This document compares the performance of two dynamic load balancing algorithms - the Honey Bee algorithm and the Throttled Load Balancing algorithm - in a cloud computing environment. It first describes both algorithms and other related concepts. It then discusses results from simulations run using the CloudAnalyst tool. The simulations show that the Honey Bee algorithm has lower average, minimum, and maximum response times compared to the Throttled algorithm. Additionally, the Honey Bee algorithm results in lower data center processing times and costs. Therefore, the document concludes the Honey Bee algorithm performs better than the Throttled algorithm for load balancing in cloud computing.
A load balancing model based on cloud partitioning for the public cloud. ppt Lavanya Vigrahala
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.
Load balancing In cloud - In a semi distributed systemAchal Gupta
Load Balancing in Cloud
What is load balancing in Cloud in semi distributed system and why it is better than a centralized system and distributed system
Base paper ppt-. A load balancing model based on cloud partitioning for the ...Lavanya Vigrahala
A load balancing model based on cloud partitioning for the public cloud. -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.
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.
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.
Live virtual machine migration based on future prediction of resource require...Tapender Yadav
This document gives the brief description of the work done during the Summer Internship at Institute for Development and Research in Banking Technology (IDRBT), Hyderabad. The project was undertaken from May 2014 - July 2014 under the exemplary guidance of Dr. G. R. Gangadharan, Asst. Professor, IDRBT, Hyderabad.
In the FACTS-based transmission line, if the fault does not include FACTS device, then the impedance calculation is like an ordinary transmission line, and when the fault includes FACTS, then the impedance calculation accounts for the impedances introduced by FACTS device.
Load Balancing from the Cloud - Layer 7 Aware SolutionImperva Incapsula
Incapsula's Layer 7 Load Balancing & Failover service enables organizations to replace their costly appliances with an enterprise-grade cloud-based solution.
The service supports all in-data center and cross-data center high availability scenarios.
Incapsula also provides real-time health monitoring to ensure that traffic is always routed to a viable web server.
Virtual Machine Migration and Allocation in Cloud Computing: A Reviewijtsrd
Cloud computing is an emerging computing technology that maintains computational resources on large data centers and accessed through internet, rather than on local computers. VM migration provides the capability to balance the load, system maintenance, etc. Virtualization technology gives power to cloud computing. The virtual machine migration techniques can be divided into two categories that is pre copy and post copy approach. The process to move running applications or VMs from one physical machine to another is known as VM migration. In migration process the processor state, storage, memory and network connection are moved from one host to another.. Two important performance metrics are downtime and total migration time that the users care about most, because these metrics deals with service degradation and the time during which the service is unavailable. This paper focus on the analysis of live VM migration Techniques in cloud computing. Khushbu Singh Chandel | Dr. Avinash Sharma "Virtual Machine Migration and Allocation in Cloud Computing: A Review" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-1 , December 2019, URL: https://www.ijtsrd.com/papers/ijtsrd29556.pdfPaper URL: https://www.ijtsrd.com/computer-science/computer-network/29556/virtual-machine-migration-and-allocation-in-cloud-computing-a-review/khushbu-singh-chandel
Php project aim is to develop dynamic and attractive web application as per user requirement. you can easily develop web application with our guidance............
for more details..... contact us..........
softroniics
calicut || palakkad || coimbatore
9037061113 , 9037291113
www.softroniics.in
Hybrid Scheduling Algorithm for Efficient Load Balancing In Cloud ComputingEswar Publications
In cloud computing environment, various users send requests for the transmission of data for different demands. The access to different number of users increase load on the cloud servers. Due to this, the cloud server does not provide best efficiency. To provide best efficiency, load has to be balanced. The highlight of this work is the division of different jobs into tasks. The job dependency checking is done on the basis of directed acyclic graph. The dependency checking the make span has to be created on the basis of first come first serve and priority based scheduling algorithms. In this paper, each scheduling algorithm has been implemented sequentially and the hybrid algorithm (round robin and priority based) has also been compared with other scheduling algorithms.
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.
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.
A load balancing model based on cloud partitioning for the public cloud. ppt Lavanya Vigrahala
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.
Load balancing In cloud - In a semi distributed systemAchal Gupta
Load Balancing in Cloud
What is load balancing in Cloud in semi distributed system and why it is better than a centralized system and distributed system
Base paper ppt-. A load balancing model based on cloud partitioning for the ...Lavanya Vigrahala
A load balancing model based on cloud partitioning for the public cloud. -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.
