A Method of Cloud Resource Load Balancing Scheduling Based on Improved Adaptive Genetic Algorithm

Save this PDF as:
 WORD  PNG  TXT  JPG

Size: px
Start display at page:

Download "A Method of Cloud Resource Load Balancing Scheduling Based on Improved Adaptive Genetic Algorithm"

Transcription

1 Journal of Information & Computational Science 9: 16 (2012) Available at A Method of Cloud Resource Load Balancing Scheduling Based on Improved Adaptive Genetic Algorithm Xin LU a,, Jing ZHOU a, Dong LIU b a School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu , China b Sichuan Chang Hong Electric Co., Mianyang , China Abstract One of the core problems which cloud resources scheduling need to solve is the load balance. In the cloud resources scheduling process, if load changes suddenly, this may cause resources scheduling tilt. This paper takes real-time load parameters (CPU occupancy rate, memory occupancy rate, network bandwidth, the process occupancy rate, service response time) from the server cluster nodes as decision variables of resources scheduling model, and uses the improved adaptive genetic algorithm to search the optimal solution, in order to realize the load balancing scheduling of cloud resource, and make the each index change smoothly. The experimental result shows that, using the improved load balancing scheduling strategy to solve the problems of the load balancing scheduling of cloud resource, not only makes the each index of the system change smoothly, but also improves the performance of the system efficiently. Keywords: Cloud Computing; Genetic Algorithm; Resource Scheduling; Load Balance 1 Introduction In the cloud computing applications, cloud infrastructure system provides users with computing resources used as needed, storage resources, network resources and other services[1]. In certain cloud applications, user access to service resources with the abrupt and uncertainties often leads to some server resource scheduling imbalance, resulting in some of the cloud application response time is too long. In order to adapt to the abrupt change of the number of user access requests, the industry generally uses scalable server cluster resources to meet. However, in the server cluster resource scheduling, we need to address an important issue, which is how to make cloud resource scheduling along with load fluctuations, dynamic and balanced [2]. In the industry s- tudy, some researchers put forward scheduling strategy such as Round-Robin Scheduling(RRS), Weighted Round Robin Scheduling(WRRS), Least-Connection Scheduling(LCS), Source Hashing Corresponding author. address: (Xin LU) / Copyright 2012 Binary Information Press December 1, 2012

2 4802 X. Lu et al. /Journal of Information & Computational Science 9: 16 (2012) Scheduling(SHC) and so on[3-4]. Document[4] contrasts and analyses these load balance scheduling algorithms, and puts forward a method for finding an optimal solution in various schemes. Using traditional scheduling algorithm to search for the optimal solution can easily produce error peaks. Zomaya A.Y has proposed using Genetic Algorithm [5] for the optimal scheduling search. However, due to the use of fixed parameter settings in the search for the evolutionary process, parameter values of the method did not change along with the search status. This algorithm can t reach equilibrium in the search process, and easily getting into the regional optimal solution, and cause premature phenomenon to appear. In order to overcome this defect, we need to dynamically set the parameters, and ensure that parameter values change the status along with the search process. The algorithm can be avoided into a regional convergence. Further taking into account species adaptation, but also need the algorithm to adaptively adjust. Therefore, this paper will combine the characteristics of adaptive genetic algorithm which can search the optimal solution in multi-objective problem, learn from the document [6] suggested load balancing ideas on the server to study the optimal scheduling strategy and to solve the load balance problems of server nodes in the cloud resources scheduling. 2 The Problem of Cloud Resource Scheduling In the cloud computing environment, the cloud resource scheduling controller, through the changing load of real-time monitor servers nodes, assigns resources to the corresponding node dynamically to meet some needs, such as high utilization of resources, excellent performance, fast response. The topology of the server group nodes that involved in the cloud resources scheduling can be described in Fig CC4 virtual machine CC5 V1 CC CCn Vk CC CC NC1 NC2 NCm NC1 NC2 NCm Fig. 1: Nodes topology of cloud server cluster The above topology can be described as an G(V,E) undirected graph, G represents the node set in the chart, E represents the set of connection between these nodes. CC i represents cluster control server node. NC i represents node controller, which is a separate physical machine. Each CC i node manages m node controller NC i s, you can run k virtual machines on each NC i, and use V i to represent virtual machine, So the node which will be mentioned in this paper is V i. In the cloud service environment, application, DBMS and other software are deployed in several V i nodes to run. Difficult issues of cloud resource scheduling need to be addressed is: when the

