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.
Cloud Partitioning of Load Balancing Using Round Robin ModelIJCERT
Abstract: The purpose of load balancing is to look up the performance of a cloud environment through an appropriate
circulation strategy. Good load balancing will construct cloud computing for more stability and efficiency. This paper
introduces a better round robin model for the public cloud based on the cloud partitioning concept with a switch mechanism
to choose different strategies for different situations. Load balancing is the process of giving out of workload among
different nodes or processor. It will introduces a enhanced approach for public cloud load distribution using screening and
game theory concept to increase the presentation of the system.
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.
Cloud computing is an on demand service in which shared resources, information, software and other devices are provided to the end user as per their requirement at a specific time. A cloud consists of several elements such as clients, datacenters and distributed servers. There are n number of clients and end users involved in cloud environment. These clients may make requests to the cloud system simultaneously, making it difficult for the cloud to manage the entire load at a time. The load can be CPU load, memory load, delay or network load. This might cause inconvenience to the clients as there may be delay in the response time or it might affect the performance and efficiency of the cloud environment. So, the concept of load balancing is very important in cloud computing to improve the efficiency of the cloud. Good load balancing makes cloud computing more efficient and improves user satisfaction. This paper gives an approach to balance the incoming load in cloud environment by making partitions of the public cloud.
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.
PROCESS OF LOAD BALANCING IN CLOUD COMPUTING USING GENETIC ALGORITHMecij
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.
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.
Cloud Partitioning of Load Balancing Using Round Robin ModelIJCERT
Abstract: The purpose of load balancing is to look up the performance of a cloud environment through an appropriate
circulation strategy. Good load balancing will construct cloud computing for more stability and efficiency. This paper
introduces a better round robin model for the public cloud based on the cloud partitioning concept with a switch mechanism
to choose different strategies for different situations. Load balancing is the process of giving out of workload among
different nodes or processor. It will introduces a enhanced approach for public cloud load distribution using screening and
game theory concept to increase the presentation of the system.
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.
Cloud computing is an on demand service in which shared resources, information, software and other devices are provided to the end user as per their requirement at a specific time. A cloud consists of several elements such as clients, datacenters and distributed servers. There are n number of clients and end users involved in cloud environment. These clients may make requests to the cloud system simultaneously, making it difficult for the cloud to manage the entire load at a time. The load can be CPU load, memory load, delay or network load. This might cause inconvenience to the clients as there may be delay in the response time or it might affect the performance and efficiency of the cloud environment. So, the concept of load balancing is very important in cloud computing to improve the efficiency of the cloud. Good load balancing makes cloud computing more efficient and improves user satisfaction. This paper gives an approach to balance the incoming load in cloud environment by making partitions of the public cloud.
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.
PROCESS OF LOAD BALANCING IN CLOUD COMPUTING USING GENETIC ALGORITHMecij
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.
Data Distribution Handling on Cloud for Deployment of Big Dataijccsa
Cloud computing is a new emerging model in the field of computer science. For varying workload Cloud computing presents a large scale on demand infrastructure. The primary usage of clouds in practice is to process massive amounts of data. Processing large datasets has become crucial in research and business environments. The big challenges associated with processing large datasets is the vast infrastructure required. Cloud computing provides vast infrastructure to store and process Big data. Vms can be provisioned on demand in cloud to process the data by forming cluster of Vms . Map Reduce paradigm can be used to process data wherein the mapper assign part of task to particular Vms in cluster and reducer combines individual output from each Vms to produce final result. we have proposed an algorithm to reduce the overall data distribution and processing time. We tested our solution in Cloud Analyst Simulation environment wherein, we found that our proposed algorithm significantly reduces the overall data processing time in cloud.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
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.
Application of selective algorithm for effective resource provisioning in clo...ijccsa
Modern day continued demand for resource hungry services and applications in IT sector has led to
development of Cloud computing. Cloud computing environment involves high cost infrastructure on one
hand and need high scale computational resources on the other hand. These resources need to be
provisioned (allocation and scheduling) to the end users in most efficient manner so that the tremendous
capabilities of cloud are utilized effectively and efficiently. In this paper we discuss a selective algorithm
for allocation of cloud resources to end-users on-demand basis. This algorithm is based on min-min and
max-min algorithms. These are two conventional task scheduling algorithm. The selective algorithm uses
certain heuristics to select between the two algorithms so that overall makespan of tasks on the machines is
minimized. The tasks are scheduled on machines in either space shared or time shared manner. We
evaluate our provisioning heuristics using a cloud simulator, called CloudSim. We also compared our
approach to the statistics obtained when provisioning of resources was done in First-Cum-First-
Serve(FCFS) manner. The experimental results show that overall makespan of tasks on given set of VMs
minimizes significantly in different scenarios.
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 that ensuing generation of computation. In all probability folks can have everything they need on the cloud. Cloud computing provides resources to shopper on demand. The resources also are code package resources or hardware resources. Cloud computing architectures unit distributed, parallel and serves the requirements of multiple purchasers in various things. This distributed style deploys resources distributive to deliver services with efficiency to users in various geographical channels. Purchasers in a very distributed setting generate request haphazardly in any processor. So the most important disadvantage of this randomness is expounded to task assignment. The unequal task assignment to the processor creates imbalance i.e., variety of the processors sq. measure over laden and many of them unit of measurement to a lower place loaded. The target of load equalisation is to transfer the load from over laden technique to a lower place loaded technique transparently. Load equalisation is one altogether the central issues in cloud computing. To comprehend high performance, minimum interval and high resource utilization relation we want to transfer the tasks between nodes in cloud network. Load equalisation technique is utilized to distribute tasks from over loaded nodes to a lower place loaded or idle nodes. In following sections we have a tendency to tend to stand live discuss concerning cloud computing, load equalisation techniques and additionally the planned work of our load equalisation system. Proposed load equalisation rule is simulated on Cloud Analyst toolkit. Performance is analyzed on the parameters of overall interval, knowledge transfer, average knowledge center mating time and total value of usage. Results area unit compared with 3 existing load equalisation algorithms specifically spherical Robin, Equally unfold Current Execution Load, and Throttled. Results on the premise of case studies performed shows additional knowledge transfer with minimum interval.