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.
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.
Live virtual machine migration based on future prediction of resource require...Tapender Yadav
This document gives the brief description of the work done during the Summer Internship at Institute for Development and Research in Banking Technology (IDRBT), Hyderabad. The project was undertaken from May 2014 - July 2014 under the exemplary guidance of Dr. G. R. Gangadharan, Asst. Professor, IDRBT, Hyderabad.
In the FACTS-based transmission line, if the fault does not include FACTS device, then the impedance calculation is like an ordinary transmission line, and when the fault includes FACTS, then the impedance calculation accounts for the impedances introduced by FACTS device.
Load Balancing from the Cloud - Layer 7 Aware SolutionImperva Incapsula
Incapsula's Layer 7 Load Balancing & Failover service enables organizations to replace their costly appliances with an enterprise-grade cloud-based solution.
The service supports all in-data center and cross-data center high availability scenarios.
Incapsula also provides real-time health monitoring to ensure that traffic is always routed to a viable web server.
Virtual Machine Migration and Allocation in Cloud Computing: A Reviewijtsrd
Cloud computing is an emerging computing technology that maintains computational resources on large data centers and accessed through internet, rather than on local computers. VM migration provides the capability to balance the load, system maintenance, etc. Virtualization technology gives power to cloud computing. The virtual machine migration techniques can be divided into two categories that is pre copy and post copy approach. The process to move running applications or VMs from one physical machine to another is known as VM migration. In migration process the processor state, storage, memory and network connection are moved from one host to another.. Two important performance metrics are downtime and total migration time that the users care about most, because these metrics deals with service degradation and the time during which the service is unavailable. This paper focus on the analysis of live VM migration Techniques in cloud computing. Khushbu Singh Chandel | Dr. Avinash Sharma "Virtual Machine Migration and Allocation in Cloud Computing: A Review" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-1 , December 2019, URL: https://www.ijtsrd.com/papers/ijtsrd29556.pdfPaper URL: https://www.ijtsrd.com/computer-science/computer-network/29556/virtual-machine-migration-and-allocation-in-cloud-computing-a-review/khushbu-singh-chandel
Php project aim is to develop dynamic and attractive web application as per user requirement. you can easily develop web application with our guidance............
for more details..... contact us..........
softroniics
calicut || palakkad || coimbatore
9037061113 , 9037291113
www.softroniics.in
Hybrid Scheduling Algorithm for Efficient Load Balancing In Cloud ComputingEswar Publications
In cloud computing environment, various users send requests for the transmission of data for different demands. The access to different number of users increase load on the cloud servers. Due to this, the cloud server does not provide best efficiency. To provide best efficiency, load has to be balanced. The highlight of this work is the division of different jobs into tasks. The job dependency checking is done on the basis of directed acyclic graph. The dependency checking the make span has to be created on the basis of first come first serve and priority based scheduling algorithms. In this paper, each scheduling algorithm has been implemented sequentially and the hybrid algorithm (round robin and priority based) has also been compared with other scheduling algorithms.
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.
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.
Enhanced equally distributed load balancing algorithm for cloud computingeSAT Publishing House
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.
Enhanced equally distributed load balancing algorithm for cloud computingeSAT Journals
Abstract Cloud Computing as the name suggests, it is a style of computing where different users uses the resources on the go i.e. over the Internet. In the recent era, this technology has emerged as a strong option for not only large scale organizations but also for small scale organizations that only access/use the resources what they want. In recent research study, many organizations lose significant part of their revenues in handling the requests given by the clients over the web servers i.e. unable to balance the load for web servers which results in loss of data, delay in time and increased costs. This Paper gives a new enhanced load balancing algorithm by which the performance of their web application can be increased. This Algorithm works on the major drawbacks such as delay in time, response to request ratio etc.
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.
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.
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.
Load Balancing Algorithm to Improve Response Time on Cloud Computingneirew J
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.
Content-Based Image Retrieval (CBIR) systems have been used for the searching of relevant images in various research areas. In CBIR systems features such as shape, texture and color are used. The extraction of features is the main step on which the retrieval results depend. Color features in CBIR are used as in the color histogram, color moments, conventional color correlogram and color histogram. Color space selection is used to represent the information of color of the pixels of the query image. The shape is the basic characteristic of segmented regions of an image. Different methods are introduced for better retrieval using different shape representation techniques; earlier the global shape representations were used but with time moved towards local shape representations. The local shape is more related to the expressing of result instead of the method. Local shape features may be derived from the texture properties and the color derivatives. Texture features have been used for images of documents, segmentation-based recognition,and satellite images. Texture features are used in different CBIR systems along with color, shape, geometrical structure and sift features.