3 X. Lu et al. /Journal of Information & Computational Science 9: 16 (2012) user requests the use of cloud resources services, cloud resources scheduling controller should adopt what kind of resources scheduling model and strategy, this makes each node of the whole system deal with tasks in load balance way, and prevent scheduling tilt of the cloud services platform system resources. 3 Load-balancing Cloud Resource Scheduling Model In order to solve the above problems, this paper proposes a model program which is multi-objective optimization and load balancing of cloud resource schedule, the model is shown in Fig.2. request cloud controller Load balancing scheduling strategy user virtual resource pool cluster server n cluster server 2 [29] cluster server 1 V 1 V m V2 [29] V 3 V 4 request quene... cluster controller real-time load monitoring multi-objective parametes calculate fitness cluster n cluster 2 The optimal solution Improved Adaptive Genetic Algorithm fitness of cluster 2 fitness of cluster 1 Fig. 2: Load-balancing cloud resource scheduling model When the user makes a request, the cloud controller receives the request, then it will arrange the request in the queue of the server cluster. There have more than one cluster controller below the cloud controller, cluster controllers real-time monitor the running load parameters of every V i in virtual resource pool of this cluster, such as CPU occupancy rate, memory occupancy rate, the network bandwidth and process occupancy rate and so on, Therefore, this is a multi-objective problem which searches for the optimal solution. According to monitoring the multi-objective parameter, cluster controller will calculate the fitness of each V i node, then for the whole cluster, use Improved Adaptive Genetic Algorithm to search the best solution in the multi-objective question, which is the lighter node by use algorithm to solve. Finally, according to cloud resource scheduling strategy of load balancing, service resource will be assigned to the best server node. By using this method proposed in this paper, the whole system can make load balance. 3.1 Fitness calculation function This paper chooses several key indexes (such as CPU occupancy rate, memory occupancy rate, the network bandwidth and process occupancy rate) of a service node, which as decision variables of multi-objective optimization problem. Respectively, use CPU, memory, net, response and process to express the decision variables, w i represents load information weighting coefficient of these variables. Sub-objective function f i (x) is set up different weight, due to the different

4 4804 X. Lu et al. /Journal of Information & Computational Science 9: 16 (2012) applications require different parameter values, so w i expresses the important degree of decision variable in the optimization problem, and w i = 1.When using cluster for the first time, we can set the initial weights of each node, and then with the change of these node load. The system will dynamical adaptively adjust load changes. In this paper, we do not consider the configuration of servers. Assuming that configuration of all servers are the same. Therefore, the objective function can be described as follow. n m t(f(x)) = (w i f i (x)/c j ) (1) i=1 j=1 In formula (1), n is the number of decision variable, m stands for the number of nodes, c j is task proportion which server be assigned. The weight of each nodecan express for the expansions of formula(1). It can be distributed as follow. t(f(j)) = w 1 cpu[j] + w 2 memory[j] + w 3 net[j] + w 4 response[j] + w 5 process[j], 0 < cpu[j] 1, 0 < memory[j] 1, 0 < net[j] 1, 0 < response[j] 100ms, 0 process[j] 1 (2) The objective function is used to calculate the weight of the nodes. So it is more appropriate that choosing the objective function to determine the fitness calculation function. The fitness function is shown in the formula (3). We can seek the optimal chromosomes of feasible solution, namely the optimal solution. { n m n } F (x) = c (w i f i (x)), c max w i f i (x) (3) i=1 j=1 For multi-objective optimization problem, to improve a target may reduce the performance of another one. For example, there is an inverse proportion between server response time and CPU occupancy rate, network bandwidth. The relationship can be described as follow. i=1 response = c/(cpu net), c is constant (4) Therefore, we only need to acquire these four parameters which are CPU and memory occupancy rate, network bandwidth, processes occupancy rate, and these four parameters are introduced into the algorithm, then through the calculation of these parameters to obtain the optimal solution. 3.2 The design of improved adaptive genetic algorithm In the upper segment, decision variables of problems which need to be solved in this paper have been set, and the fitness function has been set up. The multi-objective optimization problem will be solved by the design of the Improved Adaptive Genetic Algorithm. The flowchart of the algorithm is shown in Fig.3. The main improvement of Improved Adaptive Genetic Algorithm is taking advantage of the constant change of population diversity to avoid the regional optimal solution which is common in standard adaptive algorithm. This section will code the decision variables and establish initial population, and then will compute the population fitness and repeated use selection operator, commuting operator and mutation operator. During evolution, along with the change of the