Energy-aware Load Balancing and Application Scaling for the Cloud Ecosystem1crore projects
IEEE PROJECTS 2015
1 crore projects is a leading Guide for ieee Projects and real time projects Works Provider.
It has been provided Lot of Guidance for Thousands of Students & made them more beneficial in all Technology Training.
Dot Net
DOTNET Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
Java Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
ECE IEEE Projects 2015
1. Matlab project
2. Ns2 project
3. Embedded project
4. Robotics project
Eligibility
Final Year students of
1. BSc (C.S)
2. BCA/B.E(C.S)
3. B.Tech IT
4. BE (C.S)
5. MSc (C.S)
6. MSc (IT)
7. MCA
8. MS (IT)
9. ME(ALL)
10. BE(ECE)(EEE)(E&I)
TECHNOLOGY USED AND FOR TRAINING IN
1. DOT NET
2. C sharp
3. ASP
4. VB
5. SQL SERVER
6. JAVA
7. J2EE
8. STRINGS
9. ORACLE
10. VB dotNET
11. EMBEDDED
12. MAT LAB
13. LAB VIEW
14. Multi Sim
CONTACT US
1 CRORE PROJECTS
Door No: 214/215,2nd Floor,
No. 172, Raahat Plaza, (Shopping Mall) ,Arcot Road, Vadapalani, Chennai,
Tamin Nadu, INDIA - 600 026
Email id: 1croreprojects@gmail.com
website:1croreprojects.com
Phone : +91 97518 00789 / +91 72999 51536
Partitioning based Approach for Load Balancing Public CloudIJERA Editor
Load Balancing Model Based on Cloud Partitioning for the Public Cloud environment has an important impact
on the performance of network load. A cloud computing system which does not use load balancing has
numerous drawbacks. Now-a-days the usage of internet and related resources has increased widely. Due to this
there is tremendous increase in workload. So there is uneven distribution of this workload which results in
server overloading and may crash. In such systems the resources are not optimally used. Due to this the
performance degrades and efficiency reduces. Cloud computing efficient and improves user satisfaction. This
project is a better load balance model for public cloud based on the cloud partitioning concept with a switch
mechanism to choose different strategies for different situations. The algorithm applies the game theory for load
balancing strategy to improve the efficiency in the public cloud environment.
LOAD BALANCING ALGORITHM TO IMPROVE RESPONSE TIME ON CLOUD COMPUTINGijccsa
Load balancing techniques in cloud computing can be applied at different levels. There are two main
levels: load balancing on physical server and load balancing on virtual servers. Load balancing on a
physical server is policy of allocating physical servers to virtual machines. And load balancing on virtual
machines is a policy of allocating resources from physical server to virtual machines for tasks or
applications running on them. Depending on the requests of the user on cloud computing is SaaS (Software
as a Service), PaaS (Platform as a Service) or IaaS (Infrastructure as a Service) that has a proper load
balancing policy. When receiving the task, the cloud data center will have to allocate these tasks efficiently
so that the response time is minimized to avoid congestion. Load balancing should also be performed
between different datacenters in the cloud to ensure minimum transfer time. In this paper, we propose a
virtual machine-level load balancing algorithm that aims to improve the average response time and
average processing time of the system in the cloud environment. The proposed algorithm is compared to the
algorithms of Avoid Deadlocks [5], Maxmin [6], Throttled [8] and the results show that our algorithms
have optimized response times.
TASK SCHEDULING USING AMALGAMATION OF MET HEURISTICS SWARM OPTIMIZATION ALGOR...Journal For Research
Cloud Computing is the latest networking technology and also popular archetype for hosting the application and delivering of services over the network. The foremost technology of the cloud computing is virtualization which enables of building the applications, dynamically sharing of resources and providing diverse services to the cloud users. With virtualization, a service provider can guarantee Quality of Service to the user at the same time as achieving higher server consumption and energy competence. One of the most important challenges in the cloud computing environment is the VM placemnt and task scheduling problem. This paper focus on Metaheuristic Swarm Optimisation Algorithms(MSOA) deals with the problem of VM placement and Task scheduling in cloud environment. The MSOA is a simple parallel algorithm that can be applied in different ways to resolve the task scheduling problems. The proposed algorithm is considered an amalgamation of the SO algorithm and the Cuckoo search (CS) algorithm; called MSOACS. The proposed algorithm is evaluated using Cloudsim Simulator. The results proves the reduction of the makespan and increase the utilization ratio of the proposed MSOACS algorithm compared with SOA algorithms and Randomised Allocation Allocation (RA).
Scheduling Divisible Jobs to Optimize the Computation and Energy Costsinventionjournals
ABSTRACT : The important challenge in cloud computing environment is to design a scheduling strategy to handle jobs, and to process them in a heterogeneous environment with shared data centers. In this paper, we attempt to investigate a new analytical framework model that enables an existing private cloud data-center for scheduling jobs and minimizing the overall computation and energy cost together. Our model is based on Divisible Load Theory (DLT) model to derive closed-form solution for the load fractions to be assigned to each machines considering computation and energy cost. Our analysis also attempts to schedule the jobs such a way that cloud provider can gain maximum benefit for his service and Quality of Service (QoS) requirement user’s job. Finally, we quantify the performance of the strategies via rigorous simulation studies.