The cyber attacks have become most prevalent in the past few years. During this time, attackers have discovered new vulnerabilities to carry out malicious activities on the internet. Both the clients and the servers have been victimized by the attackers. Clickjacking is one of the attacks that have been adopted by the attackers to deceive the innocuous internet users to initiate some action. Clickjacking attack exploits one of the vulnerabilities existing in the web applications. This attack uses a technique that allows cross domain attacks with the help of userinitiated clicks and performs unintended actions. This paper traces out the vulnerabilities that make a website vulnerable to clickjacking attack and proposes a solution for the same.
Performance Analysis of Audio and Video Synchronization using Spreaded Code D...Eswar Publications
The audio and video synchronization plays an important role in speech recognition and multimedia communication. The audio-video sync is a quite significant problem in live video conferencing. It is due to use of various hardware components which introduces variable delay and software environments. The objective of the synchronization is used to preserve the temporal alignment between the audio and video signals. This paper proposes the audio-video synchronization using spreading codes delay measurement technique. The performance of the proposed method made on home database and achieves 99% synchronization efficiency. The audio-visual
signature technique provides a significant reduction in audio-video sync problems and the performance analysis of audio and video synchronization in an effective way. This paper also implements an audio- video synchronizer and analyses its performance in an efficient manner by synchronization efficiency, audio-video time drift and audio-video delay parameters. The simulation result is carried out using mat lab simulation tools and simulink. It is automatically estimating and correcting the timing relationship between the audio and video signals and maintaining the Quality of Service.
Due to the availability of complicated devices in industry, models for consumers at lower cost of resources are developed. Home Automation systems have been developed by several researchers. The limitations of home automation includes complexity in architecture, higher costs of the equipment, interface inflexibility. In this paper as we have proposed, the working protocol of PIC 16F72 technology is which is secure, cost efficient, flexible that leads to the development of efficient home automation systems. The system is operational to control various home appliances like fans, Bulbs, Tube light. The following paper describes about components used and working of all components connected. The home automation system makes use of Android app entitled “Home App” which gives
flexibility and easy to use GUI.
Semantically Enchanced Personalised Adaptive E-Learning for General and Dysle...Eswar Publications
E-learning plays an important role in providing required and well formed knowledge to a learner. The medium of e- learning has achieved advancement in various fields such as adaptive e-learning systems. The need for enhancing e-learning semantically can enhance the retrieval and adaptability of the learning curriculum. This paper provides a semantically enhanced module based e-learning for computer science programme on a learnercentric perspective. The learners are categorized based on their proficiency for providing personalized learning environment for users. Learning disorders on the platform of e-learning still require lots of research. Therefore, this paper also provides a personalized assessment theoretical model for alphabet learning with learning objects for
children’s who face dyslexia.
Agriculture plays an important role in the economy of our country. Over 58 percent of the rural households depend on the agriculture sector as their means of livelihood. Agriculture is one of the major contributors to Gross Domestic Product(GDP). Seeds are the soul of agriculture. This application helps in reducing the time for the researchers as well as farmers to know the seedling parameters. The application helps the farmers to know about the percentage of seedlings that will grow and it is very essential in estimating the yield of that particular crop. Manual calculation may lead to some error, to minimize that error, the developed app is used. The scientist and farmers require the app to know about the physiological seed quality parameters and to take decisions regarding their farming activities. In this article a desktop app for seed germination percentage and vigour index calculation are developed in PHP scripting language.
What happens when adaptive video streaming players compete in time-varying ba...Eswar Publications
Competition among adaptive video streaming players severely diminishes user-QoE. When players compete at a bottleneck link many do not obtain adequate resources. This imbalance eventually causes ill effects such as screen flickering and video stalling. There have been many attempts in recent years to overcome some of these problems. However, added to the competition at the bottleneck link there is also the possibility of varying network bandwidth which can make the situation even worse. This work focuses on such a situation. It evaluates current heuristic adaptive video players at a bottleneck link with time-varying bandwidth conditions. Experimental setup includes the TAPAS player and emulated network conditions. The results show PANDA outperforms FESTIVE, ELASTIC and the Conventional players.