5 X. Lu et al. /Journal of Information & Computational Science 9: 16 (2012) Describe the problem of solving the optimal solution Set decision variables According to the actual situation, set up the objective function and fitness function To code for variable, create initial population Use fitness function to calculate individual fitness in the population Design genetic operator, to determine operation parameters with diversity Use algorithm to calculate The optimal solution in the algrothim as the best individual in the current population Fig. 3: Improved adaptive genetic algorithm flowchart population, Improved Adaptive Genetic Algorithm dynamically adjusts the commuting rates and mutation rates until finally seeks out the optimal solution. Genetic algorithms usually use binary code, because it makes encoding/decoding and genetic operations simple. But the disadvantage of this method is that it can t fully reflect the specific knowledge of the target problem, such as the optimization problem of some continuous functions. Considering the defects of binary code, this paper chooses real-number-coding. The real-numbercoding uses a real number in a certain range to represent each gene value of the individual. Since the length of coding for the individual is equal to the number of decision variables that the individual has, real-number-coding usually utilizes the real value of decision variables. The optimization problem in this paper contains four decision variables x i (i = 1, 2, 3, 4), and each decision variable is in [0,1], so the improved Adaptive Genetic Algorithm can use any four values in [0,1] to represent the genotypes of individuals. Selection operation, also known as copy operation, can pass the individuals which have high fitness in the current group to the next generation of population with some rules by calculating the fitness with fitness function. It mimics biological genetic and evolutionary process, and genetic algorithms can implement individual survival of the fittest by the selection operators or copy operators. This paper chooses fitness proportional model and elite selection algorithms to select and copy individuals. In this strategy, the probability of an individual with higher fitness is chosen to pass to the next generation is proportional to the value of individual fitness. Formula (5) shows the relationship between them. n p i = F i / F j (5) In this formula, p i is the selective probability of the ith individual, F i is the fitness value of the ith individual, n is the size of population. As can be seen from the formula, p i reflects the j=1

6 4806 X. Lu et al. /Journal of Information & Computational Science 9: 16 (2012) proportions of the fitness of an individual to the total fitness of all individuals in the entire population. So the higher the fitness of an individual and the higher the probability of this individual can be chosen. Since the regional optimum is common in standard adaptive genetic algorithm and leads to the premature phenomenon, the Improved Adaptive Genetic Algorithm improves P c and P m to make them increase properly when the fitness of population tends to the same value, and decrease properly when the fitness of population is dispersed. So that, Improved Adaptive Genetic Algorithm increase the mutation probability and decrease the crossover probability of individual whose fitness is lower than the average, and protect the solution to let the individual whose fitness is higher than the average can be passed to next generation. The formula to calculate the improved crossover probability P c and mutation probability P m is as following: p c = { pc1 (p c1 p c2 )(f f) f max f, f f p c1, f < f (6) p m = { pm1 (p m1 p m2 )(f max f) f max f, f f p m1, f < f (7) In formula(6) and (7), f is the fitness value of the individual with higher fitness of the two cross individuals, f is the fitness average value of each generation, f max is the maximum fitness value in the entire population, f is the fitness value of variation individuals. p c1 is the upper limit of crossover probability and p c2 is the amplitude of crossover probability. Generally speaking, p c1 =0.9 or 1, the value of p c2 is in the range of [0.5,1]. p m1 is the upper limit of mutation probability and p m2 is the amplitude of mutation probability. The value of p m1 is always 0.1, and the value of p m2 is in the range of [0.01,0.05]. Improved Adaptive Genetic Algorithm proposed for the optimal solution is an iterative process, in this paper, we set iteration time T as 500, that is to say, Improved Adaptive Genetic Algorithm terminates after 500 iteration times, and returns to the optimal solution of the problem. The above process is used for searching the optimal solution, so through this algorithm we find out the lightest load node of the whole cluster, then the requests of the tail taken out from the request queue of users are processed by the free node, so balance the load of cluster, and make the cluster not show up that some nodes are special busy, some are special idle. 3.3 Cloud resource load balancing strategy based on adaptive genetic algorithm When the requests of users arrived, how to distribute these requests to the relative idle server to balance the load of the server cluster is the key issue of adaptive cloud resource load balancing model. In the cloud resource positioning, when the request queue is not empty, Improved Adaptive Genetic Algorithm based on the decision variables calculates the population fitness, and dynamically adjusts the crossover and mutation rate along with the changes of fitness, thus gives rise to a new population, and then calculates the fitness of new population, adjusts the rate of crossover and mutation, repeats this iteration until searching out the global optimal solution which is the