Batch Thermodynamics and Kinetic Study for Removal of Cationic Dye from Aqueo...IOSR Journals
Abstract: Model using Iraqi limestone as an adsorbent has been investigated. The influences of equilibrium time, limestone dosage, limestone particle size, pH of solution, adsorption isotherms and temperature on adsorption performance have been experimentally verified by a batch method. The adsorbent used in this study exhibited a good adsorption potential at initial pH 10, temperatures 28ºC, particle size 75 μm, limestone dosage 0.1g for equilibrium time 140 min. The removal efficiency and distribution coefficient have also been determined for the adsorption system as a function of dosage of the adsorbent. The experimental results are described by Langmuir and Freundlich isotherm models. Experimental results show that the kinetic model of pseudo-second order provided a good description of the whole experimental data more than the kinetic of the Lagergren-first order.
Role of Educational Qualification of Consumers on Need Recognition: A Study w...IOSR Journals
Demographic variables are the most popular bases for segmenting the customer groups. One reason is that consumer needs, wants, preferences and usage rates often highly associated with demographic variables. Another is that demographic variables are easier to measure than the most of other type variables. Marketers are keenly interested in the size and growth rate of population in different cities, regions, nations; age distribution; educational levels; household patterns; and regional characteristics and movements. Because, on the basis of these measures only, marketers have to formulate their marketing strategies in order to fulfil the needs, wants and preferences of consumers. Moreover, demographic variables make known the ongoing trends, such as shifts in age, sex and income distribution that signal new business opportunities to the marketers. Demographic trends are highly reliable for the short and intermediate run. This paper, with a strong backing of literature, explains the role of educational qualification of consumers on recognizing a need for car.
Data Distribution Handling on Cloud for Deployment of Big Dataijccsa
Cloud computing is a new emerging model in the field of computer science. For varying workload Cloud computing presents a large scale on demand infrastructure. The primary usage of clouds in practice is to process massive amounts of data. Processing large datasets has become crucial in research and business environments. The big challenges associated with processing large datasets is the vast infrastructure required. Cloud computing provides vast infrastructure to store and process Big data. Vms can be provisioned on demand in cloud to process the data by forming cluster of Vms . Map Reduce paradigm can be used to process data wherein the mapper assign part of task to particular Vms in cluster and reducer combines individual output from each Vms to produce final result. we have proposed an algorithm to reduce the overall data distribution and processing time. We tested our solution in Cloud Analyst Simulation environment wherein, we found that our proposed algorithm significantly reduces the overall data processing time in cloud.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
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.
Application of selective algorithm for effective resource provisioning in clo...ijccsa
Modern day continued demand for resource hungry services and applications in IT sector has led to
development of Cloud computing. Cloud computing environment involves high cost infrastructure on one
hand and need high scale computational resources on the other hand. These resources need to be
provisioned (allocation and scheduling) to the end users in most efficient manner so that the tremendous
capabilities of cloud are utilized effectively and efficiently. In this paper we discuss a selective algorithm
for allocation of cloud resources to end-users on-demand basis. This algorithm is based on min-min and
max-min algorithms. These are two conventional task scheduling algorithm. The selective algorithm uses
certain heuristics to select between the two algorithms so that overall makespan of tasks on the machines is
minimized. The tasks are scheduled on machines in either space shared or time shared manner. We
evaluate our provisioning heuristics using a cloud simulator, called CloudSim. We also compared our
approach to the statistics obtained when provisioning of resources was done in First-Cum-First-
Serve(FCFS) manner. The experimental results show that overall makespan of tasks on given set of VMs
minimizes significantly in different scenarios.
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 that ensuing generation of computation. In all probability folks can have everything they need on the cloud. Cloud computing provides resources to shopper on demand. The resources also are code package resources or hardware resources. Cloud computing architectures unit distributed, parallel and serves the requirements of multiple purchasers in various things. This distributed style deploys resources distributive to deliver services with efficiency to users in various geographical channels. Purchasers in a very distributed setting generate request haphazardly in any processor. So the most important disadvantage of this randomness is expounded to task assignment. The unequal task assignment to the processor creates imbalance i.e., variety of the processors sq. measure over laden and many of them unit of measurement to a lower place loaded. The target of load equalisation is to transfer the load from over laden technique to a lower place loaded technique transparently. Load equalisation is one altogether the central issues in cloud computing. To comprehend high performance, minimum interval and high resource utilization relation we want to transfer the tasks between nodes in cloud network. Load equalisation technique is utilized to distribute tasks from over loaded nodes to a lower place loaded or idle nodes. In following sections we have a tendency to tend to stand live discuss concerning cloud computing, load equalisation techniques and additionally the planned work of our load equalisation system. Proposed load equalisation rule is simulated on Cloud Analyst toolkit. Performance is analyzed on the parameters of overall interval, knowledge transfer, average knowledge center mating time and total value of usage. Results area unit compared with 3 existing load equalisation algorithms specifically spherical Robin, Equally unfold Current Execution Load, and Throttled. Results on the premise of case studies performed shows additional knowledge transfer with minimum interval.
Energy-aware Load Balancing and Application Scaling for the Cloud Ecosystem1crore projects
IEEE PROJECTS 2015
1 crore projects is a leading Guide for ieee Projects and real time projects Works Provider.
It has been provided Lot of Guidance for Thousands of Students & made them more beneficial in all Technology Training.