WLI-FCM and Artificial Neural Network Based Cloud Intrusion Detection SystemEswar Publications
Security and Performance aspects of cloud computing are the major issues which have to be tended to in Cloud Computing. Intrusion is one such basic and imperative security problem for Cloud Computing. Consequently, it is essential to create an Intrusion Detection System (IDS) to detect both inside and outside assaults with high detection precision in cloud environment. In this paper, cloud intrusion detection system at hypervisor layer is developed and assesses to detect the depraved activities in cloud computing environment. The cloud intrusion detection system uses a hybrid algorithm which is a fusion of WLI- FCM clustering algorithm and Back propagation artificial Neural Network to improve the detection accuracy of the cloud intrusion detection system. The proposed system is implemented and compared with K-means and classic FCM. The DARPA’s KDD cup dataset 1999 is used for simulation. From the detailed performance analysis, it is clear that the proposed system is able to detect the anomalies with high detection accuracy and low false alarm rate.
Spreading Trade Union Activities through Cyberspace: A Case StudyEswar Publications
This report present the outcome of an investigative research conducted to examine the modu-operandi of academic staff union of polytechnics (ASUP) YabaTech. The investigation covered the logistics and cost implication for spreading union activities among members. It was discovered that cost of management and dissemination of information to members was at high side, also logistics problem constitutes to loss of information in transit hence cut away some members from union activities. To curtail the problem identified, we proposed the
design of secure and dynamic website for spreading union activities among members and public. The proposed system was implemented using HTML5 technology, interface frameworks like Bootstrap and j query which enables the responsive feature of the application interface. The backend was designed using PHPMYSQL. It was discovered from the evaluation of the new system that cost of managing information has reduced considerably, and logistic problems identified in the old system has become a forgotten issue.
Identifying an Appropriate Model for Information Systems Integration in the O...Eswar Publications
Nowadays organizations are using information systems for optimizing processes in order to increase coordination and interoperability across the organizations. Since Oil and Gas Industry is one of the large industries in whole of the world, there is a need to compatibility of its Information Systems (IS) which consists three categories of systems: Field IS, Plant IS and Enterprise IS to create interoperability and approach the
optimizing processes as its result. In this paper we introduce the different models of information systems integration, identify the types of information systems that are using in the upstream and downstream sectors of petroleum industry, and finally based on expert’s opinions will identify a suitable model for information systems integration in this industry.
Link-and Node-Disjoint Evaluation of the Ad Hoc on Demand Multi-path Distance...Eswar Publications
This work illustrates the AOMDV routing protocol. Its ancestor, the AODV routing protocol is also described. This tutorial demonstrates how forward and reverse paths are created by the AOMDV routing protocol. Loop free paths formulation is described, together with node and link disjoint paths. Finally, the performance of the AOMDV routing protocol is investigated along link and node disjoint paths. The WSN with the AOMDV routing protocol using link disjoint paths is better than the WSN with the AOMDV routing protocol using node disjoint paths for energy consumption.
Bridging Centrality: Identifying Bridging Nodes in Transportation NetworkEswar Publications
To identify the importance of node of a network, several centralities are used. Majority of these centrality measures are dominated by components' degree due to their nature of looking at networks’ topology. We propose a centrality to identification model, bridging centrality, based on information flow and topological aspects. We apply bridging centrality on real world networks including the transportation network and show that the nodes distinguished by bridging centrality are well located on the connecting positions between highly connected regions. Bridging centrality can discriminate bridging nodes, the nodes with more information flowed through them and locations between highly connected regions, while other centrality measures cannot.
Now a days we are living in an era of Information Technology where each and every person has to become IT incumbent either intentionally or unintentionally. Technology plays a vital role in our day to day life since last few decades and somehow we all are depending on it in order to obtain maximum benefit and comfort. This new era equipped with latest advents of technology, enlightening world in the form of Internet of Things (IoT). Internet of things is such a specified and dignified domain which leads us to the real world scenarios where each object can perform some task while communicating with some other objects. The world with full of devices, sensors and other objects which will communicate and make human life far better and easier than ever. This paper provides an overview of current research work on IoT in terms of architecture, a technology used and applications. It also highlights all the issues related to technologies used for IoT, after the literature review of research work. The main purpose of this survey is to provide all the latest technologies, their corresponding
trends and details in the field of IoT in systematic manner. It will be helpful for further research.