7 X. Lu et al. /Journal of Information & Computational Science 9: 16 (2012) lightest load node, then the request which removed from the tail of request queue, is allocated to the lightest load node. The problem of the unbalanced load in the cloud resource is solved, and the server can handle requests of users with the best efficiency. The adaptive cloud resource load balancing scheduling strategy of this paper is as follows: this paper takes CPU occupancy rate, memory occupancy rate, the network bandwidth and process occupancy rate and service response time of V i node in cluster server as variables of measuring the server load multi-objective problems, these parameters are the decision variables of the Adaptive Genetic Algorithm, and use these variables to determine fitness function. In the cloud service environment, with the process of searching and evolving, population will gradually appear in a single case, so the crossover and mutation probability needs to be adaptively adjusted for the search in order to avoid the regional convergence. By improving Adaptive Genetic Algorithm we can find the optimal solution, namely the node has the lightest load, and the node will handle the current request. So that subsequence request can be assigned to load lighter server, this algorithm balances the system load. By using Improved Adaptive Genetic Algorithm, the system can converge to the global optimal solution with the fastest speed to load balance. 4 Simulate Experiment To demonstrate load balancing scheduling model process performance based on Improved Adaptive Genetic Algorithm in this paper, the experiment uses five PCs to build cluster server platform environment. In this experiment, network environment is 100M bit/s. Tomcat 6.0 is chosen as the web server. In the five servers, one is the client machine, one is the cluster management machine, and the rest are the node machines. Cluster management machine and node machine must place in a regional area network. Cloud resource load balancing scheduling program and HTTP agent need to be installed in the cluster management machine. Two virtual machines are respectively created by using Xen in two of node machines, and a virtual machine is created in another node. Every virtual machine can deploy and operate Web applications and probe monitoring tool. After experimental environment is successfully set up, firstly, we test the convergence performance of the algorithm. Fig.4 is the comparison on convergence degree between the Adaptive Genetic Algorithm and Improved Adaptive Genetic Algorithm in this paper. Fig. 4: Convergence degree contrast

8 4808 X. Lu et al. /Journal of Information & Computational Science 9: 16 (2012) As Fig.4 shows, Improved Adaptive Genetic Algorithm proposed in this paper has an experimental result of relatively stable, fast convergence, and does not appear phenomenon of immature convergence. In contrast, Adaptive Genetic Algorithm has slow speed in convergence, and which appears obvious premature convergence phenomenon after 50 times and 100 times of iteration. The experimental results show that Improved Adaptive Genetic Algorithm can effectively prevent premature phenomenon compared with Adaptive Genetic Algorithm, and search the optimal solution fast, and it has obvious advantages. Finally, we test the cloud resource load balancing scheduling strategy proposed in this paper. This experiment chooses the smallest connection strategy (LCS) which compares with load balancing scheduling strategy (LBS) of this paper, and check and record the running situation of CPU and memory of the cluster nodes. The experimental result displays CPU and memory occupancy rate which the system runs in the 45th minutes, each is shown in Fig.5 and Fig.6. Fig. 5: CPU occupancy rate of each node in cluster Fig. 6: Memory occupancy rate of each node in cluster Fig.5 and Fig.6 indicate that LBS proposed in this paper can eliminate the larger load differences in each node and serious load imbalance, is better than LCS strategy. The results show that LBS can reasonably assign tasks, improve the process performance of the whole cluster, therefore LBS proposed in this paper is feasible.

9 X. Lu et al. /Journal of Information & Computational Science 9: 16 (2012) Conclusions In this paper, by real-time monitoring CPU and memory occupancy rate, network bandwidth, response time, processes occupancy rate of Vi nodes in cluster server, the fitness of each node is determined, and using the Improved Adaptive Genetic Algorithm searches for the optimal solution according to fitness value, namely load lighter node, then the request will be allocated to this node. This method largely eliminates the serious load imbalance between nodes, makes each index of the node smoothly change, so that this can achieve the load balancing of cloud resource. Using this strategy to handle the problem of load balancing scheduling of cloud resource, not only prevent the tilt of the cluster scheduling, but also improve the running performance of the whole cluster. References [1] Michal Armbrust, Armando Fox, and Rean Griffith, et al. Above the Clouds: A Berkeley View of Cloud Cpmputing[J], mimeo, UC Berkeley, RAD Laboratory, 2009, 2: [2] Shijue Zheng, Wanneng Shu, Guangdong Chen. A Load Balanced Method Based on Campus Grid [C]. Beijing: International Symposium on Communication and Information Technologlies (ISCTT), [3] V. Cardellini, M. Colajanni. Dynamic load balancing on Web-server systems[j]. IEEE Internet Computing, 1999, 3(3): [4] Clandio Casetti, Renato Lo Cigno. A New Class of QoS RoutingStrategies Based on Network Graph Reduction[J]. Computer Net-works, 2003, 41(4): [5] Xiaofeng Xue, Yuesheng Gu.Global Optimization Based on Hybrid Clonal Selection Genetic Algorithm for Task Scheduling[J]. Journal of Computational Information Systems, 2010, 6(1): [6] Wanneng Shu, Shijue Zheng. A Real-course-based Load Balanced Algorithm of VOD Cluster[C]. Ningbo: International Symposium on Computer Science and Technology(ISCST), [7] Dong Wang, Xinqing Wang, Jian Tang, Chunsheng Zhu, Ting Xu. Fault Diagnosis of the PT Fuel System Based on Improved Adaptive Genetic Algorithm[J]. Journal of Computational Information Systems, 2012, 8(9):