Dot Net
DOTNET Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
Java Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
ECE IEEE Projects 2015
1. Matlab project
2. Ns2 project
3. Embedded project
4. Robotics project
Eligibility
Final Year students of
1. BSc (C.S)
2. BCA/B.E(C.S)
3. B.Tech IT
4. BE (C.S)
5. MSc (C.S)
6. MSc (IT)
7. MCA
8. MS (IT)
9. ME(ALL)
10. BE(ECE)(EEE)(E&I)
TECHNOLOGY USED AND FOR TRAINING IN
1. DOT NET
2. C sharp
3. ASP
4. VB
5. SQL SERVER
6. JAVA
7. J2EE
8. STRINGS
9. ORACLE
10. VB dotNET
11. EMBEDDED
12. MAT LAB
13. LAB VIEW
14. Multi Sim
CONTACT US
1 CRORE PROJECTS
Door No: 214/215,2nd Floor,
No. 172, Raahat Plaza, (Shopping Mall) ,Arcot Road, Vadapalani, Chennai,
Tamin Nadu, INDIA - 600 026
Email id: 1croreprojects@gmail.com
website:1croreprojects.com
Phone : +91 97518 00789 / +91 72999 51536
Partitioning based Approach for Load Balancing Public CloudIJERA Editor
Load Balancing Model Based on Cloud Partitioning for the Public Cloud environment has an important impact
on the performance of network load. A cloud computing system which does not use load balancing has
numerous drawbacks. Now-a-days the usage of internet and related resources has increased widely. Due to this
there is tremendous increase in workload. So there is uneven distribution of this workload which results in
server overloading and may crash. In such systems the resources are not optimally used. Due to this the
performance degrades and efficiency reduces. Cloud computing efficient and improves user satisfaction. This
project is a better load balance model for public cloud based on the cloud partitioning concept with a switch
mechanism to choose different strategies for different situations. The algorithm applies the game theory for load
balancing strategy to improve the efficiency in the public cloud environment.
LOAD BALANCING ALGORITHM TO IMPROVE RESPONSE TIME ON CLOUD COMPUTINGijccsa
Load balancing techniques in cloud computing can be applied at different levels. There are two main
levels: load balancing on physical server and load balancing on virtual servers. Load balancing on a
physical server is policy of allocating physical servers to virtual machines. And load balancing on virtual
machines is a policy of allocating resources from physical server to virtual machines for tasks or
applications running on them. Depending on the requests of the user on cloud computing is SaaS (Software
as a Service), PaaS (Platform as a Service) or IaaS (Infrastructure as a Service) that has a proper load
balancing policy. When receiving the task, the cloud data center will have to allocate these tasks efficiently
so that the response time is minimized to avoid congestion. Load balancing should also be performed
between different datacenters in the cloud to ensure minimum transfer time. In this paper, we propose a
virtual machine-level load balancing algorithm that aims to improve the average response time and
average processing time of the system in the cloud environment. The proposed algorithm is compared to the
algorithms of Avoid Deadlocks [5], Maxmin [6], Throttled [8] and the results show that our algorithms
have optimized response times.
TASK SCHEDULING USING AMALGAMATION OF MET HEURISTICS SWARM OPTIMIZATION ALGOR...Journal For Research
Cloud Computing is the latest networking technology and also popular archetype for hosting the application and delivering of services over the network. The foremost technology of the cloud computing is virtualization which enables of building the applications, dynamically sharing of resources and providing diverse services to the cloud users. With virtualization, a service provider can guarantee Quality of Service to the user at the same time as achieving higher server consumption and energy competence. One of the most important challenges in the cloud computing environment is the VM placemnt and task scheduling problem. This paper focus on Metaheuristic Swarm Optimisation Algorithms(MSOA) deals with the problem of VM placement and Task scheduling in cloud environment. The MSOA is a simple parallel algorithm that can be applied in different ways to resolve the task scheduling problems. The proposed algorithm is considered an amalgamation of the SO algorithm and the Cuckoo search (CS) algorithm; called MSOACS. The proposed algorithm is evaluated using Cloudsim Simulator. The results proves the reduction of the makespan and increase the utilization ratio of the proposed MSOACS algorithm compared with SOA algorithms and Randomised Allocation Allocation (RA).
Scheduling Divisible Jobs to Optimize the Computation and Energy Costsinventionjournals
ABSTRACT : The important challenge in cloud computing environment is to design a scheduling strategy to handle jobs, and to process them in a heterogeneous environment with shared data centers. In this paper, we attempt to investigate a new analytical framework model that enables an existing private cloud data-center for scheduling jobs and minimizing the overall computation and energy cost together. Our model is based on Divisible Load Theory (DLT) model to derive closed-form solution for the load fractions to be assigned to each machines considering computation and energy cost. Our analysis also attempts to schedule the jobs such a way that cloud provider can gain maximum benefit for his service and Quality of Service (QoS) requirement user’s job. Finally, we quantify the performance of the strategies via rigorous simulation studies.
Batch Thermodynamics and Kinetic Study for Removal of Cationic Dye from Aqueo...IOSR Journals
Abstract: Model using Iraqi limestone as an adsorbent has been investigated. The influences of equilibrium time, limestone dosage, limestone particle size, pH of solution, adsorption isotherms and temperature on adsorption performance have been experimentally verified by a batch method. The adsorbent used in this study exhibited a good adsorption potential at initial pH 10, temperatures 28ºC, particle size 75 μm, limestone dosage 0.1g for equilibrium time 140 min. The removal efficiency and distribution coefficient have also been determined for the adsorption system as a function of dosage of the adsorbent. The experimental results are described by Langmuir and Freundlich isotherm models. Experimental results show that the kinetic model of pseudo-second order provided a good description of the whole experimental data more than the kinetic of the Lagergren-first order.