Automatic Monitoring of Soil Moisture and Controlling of Irrigation SystemEswar Publications
In past couple of decades, there is immediate growth in field of agricultural technology. Utilization of proper method of irrigation by drip is very reasonable and proficient. A various drip irrigation methods have been proposed, but they have been found to be very luxurious and dense to use. The farmer has to maintain watch on irrigation schedule in the conventional drip irrigation system, which is different for different types of crops. In remotely monitored embedded system for irrigation purposes have become a new essential for farmer to accumulate his energy, time and money and will take place only when there will be requirement of water. In this approach, the soil test for chemical constituents, water content, and salinity and fertilizer requirement data collected by wireless and processed for better drip irrigation plan. This paper reviews different monitoring systems and proposes an automatic monitoring system model using Wireless Sensor Network (WSN) which helps the farmer to improve the yield.
Multi- Level Data Security Model for Big Data on Public Cloud: A New ModelEswar Publications
With the advent of cloud computing the big data has emerged as a very crucial technology. The certain type of cloud provides the consumers with the free services like storage, computational power etc. This paper is intended to make use of infrastructure as a service where the storage service from the public cloud providers is going to leveraged by an individual or organization. The paper will emphasize the model which can be used by anyone without any cost. They can store the confidential data without any type of security issue, as the data will be altered
in such a way that it cannot be understood by the intruder if any. Not only that but the user can retrieve back the original data within no time. The proposed security model is going to effectively and efficiently provide a robust security while data is on cloud infrastructure as well as when data is getting migrated towards cloud infrastructure or vice versa.
Impact of Technology on E-Banking; Cameroon PerspectivesEswar Publications
The financial services industry is experiencing rapid changes in services delivery and channels usage, and financial companies and users of financial services are looking at new technologies as they emerge and deciding whether or not to embrace them and the new opportunities to save and manage enormous time, cost and stress.
There is no doubt about the favourable and manifold impact of technology on e-banking as pictured in this review paper, almost all banks are with the least and most access e-banking Technological equipments like ATMs and Cards. On the other Hand cheap and readily available technology has opened a favourable competition in ebanking services business with a lot of wide range competitors competing with Commercial Banks in Cameroon in providing digital financial services.
Classification Algorithms with Attribute Selection: an evaluation study using...Eswar Publications
Attribute or feature selection plays an important role in the process of data mining. In general the data set contains more number of attributes. But in the process of effective classification not all attributes are relevant.
Attribute selection is a technique used to extract the ranking of attributes. Therefore, this paper presents a comparative evaluation study of classification algorithms before and after attribute selection using Waikato Environment for Knowledge Analysis (WEKA). The evaluation study concludes that the performance metrics of the classification algorithm, improves after performing attribute selection. This will reduce the work of processing irrelevant attributes.
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Performance Comparision of Dynamic Load Balancing Algorithm in Cloud Computing
1. Int. J. Advanced Networking and Applications
Volume: 08 Issue: 01 Pages: 2986-2990 (2016) ISSN: 0975-0290
2986
Performance Comparision of Dynamic Load
Balancing Algorithm in Cloud Computing
Yogita kaushik
AIM & ACT Department, Banasthali University, Rajasthan
Email: yogitamca62@gmail.com
Dr. C.K Jha
AIM & ACT Department, Banasthali University, Rajasthan
Email: CKjha1@gmail.com
-----------------------------------------------------------------------ABSTRACT-----------------------------------------------------------
Cloud computing as a distributed paradigm, it has the latent to make over a large part of the Cooperative
industry. In cloud computing it’s automatically describe more technologies like distributed computing,
virtualization, software, web services and networking. We review the new cloud computing technologies, and
indicate the main challenges for their development in future, among which load balancing problem stands out and
attracts our attention Concept of load balancing in networking and in cloud environment both are widely
different. Load balancing in networking its complete concern to avoid the problem of overloading and under
loading in any sever networking cloud computing its complete different its involves different elements metrics such
as security, reliability, throughput, tolerance, on demand services, cost etc. Through these elements we avoiding
various node problem of distributing system where many services waiting for request and others are heavily
loaded and through these its increase response time and degraded performance optimization. In this paper first we
classify algorithms in static and dynamic. Then we analyzed the dynamic algorithms applied in dynamics
environments in cloud. Through this paper we have been show compression of various dynamics algorithm in
which we include honey bee algorithm, throttled algorithm, Biased random algorithm with different elements and
describe how and which is best in cloud environment with different metrics mainly used elements are
performance, resource utilization and minimum cost. Our main focus of paper is in the analyze various load
balancing algorithms and their applicability in cloud environment.