A Load Balancing Algorithm based on the Variation Trend of Entropy in Homogeneous Cluster

A Load Balancing Algorithm based on the Variation Trend of Entropy in Homogeneous Cluster , pp.11-20 http://dx.doi.org/10.14257/ ijgdc.2014.7.2.02 A Load Balancing Algorithm based on the Variation Trend of Entropy in Homogeneous Cluster Kehe Wu 1, Long Chen 2, Shichao Ye 2 and Yi Li 2 1 Beijing

More information

Dynamic Adaptive Feedback of Load Balancing Strategy

Dynamic Adaptive Feedback of Load Balancing Strategy Journal of Information & Computational Science 8: 10 (2011) 1901 1908 Available at http://www.joics.com Dynamic Adaptive Feedback of Load Balancing Strategy Hongbin Wang a,b, Zhiyi Fang a,, Shuang Cui

More information

UPS battery remote monitoring system in cloud computing

UPS battery remote monitoring system in cloud computing , pp.11-15 http://dx.doi.org/10.14257/astl.2014.53.03 UPS battery remote monitoring system in cloud computing Shiwei Li, Haiying Wang, Qi Fan School of Automation, Harbin University of Science and Technology

More information

An Optimized Load-balancing Scheduling Method Based on the WLC Algorithm for Cloud Data Centers

An Optimized Load-balancing Scheduling Method Based on the WLC Algorithm for Cloud Data Centers Journal of Computational Information Systems 9: 7 (23) 689 6829 Available at http://www.jofcis.com An Optimized Load-balancing Scheduling Method Based on the WLC Algorithm for Cloud Data Centers Lianying

More information

A Service Revenue-oriented Task Scheduling Model of Cloud Computing

A Service Revenue-oriented Task Scheduling Model of Cloud Computing Journal of Information & Computational Science 10:10 (2013) 3153 3161 July 1, 2013 Available at http://www.joics.com A Service Revenue-oriented Task Scheduling Model of Cloud Computing Jianguang Deng a,b,,

More information

A Survey on Load Balancing and Scheduling in Cloud Computing

A Survey on Load Balancing and Scheduling in Cloud Computing IJIRST International Journal for Innovative Research in Science & Technology Volume 1 Issue 7 December 2014 ISSN (online): 2349-6010 A Survey on Load Balancing and Scheduling in Cloud Computing Niraj Patel

More information

Sensors & Transducers 2015 by IFSA Publishing, S. L. http://www.sensorsportal.com

Sensors & Transducers 2015 by IFSA Publishing, S. L. http://www.sensorsportal.com Sensors & Transducers 2015 by IFSA Publishing, S. L. http://www.sensorsportal.com A Dynamic Deployment Policy of Slave Controllers for Software Defined Network Yongqiang Yang and Gang Xu College of Computer

More information

Auto-Scaling Model for Cloud Computing System

Auto-Scaling Model for Cloud Computing System Auto-Scaling Model for Cloud Computing System Che-Lun Hung 1*, Yu-Chen Hu 2 and Kuan-Ching Li 3 1 Dept. of Computer Science & Communication Engineering, Providence University 2 Dept. of Computer Science

More information

Advanced Task Scheduling for Cloud Service Provider Using Genetic Algorithm

Advanced Task Scheduling for Cloud Service Provider Using Genetic Algorithm IOSR Journal of Engineering (IOSRJEN) ISSN: 2250-3021 Volume 2, Issue 7(July 2012), PP 141-147 Advanced Task Scheduling for Cloud Service Provider Using Genetic Algorithm 1 Sourav Banerjee, 2 Mainak Adhikari,

More information

A COGNITIVE NETWORK BASED ADAPTIVE LOAD BALANCING ALGORITHM FOR EMERGING TECHNOLOGY APPLICATIONS *

A COGNITIVE NETWORK BASED ADAPTIVE LOAD BALANCING ALGORITHM FOR EMERGING TECHNOLOGY APPLICATIONS * International Journal of Computer Science and Applications, Technomathematics Research Foundation Vol. 13, No. 1, pp. 31 41, 2016 A COGNITIVE NETWORK BASED ADAPTIVE LOAD BALANCING ALGORITHM FOR EMERGING

