International
Conference on Computational Intelligence: Modeling Techniques and Applications (CIMTA),
Procedia Technology 10 ( 2013 ) Pp. 340 – 347.
[3] M. D. Dikaiakos, G. Pallis, D. Katsa, P. Mehra, and A. Vakali, “Cloud Computing: Distributed Internet
Computing for IT and Scientific Research”, in Proc. of IEEE Journal of Internet Computing, Vol. 13,
No. 5, pp. 10-13, 2009.
[4] On File and Task Placements and Dynamic Load Balancing in Distributed Systems, Po-Jen Chuang
and Chi-Wei Cheng, Tamkang Journal of Science and Engineering, Vol. 5, No. 4, pp. 241-252 (2002).
[5] Observations on Using Genetic Algorithms for Dynamic Load-Balancing Albert Y. Zomaya, Senior
Member, IEEE, and Yee-Hwei IEEE Transactions on Parallel and Distributed Systems, Vol. 12, No. 9,
September 2001.
[6] Cloud Computing Bible book, Barrie Sisisky, ISBN: 978-0-470-90356-8.
[7] Comparative Study on Load Balancing Techniques in Cloud Computing, Open Journal of Mobile
Computing and Cloud Computing, N. S. Raghava* and Deepti Singh, Volume 1, Number 1, August
2014.
Formation
of Cloud
DC With
No. of
VMs
RR
Response time
SHC
Response time
FCFS
Response time
GA Response
time
FC1 30 330.16 340.76 339.73 321.30
FC2 60 331.62 339.38 338.37 318.03
FC3 90 337.56 338.49 336.58 317.01
FC4 120 335.98 339.91 335.76 316.88
FC4 30,60,90,1
20
331.99 337.37 331.59 315.01
9. Electrical & Computer Engineering: An International Journal (ECIJ) Volume 4, Number 2, June 2015
65
AUTHORS
Md. Shahjahan Kabir is currently a lecturer of CSE in Dhaka International University. He has got Bsc.
from RUET in 2012. He has already three online publications in the international journal .
Kh. Mohaimenul Kabir is a current student of Bsc. in CSE in Dhaka international University. He has a
online publication in online journal.
Dr. Rabiul Islam is currently a Associate professor of CSE in RUET. He has already completed his Bsc. ,
Msc and Phd. From RUET. He has lots of research and publications. He has published 65 online
international journals till now