Keywords - cloud computing, Load balancing, cloud load balancing, Honeybee algorithm, biased random
algorithm, Throttled Load Balancing Algorithm, Cloud-Analyst.
---------------------------------------------------------------------------------------------------------------------------------------------------
Date of submission: July 28, 2016 Date of Acceptance: Aug 24, 2016
---------------------------------------------------------------------------------------------------------------------------------------------------
I. INTRODUCTION
Cloud Computing is growing up like a rapid fire. It takes
in all fields: IT industries, research, students, government
sector, non IT etc. In traditional computing, we install
each and every software which we have required and
update hardware time to time. The works we have done
and stored in our computer they are accessible only in our
network we cannot be access all these matter or
documentation outside the network. Cloud computing
provide services according to your wish, if you want to
take service you can start and when you want to stop you
can. You pay for what you have used. It is very easy to use
a cloud we need only a thin client or a laptop to access the
internet. It is like electricity used at home. A very simple
example given by the formal federal of U.S Government
to explain the cloud computing is: “There was a time when
every household, town, farm, or village had its own water
well. Today, shared public utilities give us access to clean
water by simply Turing on the tap; cloud computing works
in a similar faction. Just like water from the tap in your
kitchen, cloud computing services can be turned on or off
quickly as needed. Like at the water company, there is a
team of dedicated professionals making sure the service
provided is safe , secure, and available on a 24/7 basis.
When the tap isn’t on, not only are you saving water, but
you aren’t paying for resources you don’t currently need”
Cloud computing provides all the features of grid
computing like software as a service and utility
computing. These services are provided by service
providers such as GOOGLE, MICROSOFT, AMAZON’s
etc, which are big provider of cloud services It also use the
concept of virtualization. Cloud computing architectures
are basically parallel, distributed and serve the desires of
various clients in different scenarios.
1.1 Load balancing algorithm
Load Balancing is a method in which work is distributed
among all the servers, network interfaces and computing
resources. Load balancing in the cloud is completely
different from the classical perception on load balancing
implementation by using commodity servers to perform
the load balancing. It is a process in which data is
distributed to various servers through different algorithms
and methods to improve the performance and resource
utilization. It makes sure that our all the servers are being
utilized and none is Idle. Load balancing is used to
distribute a larger processing load to smaller processing
server for increasing the overall performance.
2. Int. J. Advanced Networking and Applications
Volume: 08 Issue: 01 Pages: 2986-2990 (2016) ISSN: 0975-0290
2987
These are some existing load balancing algorithm in
Cloud computing.
a) Honey bee foraging algorithm [3]:
It is a natural inspired algorithm for self organization. It is
based on the behavior of honeybee. In this algorithm
servers are grouped under virtual servers, and they have
own queue for each. Each server sends a request from the
queue and checks the availability. If the availability is high
than the bees perform their waggle dance. It increases the
system diversity but it does not increase the throughput.
b) Biased Random Sampling [3]:
It is a distributed and scalable load balancing approach
that uses random sampling of system domain to achieve
self organization. In this algorithm firstly virtual graph is
constructed with the connectivity of each server
representing the load of server. Load balancing scheme
used fully decentralize and it makes a large network
system like Cloud. Performance is degraded with an
increase in population Diversity.
c) Active Clustering [3]:
It works on the principle of grouping similar nodes
together and working of groups. Performance degrades
with an increase in system diversity.
d) Round Robin Algorithm [3]:
In this algorithm load is transferred randomly and it can
cause some server to be heavily loaded and other to be idle
or lightly loaded. In this algorithm, processes are
scheduled in a FIFO manner but are given a limit time-
slice or a quantum. If a process is not completed in its time
slot the CPU preempts that process and gives to the next
process which is in queue. The preempted process is then
placed at the last of queue. The response time and
processing time can be improved in the respect of cost
optimization considered.
e) Equally Spread Current Execution
Algorithm [3]:
Equally spread current execution algorithm processes handle
the priorities. It distributes the load randomly by checking
the size and transfer of the load to that virtual machine which
is lightly loaded or handles that task easy and take less time,
and give maximum throughput. It is spread spectrum
technique in which the load balancer spreads the load into
multiple virtual machines.
f) Throttled Load Balancing Algorithm [3]:
Throttled algorithm is completely based on virtual machine.