More information

A Novel Load Balancing Optimization Algorithm Based on Peer-to-Peer

A Novel Load Balancing Optimization Algorithm Based on Peer-to-Peer A Novel Load Balancing Optimization Algorithm Based on Peer-to-Peer Technology in Streaming Media College of Computer Science, South-Central University for Nationalities, Wuhan 430074, China shuwanneng@yahoo.com.cn

More information

On Cloud Computing Technology in the Construction of Digital Campus

On Cloud Computing Technology in the Construction of Digital Campus 2012 International Conference on Innovation and Information Management (ICIIM 2012) IPCSIT vol. 36 (2012) (2012) IACSIT Press, Singapore On Cloud Computing Technology in the Construction of Digital Campus

More information

Load Balancing Algorithm Based on Services

Load Balancing Algorithm Based on Services Journal of Information & Computational Science 10:11 (2013) 3305 3312 July 20, 2013 Available at http://www.joics.com Load Balancing Algorithm Based on Services Yufang Zhang a, Qinlei Wei a,, Ying Zhao

More information

Method of Fault Detection in Cloud Computing Systems

Method of Fault Detection in Cloud Computing Systems , pp.205-212 http://dx.doi.org/10.14257/ijgdc.2014.7.3.21 Method of Fault Detection in Cloud Computing Systems Ying Jiang, Jie Huang, Jiaman Ding and Yingli Liu Yunnan Key Lab of Computer Technology Application,

More information

Modeling on Energy Consumption of Cloud Computing Based on Data Center Yu Yang 1, a Jiang Wei 2, a Guan Wei 1, a Li Ping 1, a Zhou Yongmin 1, a

Modeling on Energy Consumption of Cloud Computing Based on Data Center Yu Yang 1, a Jiang Wei 2, a Guan Wei 1, a Li Ping 1, a Zhou Yongmin 1, a International Conference on Applied Science and Engineering Innovation (ASEI 2015) Modeling on Energy Consumption of Cloud Computing Based on Data Center Yu Yang 1, a Jiang Wei 2, a Guan Wei 1, a Li Ping

More information

A resource schedule method for cloud computing based on chaos particle swarm optimization algorithm

A resource schedule method for cloud computing based on chaos particle swarm optimization algorithm Abstract A resource schedule method for cloud computing based on chaos particle swarm optimization algorithm Lei Zheng 1, 2*, Defa Hu 3 1 School of Information Engineering, Shandong Youth University of

More information

Efficient DNS based Load Balancing for Bursty Web Application Traffic

Efficient DNS based Load Balancing for Bursty Web Application Traffic ISSN Volume 1, No.1, September October 2012 International Journal of Science the and Internet. Applied However, Information this trend leads Technology to sudden burst of Available Online at http://warse.org/pdfs/ijmcis01112012.pdf

More information

AN ADAPTIVE DISTRIBUTED LOAD BALANCING TECHNIQUE FOR CLOUD COMPUTING

AN ADAPTIVE DISTRIBUTED LOAD BALANCING TECHNIQUE FOR CLOUD COMPUTING AN ADAPTIVE DISTRIBUTED LOAD BALANCING TECHNIQUE FOR CLOUD COMPUTING Gurpreet Singh M.Phil Research Scholar, Computer Science Dept. Punjabi University, Patiala gurpreet.msa@gmail.com Abstract: Cloud Computing

More information

Fault Analysis in Software with the Data Interaction of Classes

Fault Analysis in Software with the Data Interaction of Classes , pp.189-196 http://dx.doi.org/10.14257/ijsia.2015.9.9.17 Fault Analysis in Software with the Data Interaction of Classes Yan Xiaobo 1 and Wang Yichen 2 1 Science & Technology on Reliability & Environmental

More information

Optimization of PID parameters with an improved simplex PSO

Optimization of PID parameters with an improved simplex PSO Li et al. Journal of Inequalities and Applications (2015) 2015:325 DOI 10.1186/s13660-015-0785-2 R E S E A R C H Open Access Optimization of PID parameters with an improved simplex PSO Ji-min Li 1, Yeong-Cheng

More information

Memory Allocation Technique for Segregated Free List Based on Genetic Algorithm

Memory Allocation Technique for Segregated Free List Based on Genetic Algorithm Journal of Al-Nahrain University Vol.15 (2), June, 2012, pp.161-168 Science Memory Allocation Technique for Segregated Free List Based on Genetic Algorithm Manal F. Younis Computer Department, College