Role of Educational Qualification of Consumers on Need Recognition: A Study w...IOSR Journals
Demographic variables are the most popular bases for segmenting the customer groups. One reason is that consumer needs, wants, preferences and usage rates often highly associated with demographic variables. Another is that demographic variables are easier to measure than the most of other type variables. Marketers are keenly interested in the size and growth rate of population in different cities, regions, nations; age distribution; educational levels; household patterns; and regional characteristics and movements. Because, on the basis of these measures only, marketers have to formulate their marketing strategies in order to fulfil the needs, wants and preferences of consumers. Moreover, demographic variables make known the ongoing trends, such as shifts in age, sex and income distribution that signal new business opportunities to the marketers. Demographic trends are highly reliable for the short and intermediate run. This paper, with a strong backing of literature, explains the role of educational qualification of consumers on recognizing a need for car.
Modeling and Analysis for Cutting Temperature in Turning of Aluminium 6063 Us...IOSR Journals
Deviation in machining process due to the temperature influence, cutting force, tool wear leads to
highly inferior quality of finished product, especially in high speed machining operations where product quality
and physical dimensions seems to be meticulous. Moreover, temperature is a significant noise parameter which
directly affects the cutting tool and work piece. Hence the aim of this project work is to study the machining
effect on 6063 Aluminium alloy at varies combinations of process parameters such as speed, feed rate and depth
of cut; and also to determine the effect of those parameters over the quality of finished product. A L27
Orthogonal Array (OA) based Design of Experiments (DOE) approach and Response Surface Methodology
(RSM) was used to analyse the machining effect on work material in this study. Using the practical data
obtained, a mathematical model was developed to predict the temperature influence and surface quality of
finished product. The ultimate goal of the study is to optimize the machining parameters for temperature
minimization in machining zone and improvement in surface finish.
Mobile computing application risks in ZimbabweIOSR Journals
Abstract: Mobile technology has now become the order of the day. Everyone seems to own one or more mobile
devices. Everyone is so excited because this has made life easier for a lot of people. Though mobile technology
has brought many benefits to people’s lives, its application has some risks that come with it. This paper
therefore looked at mobile computing application areas in Zimbabwe, the risks brought about by mobile
computing application in Zimbabwe and how mobile computing application risks are mitigated in a developing
country like Zimbabwe where the technology level seems to be behind. The study findings showed that mobile
application areas in Zimbabwe include paying bills, social networking and playing games. Most Zimbabweans
revealed that they do not know how to mitigate mobile computing application risks. The study finally
recommended that mobile computing application risks should be taught in schools as well as in televisions and
radios so that everyone is aware of them.
Keywords: application risks, mobile computing, mobile device,mobile technology
Study of Various Histogram Equalization TechniquesIOSR Journals
Abstract: Histogram equalization (HE) works well on single channel images for contrast enhancement. However, the technique used is ineffective on multiple channel images. So, it is not suitable for consumer electronic products, where preserving the original brightness is necessary in order not to introduce unnecessary visual deterioration. Bi-histogram equalization (BHE) has been developed and it is analyzed mathematically.BHE separates the input image’s histogram into two, based on its mean before equalizing them independently so that it can preserve the original brightness up to certain extends. Recursive Mean-Separate Histogram Equalization (RMSHE) is another technique to provide better and scalable brightness preservation for gray scale and color images. While the separation is done only once in BHE, RMSHE performs the separation recursively based on their respective mean. It is analyzed mathematically that the output images mean brightness will converge to the input images mean brightness as the number of recursive mean separation increases. The recursive nature of RMSHE also allows scalable brightness preservation, which is very useful in consumer electronics. Finally a comparative study was made to analyze all the above methods using gray scale and color images. Keywords: Bi-histogram equalization, histogram equalization, scalable brightness preservation, recursive mean-separate
Effect of Regular Exercise on Prolactin Secretion: A Pilot StudyIOSR Journals
Abstract:
Introduction: Evidence suggested that exercise may affects release of prolactin hormone. Participation in
exercise may increase secretion of prolactin hormone and may give sharp decrease in secretion of prolactin and
not only that published work demanded that there is no effect of exercise on secretion of prolactin. In this
context the researcher intend to know whether exercise affects positively or negatively or not on the release of
prolactin hormone. Aim: Determine whether participation in exercise may increase the level of secretion of
prolactine hormone or not. Method: Only two female students 29 years aged were participated in this study.
They were regularly practiced yogic asana and pranayam for 1 hour per day in the evening, 6 days per week,
for 8 weeks. The level of prolactin hormone was assessed by CLI method. In the present study all the
measurements were done at the baseline and 8 weeks of exercise training. Simple percentage calculated from
the mean value to see the quantitative changes in secretion of prolactin due to participation in the exercise
training. Result: Pre test mean was 5.80 and post test mean was 17.63 which imply that 203.96% increase in
secretion of prolactin significantly. Discussion and Conclusion: Level of secretion increased may be due to
multiple neural pathways that influence PRL secretion converges on the hypothalamus from other parts of the
brain; the effect of exercise on the secretion of PRL may also reflect the action of different neural inputs on the
activity of the hypothalamic–pituitary axis.
Key word: Exercise; Prolactin.