In this algorithm end user first request the load balancer to
check the availability of virtual machine which access that
load easily and performs the job. In this algorithm the client
first requests the load balancer to find a suitable Virtual
Machine to perform the required operation.
II. LITERATURE SURVEY
According to Sajid Mohammad et al. Cloud computing is
new computing paradigm due to progress of information
technology demand, which deliver computing services on
minimum charges. All though cloud computing has radiant
future in business and research area few consequence
regarding performance, security, Virtualization, reliability,
interoperability, scalability etc., are exist in this
technology.[1]
Moharana Shanti et al. according to this paper load
balancing approach categorized in two ways static and
dynamic in cloud environment mainly dynamic load
balancing approach is used. A static load balancing
algorithm does not take into account the previous state or
behavior of a node while distributing the load. On the
other hand, a dynamic load balancing algorithm checks the
previous state of a node while distributing the load. The
dynamic load balancing algorithm is applied either as a
distributed or non-distributed. In static load balancing
algorithm Round robin, MIN-MIN, MIN-MAX
Algorithms mainly focus on response time minimization
these algorithm are not focus on throughput of overall
system because resource are allocated without knowing
the previous stage of VM.IN dynamic load balancing
algorithm, Equally Spread Current Execution algorithm,
Throttled Load Balancer, Honeybee Foraging Algorithm
focus on response time and throughput of the system.[5]
SharmaTejinder et al. according to author for minimum
data processing time, minimum average time, optimal
resource utilization and avoid overload is main goal of any
load balancing algorithm. The computing parameter like
response time, waiting time, execution time effect on load
balancing algorithm.[6]
Mohamaddiah Mohd Hairy et al.in this research paper
author objective is management of resource allocation
process .To provide a better resource allocation and
monitoring process in terms of a better performance,
competitive and efficiency to meet the required SLA,
improved the resource performance and lowered the power
consumption.[10]
Neeraj Mangla et al, This research paper is allocate the
resource based on auction resource allocation strategies.
The providers advertise their resources with their price and
interested consumer submit their bid. One mediator known
as meta-broker match the bids and declare the winner of
auction. Consumer who win the auction able to use the
allocated resource. [11][9]
Pawar Chandrasekhar S et al. Allocate the resource to
request based on priority of request. Minimum priority
request schedule after the certain period of time. Before
allocating resource find load on every VM and assign the
priority of every request then apply the dynamic min-min
algorithm, using these three algorithm concept allocate the
resource to receiving request. Using priority parameter in
3. Int. J. Advanced Networking and Applications
Volume: 08 Issue: 01 Pages: 2986-2990 (2016) ISSN: 0975-0290
2988
resource allocation focus on customer satisfaction also
reduce the response time and increase the throughput of
overall system. [12]
Portaluri Giuseppe et al., cloud provider required to
maintain the hardware, maintain the client data backup
(redundancy) for fault tolerance for minimize the
operation on. Objective of this paper is minimize the total
completion time and power consumption on switches and
servers. For reduce the cost. Using genetic algorithm using
joint allocation approach. [13]
Xiao Zhen et al. in this paper for find the performance of
multidimensional resource utilization of server use the
skewness. By minimize the skewness increase the overall
utilization of server resources.[19]
Gouda K C et al. according to author develop resource
allocation method using priority computing parameter for
minimize the resource become idle and minimum profit.
Objective of priority based resource allocation algorithm is
improving the performance of cloud system. [21]
Jena Soumya Ranjan et al. According to author equally
distribute the load is big challenge in distributed system.
For minimize the load minimize response time on every
node.[23]
Zhou Zhigang et al. For continuous resource allocation
resource reservation scheme is used this scheme reduce
the process migration and reduce cost because less process
migration event. [24]
BhoiUpendra et al.Hardware and software resource are
deliver the computing services over internet .Allocation of
resources using Max-Min algorithm with the help of
expected execution time. Find maximum allocation
execution time for resource allocation. [25]
PROBLEM DEFINITION
The random arrival of load in cloud atmosphere causes
some servers are heavily loaded in comparison to others.