More information

A LOAD BALANCING MODEL USING FIREFLY ALGORITHM IN CLOUD COMPUTING

A LOAD BALANCING MODEL USING FIREFLY ALGORITHM IN CLOUD COMPUTING Journal of Computer Science 10 (7): 1156-1165, 2014 ISSN: 1549-3636 2014 doi:10.3844/jcssp.2014.1156.1165 Published Online 10 (7) 2014 (http://www.thescipub.com/jcs.toc) A LOAD BALANCING MODEL USING FIREFLY

More information

IMPROVEMENT OF RESPONSE TIME OF LOAD BALANCING ALGORITHM IN CLOUD ENVIROMENT

IMPROVEMENT OF RESPONSE TIME OF LOAD BALANCING ALGORITHM IN CLOUD ENVIROMENT IMPROVEMENT OF RESPONSE TIME OF LOAD BALANCING ALGORITHM IN CLOUD ENVIROMENT Muhammad Muhammad Bala 1, Miss Preety Kaushik 2, Mr Vivec Demri 3 1, 2, 3 Department of Engineering and Computer Science, Sharda

More information

HOST SCHEDULING ALGORITHM USING GENETIC ALGORITHM IN CLOUD COMPUTING ENVIRONMENT

HOST SCHEDULING ALGORITHM USING GENETIC ALGORITHM IN CLOUD COMPUTING ENVIRONMENT International Journal of Research in Engineering & Technology (IJRET) Vol. 1, Issue 1, June 2013, 7-12 Impact Journals HOST SCHEDULING ALGORITHM USING GENETIC ALGORITHM IN CLOUD COMPUTING ENVIRONMENT TARUN

More information

International Journal Of Engineering Research & Management Technology

International Journal Of Engineering Research & Management Technology International Journal Of Engineering Research & Management Technology March- 2014 Volume-1, Issue-2 PRIORITY BASED ENHANCED HTV DYNAMIC LOAD BALANCING ALGORITHM IN CLOUD COMPUTING Srishti Agarwal, Research

More information

A Parallel Processor for Distributed Genetic Algorithm with Redundant Binary Number

A Parallel Processor for Distributed Genetic Algorithm with Redundant Binary Number A Parallel Processor for Distributed Genetic Algorithm with Redundant Binary Number 1 Tomohiro KAMIMURA, 2 Akinori KANASUGI 1 Department of Electronics, Tokyo Denki University, 07ee055@ms.dendai.ac.jp

More information

Numerical Research on Distributed Genetic Algorithm with Redundant

Numerical Research on Distributed Genetic Algorithm with Redundant Numerical Research on Distributed Genetic Algorithm with Redundant Binary Number 1 Sayori Seto, 2 Akinori Kanasugi 1,2 Graduate School of Engineering, Tokyo Denki University, Japan 10kme41@ms.dendai.ac.jp,

More information

A Dynamic Resource Management with Energy Saving Mechanism for Supporting Cloud Computing

A Dynamic Resource Management with Energy Saving Mechanism for Supporting Cloud Computing A Dynamic Resource Management with Energy Saving Mechanism for Supporting Cloud Computing Liang-Teh Lee, Kang-Yuan Liu, Hui-Yang Huang and Chia-Ying Tseng Department of Computer Science and Engineering,

More information

Energy Efficient Load Balancing of Virtual Machines in Cloud Environments

Energy Efficient Load Balancing of Virtual Machines in Cloud Environments , pp.21-34 http://dx.doi.org/10.14257/ijcs.2015.2.1.03 Energy Efficient Load Balancing of Virtual Machines in Cloud Environments Abdulhussein Abdulmohson 1, Sudha Pelluri 2 and Ramachandram Sirandas 3

More information

A Survey on Load Balancing Techniques Using ACO Algorithm

A Survey on Load Balancing Techniques Using ACO Algorithm A Survey on Load Balancing Techniques Using ACO Algorithm Preeti Kushwah Department of Computer Science & Engineering, Acropolis Institute of Technology and Research Indore bypass road Mangliya square

More information

HYBRID ACO-IWD OPTIMIZATION ALGORITHM FOR MINIMIZING WEIGHTED FLOWTIME IN CLOUD-BASED PARAMETER SWEEP EXPERIMENTS

HYBRID ACO-IWD OPTIMIZATION ALGORITHM FOR MINIMIZING WEIGHTED FLOWTIME IN CLOUD-BASED PARAMETER SWEEP EXPERIMENTS HYBRID ACO-IWD OPTIMIZATION ALGORITHM FOR MINIMIZING WEIGHTED FLOWTIME IN CLOUD-BASED PARAMETER SWEEP EXPERIMENTS R. Angel Preethima 1, Margret Johnson 2 1 Student, Computer Science and Engineering, Karunya