To Study the Efficacy of Electromyographic Biofeedback Training on Dynamic Eq...IOSR Journals
Abstract: Cerebral palsy (CP) is caused by static lesion to a developing nervous system that primarily affects
motor function. Spastic motor involvement is characteristic of most of these individual.Dynamicequinus is a
common deformity that worsens the ambulatory ability of both diplegic and hemiplegic conditions. The use of
electromyographic (EMG) biofeedback has been suggested as a training tool to improve the ability to increase
activation of weak and partially paralyzed muscles and to decrease the activation of muscles affected by spasm
or spasticity without regard to specific diagnosis. However, very few studies have reported the effects of EMG
biofeedback on ankle function among children with spastic cerebral palsy .Objectives of the study was to
increase the activation of tibialis anterior and to improve the functional ambulation.40 subjects were made part
of the study on the basis of inclusion and exclusion criteria divided into two groups group A and B.Group A
received traditional physical therapy exercises and electromypgraphic biofeedback and group B received only
exercise program.The treatment duration was for 4weeks 3 sessions a week.The results were analysed using
statistical tests that were paired and unpaired t-test and mannwhitneytest.The results showed significant
improvement in the pre and post treatment.The conclusion of the study lended a favourable outlook to use
biofeedback training in treatment of CP children, to improve functional ambulation and gait. Keywords: Cerebral palsy, dynamic equinus deformity, gait, electromyography, biofeedback.
ieee standard base paper.-Load balancing in the cloud computing environment has an important impact on the performance. Good load balancing makes cloud computing more efficient and improves user satisfaction. This article introduces a better load balance model for the public cloud based on the cloud partitioning concept with a switch mechanism to choose different strategies for different situations. The algorithm applies the game theory to the load balancing strategy to improve the efficiency in the public cloud environment.
A Novel Switch Mechanism for Load Balancing in Public CloudIJMER
In cloud computing environment, one of the core design principles is dynamic scalability,
which guarantees cloud storage service to handle the growing amounts of application data in a flexible
manner or to be readily enlarged. By integrating several private and public cloud services, the hybrid
clouds can effectively provide dynamic scalability of service and data migration. A load balancing is a
method of dividing computing loads among numerous hardware resources. Due to unpredictable job
arrival pattern and the capacities of the nodes in cloud differ for the load balancing problem. In this load
control is very crucial to improve system performance and maintenance. This paper presents a switch
mechanism for load balancing in cloud computing. The load balancing model given in this work is aimed
at the public cloud which has numerous nodes with distributed computing resources in many different
geographical areas. Thus, this model divides the public cloud environment into several cloud partitions.
When the cloud environment is very large and complex, these divisions simplify the load balancing. The
cloud environment has a main controller that chooses the suitable partitions for arriving jobs while the
balancer for each cloud partition chooses the best load balancing strategy
Cloud computing is that ensuing generation of computation. In all probability folks can have everything they need on the cloud. Cloud computing provides resources to shopper on demand. The resources also are code package resources or hardware resources. Cloud computing architectures unit distributed, parallel and serves the requirements of multiple purchasers in various things. This distributed style deploys resources distributive to deliver services with efficiency to users in various geographical channels. Purchasers in a very distributed setting generate request haphazardly in any processor. So the most important disadvantage of this randomness is expounded to task assignment. The unequal task assignment to the processor creates imbalance i.e., variety of the processors sq. measure over laden and many of them unit of measurement to a lower place loaded. The target of load equalisation is to transfer the load from over laden technique to a lower place loaded technique transparently. Load equalisation is one altogether the central issues in cloud computing. To comprehend high performance, minimum interval and high resource utilization relation we want to transfer the tasks between nodes in cloud network. Load equalisation technique is utilized to distribute tasks from over loaded nodes to a lower place loaded or idle nodes. In following sections we have a tendency to tend to stand live discuss concerning cloud computing, load equalisation techniques and additionally the planned work of our load equalisation system. Proposed load equalisation rule is simulated on Cloud Analyst toolkit. Performance is analyzed on the parameters of overall interval, knowledge transfer, average knowledge center mating time and total value of usage. Results area unit compared with 3 existing load equalisation algorithms specifically spherical Robin, Equally unfold Current Execution Load, and Throttled. Results on the premise of case studies performed shows additional knowledge transfer with minimum interval.
Efficient load rebalancing for distributed file system in CloudsIJERA Editor
Cloud computing is an upcoming era in software industry. It’s a very vast and developing technology.
Distributed file systems play an important role in cloud computing applications based on map reduce
techniques. While making use of distributed file systems for cloud computing, nodes serves computing and
storage functions at the same time. Given file is divided into small parts to use map reduce algorithms in
parallel. But the problem lies here since in cloud computing nodes may be added, deleted or modified any time
and also operations on files may be done dynamically. This causes the unequal load distribution of load among
the nodes which leads to load imbalance problem in distributed file system. Newly developed distributed file
system mostly depends upon central node for load distribution but this method is not helpful in large-scale and
where chances of failure are more. Use of central node for load distribution creates a problem of single point
dependency and chances of performance of bottleneck are more. As well as issues like movement cost and
network traffic caused due to migration of nodes and file chunks need to be resolved. So we are proposing
algorithm which will overcome all these problems and helps to achieve uniform load distribution efficiently. To
verify the feasibility and efficiency of our algorithm we will be using simulation setup and compare our
algorithm with existing techniques for the factors like load imbalance factor, movement cost and network traffic.
Modified Active Monitoring Load Balancing with Cloud Computingijsrd.com
Cloud computing is internet-based computing in which large groups of remote servers are networked to allow the centralized data storage, and online access to computer services or resources. Load Balancing is essential for efficient operations in distributed environments. As Cloud Computing is growing rapidly and clients are demanding more services and better results, load balancing for the Cloud has become a very interesting and important research area. In the absence of proper load balancing strategy/technique the growth of CC will never go as per predictions. The main focus of this paper is to verify the approach that has been proposed in the model paper [3]. An efficient load balancing algorithm has the ability to reduce the data center processing time, overall response time and to cope with the dynamic changes of cloud computing environments. The traditional load balancing Active Monitoring algorithm has been modified to achieve better data center processing time and overall response time. The algorithm presented in this paper efficiently distributes the requests to all the VMs for their execution, considering the CPU utilization of all VMs.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
Tremendous usage of internet has made huge data on the network, without compromising on the
performance of network the end-users must obtain best service. As cloud provides different services on
leasing basis many companies are migrating from their own Infrastructure to cloud,This migration should
not compromise on performance of the cloud, The performance of the cloud can be improved by having
excellent load balancing strategy such that the end user is satisfied. The paper reveals the method by which
a cloud can be partitioned and a study of different algorithm with comparative study to balance the
dynamic load. The comparative study between Ant Colony and Honey Bee algorithm gives the result which
algorithm is optimal in normal load condition also the simplest round robin algorithm is applied when the
partition are in Idle state
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.