Due to this problem client cannot be ignored since access
is denied to their request or requirement. It mostly seems
the reason for degradation of performance in cloud. The
considered uniqueness has an impact on cost, which can
be obtained by enhanced response time and processing
time. Through This paper the analyze various load
balancing algorithms and their applicability in cloud
environment.
III. SIMULATION CLOUD ANALYST
Cloud Analyst is a simulation package that has an easy to
use GUI. Cloud Analyst provides performance analysis. It
was derived from Cloud Sim and extends some of its
capabilities and features propose. It enables to repeatedly
perform simulations experiments with parameters
variations in a quick and easy way. Cloud Analyst can be
used for examining the behavior of large internet
application in a cloud environment [9]. It is a tool in which
we can do testing and perform simulation with different
matrices. In Cloud Sim we need to do core Programming.
Perform analysis of different different load balancing
policies, with different parameters. The study includes
compression of various algorithm with different service
polices.
In main configuration we took optimize response time
service broker policy and set VM’s memory and cost.
Parameters use in cloud analyst such as number of User
Base: 5, number of Datacenter: 4, simulation time: 60
minutes. In cloud analyst simulation region is globally
divided in 6 regions. We set operating system is LINUX
VMM Xen (Para virtualization).
IV. RESULTS:
Comparison of throttled algorithm and honeybee
algorithm in the simulation environment it has been done
on the basis of response time, Datacenter processing time
and cost. The comparison has been done on the basis of
minimum time, maximum time and average time for the
both algorithm. it has been analyze that the time
(minimum, maximum and average) taken by honeybee
algorithm is considerably fewer as compared to Throttled
Load Balancing Algorithm in cloud computing. The table
and graphs shows the average value, minimum value and
maximum value of overall response time (ms) for the
algorithms.
4. Int. J. Advanced Networking and Applications
Volume: 08 Issue: 01 Pages: 2986-2990 (2016) ISSN: 0975-0290
2989
Through tables and graph we easily differsiate both
algorithms and analyze them too. Overall Response Time in
Cloudlet On seeing the table and graph 1.1 (RST in
throttled) and 2.1 (RST in Honey Bee) we have shown a
comparison of RST in both algorithms. It shows in average
time in throttled DC1 took 0.47 and for same DC1 Honey
Bee took 0.46 same as we can see DC3 took 1.231 and for
same DC3 took 1.229 and we analyze respectively all Data
Center and get the results given by honey bee algorithms
are more efficient compared to throttled in request servicing
time.
Not only the basis of Request servicing time we can proof
that Honey bee is more efficient algorithm comparatively
to others . So again through the tables and graphs 1.2 (TC
in throttled) and 2.2 (TC in Honey Bee) we have shown a
comparison of COST in both algorithms. Cost is a very
important factor in cloud environment. Total cost of
Virtual Machines is measured in simulation over 10 VM’s.
It’s same in both algorithms. Total cost = (VM cost+ Data
Transfer Cost).
In the table and graph 1.3 (RT in throttled) and 2.3
(RT in Honey Bee) we have again shown a
comparison in both algorithm with response time. It
shows in average time in throttled UB1 is 50.884 and
Max is 60.033, UB2 is 50.707 and Max is 61.033 and
respectively. Same for honey bee algorithm UB1 took
50.879 in avg. and Max is 60.031, UB2 took 50.701
and Max 61.754. We analyze respectively all User
Bases and get the results given by honey bee
algorithms are more efficient compared to throttled in
request Time
5. Int. J. Advanced Networking and Applications
Volume: 08 Issue: 01 Pages: 2986-2990 (2016) ISSN: 0975-0290
2990
V. CONCLUSION
The response time and data transfer cost is a key challenge
issue in cloud environment it affects the performance in
the cloud based sectors. The paper aims to compare the
Load balancing algorithm in cloud environment. Honey
bee algorithm is found to be best among these. Both
algorithms are compared throttled on the basis of same
parameter as same cloudlet. The detail about the results
obtained for honey bee algorithm and it is comparisons
with Throttled algorithm. Honey bee Algorithm works
superior than Throttled load balancing algorithm which
itself is best amongst other accessible load balancing
algorithms in the cloud environment
REFERENCES
Journal Papers:
[1] Ren, Haozheng, Yihua Lan, and Chao Yin. "The load
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Research Fellow, Sri Guru Granth Sahib World
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[3] Swaroop Moharana, Rajadeepand and Digamber
Power “ANALYSIS OF LOAD BALANCERS IN CLOUD
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