The Grouping of Files in Allocation of Job Using Server Scheduling In Load Ba...iosrjce
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.
An Adaptive Load Sharing Algorithm for Heterogeneous Distributed SystemIJORCS
Due to the restriction of designing faster and faster computers, one has to find the ways to maximize the performance of the available hardware. A distributed system consists of several autonomous nodes, where some nodes are busy with processing, while some nodes are idle without any processing. To make better utilization of the hardware, the tasks or load of the overloaded node will be sent to the under loaded node that has less processing weight to minimize the response time of the tasks. Load balancing is a tool used effectively for balancing the load among the systems. Dynamic load balancing takes into account of the current system state for migration of the tasks from heavily loaded nodes to the lightly loaded nodes. In this paper, we devised an adaptive load-sharing algorithm to balance the load by taking into consideration of connectivity among the nodes, processing capacity of each node and link capacity.
CLOUD COMPUTING – PARTITIONING ALGORITHM AND LOAD BALANCING ALGORITHMijcseit
Tremendous usage of internet has made huge data on the network, without compromising on the
performance of network the end-users must obtain best service. As cloud provides different services on
leasing basis many companies are migrating from their own Infrastructure to cloud,This migration should
not compromise on performance of the cloud, The performance of the cloud can be improved by having
excellent load balancing strategy such that the end user is satisfied. The paper reveals the method by which
a cloud can be partitioned and a study of different algorithm with comparative study to balance the
dynamic load. The comparative study between Ant Colony and Honey Bee algorithm gives the result which
algorithm is optimal in normal load condition also the simplest round robin algorithm is applied when the
partition are in Idle state
CLOUD COMPUTING – PARTITIONING ALGORITHM AND LOAD BALANCING ALGORITHMijcseit
Tremendous usage of internet has made huge data on the network, without compromising on the
performance of network the end-users must obtain best service. As cloud provides different services on
leasing basis many companies are migrating from their own Infrastructure to cloud,This migration should
not compromise on performance of the cloud, The performance of the cloud can be improved by having
excellent load balancing strategy such that the end user is satisfied. The paper reveals the method by which
a cloud can be partitioned and a study of different algorithm with comparative study to balance the
dynamic load. The comparative study between Ant Colony and Honey Bee algorithm gives the result which
algorithm is optimal in normal load condition also the simplest round robin algorithm is applied when the
partition are in Idle state
The Concept of Load Balancing Server in Secured and Intelligent NetworkIJAEMSJORNAL
Hundreds and thousands of data packets are routed every second by computer networks which are complex systems. The data should be routed efficiently to handle large amounts of data in network. A core networking solution which is responsible for distribution of incoming traffic among servers hosting the same content is load balancing. For example, if there are ten servers within a network and two of them are doing 95% of the work, the network is not running very efficiently. If each server was handling about 10% of the traffic, the network would run much faster.Networks get more efficient with the help of Load balancing. The traffic is evenly distributed amongst the network making sure no single device is overwhelmed.When a request is balanced across multiple servers, it prevents any server from becoming a single point of failure. It improves overall availability and responsiveness. To evenly split the traffic load among several different servers web servers; often use load balancing.Load balancing requires hardware or software that divides incoming traffic amongst the available serverseither it is done on a local network or a large web server. High amount of traffic is received by a network that have one server dedicated to balance the load among other servers and devices in the network. This server is often known as load balancer. Load balancing is used by clusters or multiple computers that work together, to spread out processing jobs among the available systems.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Essentials of Automations: Optimizing FME Workflows with Parameters
C017311316
1. IOSR Journal of Computer Engineering (IOSR-JCE)
e-ISSN: 2278-0661,p-ISSN: 2278-8727, Volume 17, Issue 3, Ver. 1 (May – Jun. 2015), PP 13-16
www.iosrjournals.org
DOI: 10.9790/0661-17311316 www.iosrjournals.org 13 | Page
Load Rebalancing for Distributed Hash Tables in Cloud
Computing
Eng. Ahmed Hassan A/Elmutaal algonsol.me86@gmial.com ,
Dr. Amin Babiker A/Nabi Mustafa amin31766@gmail.com
Abstract:In cloud computing applications are provided and managed by the cloud server and data is also
stored remotely in cloud configuration. As Cloud Computing is growing rapidly and clients are demanding
more services and better results, load balancing for the Cloud has become a very interesting and important
research area. Load balancing ensures that all the processor in the system or every node in the network does
approximately the equal amount of work at any instant of time. In this paper, a fully distributed load
rebalancing algorithm is presented to cope with the load balance problem. Our algorithm is compared against a
centralized approach in a production system and a competing distributed solution presented in the literature.
I. Introduction
In cloud computing, load balancing is required to distribute the dynamic local workload evenly across
all the nodes. It helps to achieve a high user satisfaction and resource utilization ratio by ensuring an efficient
and fair allocation of every computing resource. Proper load balancing aids in minimizing resource
consumption, implementing fail-over, enabling scalability, avoiding bottlenecks and over provisioning. There
are mainlytwotypesofloadbalancingalgorithms: In staticalgorithmthetrafficdividedevenly amongtheservers.This
algorithmrequiresapriorknowledge of systemresources ,sothatthe decision of shiftingoftheloaddoes
notdependonthecurrentstateofsystem .Static algorithms roper in thesystemwhichhaslowvariation in load. In
dynamic algorithm the lightest server in the whole network or system is searched and preferred for balancing a
load. For this real time communication with network is needed which can increase the traffic in the system. Here
current state of the system is used to make decisions to manage the load. Load balancing based on Cloud
Partitioning There are several cloud computing services with this work focused on a public cloud. A public
cloud is based on the standard cloud computing model, with service provided byaservice provider. A large
public cloud will include many nodes and the nodes in different geographical locations. Cloud partitioning is
used to manage this large cloud.
EXISTING SYSTEM: However, recent experience concludes that when the number of Storage nodes, the
number of files and the number of accesses to files increase linearly, the central nodes become a performance
bottleneck, as they are unable to accommodate a large number of file accesses due to clients and Map Reduce
applications. Thus, depending on the central nodes to tackle the load imbalance problem exacerbate their heavy
loads. Even with the latest development in distributed file systems, the central nodes may still be overloaded.
PROPOSED SYSTEM: In this paper, we are interested in studying the load rebalancing problem in distributed
file systems specialized for large-scale, dynamic and data-intensive clouds. (The terms ―rebalance‖ and
―balance‖ is interchangeable in this paper.) Such a large-scale cloud has hundreds or thousands of nodes (and
may reach tens of thousands in the future). Our objective is to allocate the chunks of files as uniformly as
possible among the nodes such that no node manages an excessive number of chunks.
Figure (1): system architecture
2. Load Rebalancing For Distributed Hash Tables In Cloud computing
DOI: 10.9790/0661-17311316 www.iosrjournals.org 14 | Page
II. Methodology
Methodology:
There are many nodes in a public cloud which are at different locations. The cloud has a main controller
(MC) which chooses the suitable partitions for arriving jobs. The appropriate partition is selected by using best
load balancing strategy. All the status information is gathered and analyzed by main controller and balancers.
They also perform the load balancing operations. The system status then provides a basis for choosing the right
load balancing strategy. In this paper we will use approximately 4 different servers, which are partitioned into
small clouds called balancers (each balancer will have some servers). Cloud Service Provider (CSP) is used to
handle a Main cloud (which is made up of small Clouds) called Main Controller or Controller main. Client
interacts with cloud using a web application called client Site.
1. Uploads File:
When client upload files it will be stored in the server. The cloud will take care that it will be loaded into
the server which has minimum load. The status of every server is updated by the balancers and depending on the
status the partition is selected.
Figure (2):Upload file
2. Download File:
The servers will have following states for user download file:Idle, Normal, Overloaded. For overloaded
condition another partition is searched.
Partition status can be divided into three types:
(1) Idle: When the load exceeds alpha
(2) Normal: When the load exceeds beta
(3) Overload: When the load exceeds gamma
The parameters alpha, beta, and gamma are set by the cloud partition balancers.
Define a load parameter set : F = {F1 ;F2;…….;Fm} with each Fi(1 ≥i ≤m;Fi€ [0,1])
parameter being either static or dynamic . m represents the total number of the parameter .
Then compute the load degree as :
Load degree (N) = ∑i=1
m
=1αifi
Calculate the average cloud partition degree from the node load degree statistics as:
Load degree avg= = ∑i=1
m
=load degree (Ni)
N
Where :
1) Load is Idle when : load degree (N) = 0;
2) Load is Normalwhen : 0> load degree (N) ≤ load degreehigh ;
3) Load is Overloadwhen : load degree high≤ load degree (N)
3. System Configuration:-
We use this flowing server with (sw & hw) as billow:-
3. Load Rebalancing For Distributed Hash Tables In Cloud computing
DOI: 10.9790/0661-17311316 www.iosrjournals.org 15 | Page
H/W System Configuration:-
Processor Core i3
Speed 2.0 GHz
RAM 2.0GB (min)
Hard Disk 120 GB
Key Board Standard
Mouse Standard
Monitor Standard
S/W System Configuration:-
Operating System Windows 7
Application Server Tomcat 8.0 x
Front End HTML, JAVA, JSP
Scripts JavaScript
Server side Script Java Server Pages
Database My sql
Database Connectivity JDBC
4. Best Partition Searching Algorithm:
The first step to download files from cloud computing by client is to send a request to (MC) which located in
balancer server. Scantly, the balancer server chooses a cloud partition state, where by download speed state has
three states (Idle, Normal and overload). In case of overload state, the balancer returns the request to recheck
for anew partition, however in case of the other two states (Idle and Normal) the Jobs arrive at the cloud
partition balancer and then Assign jobs to particular nodes according to the strategy in order to complete the
download files.
Below are data flow charts that describe the process:
Figure (3): Process flow chart
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DOI: 10.9790/0661-17311316 www.iosrjournals.org 16 | Page
III. Result:
After implementing a small cloud system by using the two opting (with balancer server & without
balancer server) the result was as follows:-
System load
Figure (4): Unbalanced VS Balanced Systems
The Above Mention curve is the result of applying the equations of the load degree by its different values
(alpha, beta, and gamma) .the average response time reached the peak when the balancer server been used ,
additionally the download speedwas very good , less load , more capacity been acquired and the QOS (quality
of service ) has improved which eventually increases the satisfaction level of the client.
Having said the above, without balancer server option the result was hasn’t client satisfaction.
IV. Conclusion:
In line with the objective of this paper which is to balancer load on cloud , experimentally the uses of
balancer server has improve the performance of the cloud computing system significantly compere with (
without using balancer server ) , hence we do recommend to use the balancer server in all cloud systems for
biter QOS.
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Averageresponsetime
Without load
balancing With load
balancing