More information

Comparative Analysis of Load Balancing Algorithms in Cloud Computing

Comparative Analysis of Load Balancing Algorithms in Cloud Computing Comparative Analysis of Load Balancing Algorithms in Cloud Computing Anoop Yadav Department of Computer Science and Engineering, JIIT, Noida Sec-62, Uttar Pradesh, India ABSTRACT Cloud computing, now a

More information

Wireless Sensor Networks Coverage Optimization based on Improved AFSA Algorithm

Wireless Sensor Networks Coverage Optimization based on Improved AFSA Algorithm , pp. 99-108 http://dx.doi.org/10.1457/ijfgcn.015.8.1.11 Wireless Sensor Networks Coverage Optimization based on Improved AFSA Algorithm Wang DaWei and Wang Changliang Zhejiang Industry Polytechnic College

More information

ISSN: 2319-5967 ISO 9001:2008 Certified International Journal of Engineering Science and Innovative Technology (IJESIT) Volume 2, Issue 3, May 2013

ISSN: 2319-5967 ISO 9001:2008 Certified International Journal of Engineering Science and Innovative Technology (IJESIT) Volume 2, Issue 3, May 2013 Transistor Level Fault Finding in VLSI Circuits using Genetic Algorithm Lalit A. Patel, Sarman K. Hadia CSPIT, CHARUSAT, Changa., CSPIT, CHARUSAT, Changa Abstract This paper presents, genetic based algorithm

More information

Remaining Capacity Based Load Balancing Architecture for Heterogeneous Web Server System

Remaining Capacity Based Load Balancing Architecture for Heterogeneous Web Server System Remaining Capacity Based Load Balancing Architecture for Heterogeneous Web Server System Tsang-Long Pao Dept. Computer Science and Engineering Tatung University Taipei, ROC Jian-Bo Chen Dept. Computer

More information

Study on Cloud Computing Resource Scheduling Strategy Based on the Ant Colony Optimization Algorithm

Study on Cloud Computing Resource Scheduling Strategy Based on the Ant Colony Optimization Algorithm www.ijcsi.org 54 Study on Cloud Computing Resource Scheduling Strategy Based on the Ant Colony Optimization Algorithm Linan Zhu 1, Qingshui Li 2, and Lingna He 3 1 College of Mechanical Engineering, Zhejiang

More information

A Heuristic Location Selection Strategy of Virtual Machine Based on the Residual Load Factor

A Heuristic Location Selection Strategy of Virtual Machine Based on the Residual Load Factor Journal of Computational Information Systems 9: 18 (2013) 7389 7396 Available at http://www.jofcis.com A Heuristic Location Selection Strategy of Virtual Machine Based on the Residual Load Factor Gaochao

More information

Load Balancing using DWARR Algorithm in Cloud Computing

Load Balancing using DWARR Algorithm in Cloud Computing IJIRST International Journal for Innovative Research in Science & Technology Volume 1 Issue 12 May 2015 ISSN (online): 2349-6010 Load Balancing using DWARR Algorithm in Cloud Computing Niraj Patel PG Student

More information

Improved Dynamic Load Balance Model on Gametheory for the Public Cloud

Improved Dynamic Load Balance Model on Gametheory for the Public Cloud ISSN (Online): 2349-7084 GLOBAL IMPACT FACTOR 0.238 DIIF 0.876 Improved Dynamic Load Balance Model on Gametheory for the Public Cloud 1 Rayapu Swathi, 2 N.Parashuram, 3 Dr S.Prem Kumar 1 (M.Tech), CSE,

More information

Research for the Data Transmission Model in Cloud Resource Monitoring Zheng Zhi yun, Song Cai hua, Li Dun, Zhang Xing -jin, Lu Li-ping

Research for the Data Transmission Model in Cloud Resource Monitoring Zheng Zhi yun, Song Cai hua, Li Dun, Zhang Xing -jin, Lu Li-ping Research for the Data Transmission Model in Cloud Resource Monitoring 1 Zheng Zhi-yun, Song Cai-hua, 3 Li Dun, 4 Zhang Xing-jin, 5 Lu Li-ping 1,,3,4 School of Information Engineering, Zhengzhou University,

More information

Figure 1. The cloud scales: Amazon EC2 growth [2].

Figure 1. The cloud scales: Amazon EC2 growth [2]. - Chung-Cheng Li and Kuochen Wang Department of Computer Science National Chiao Tung University Hsinchu, Taiwan 300 shinji10343@hotmail.com, kwang@cs.nctu.edu.tw Abstract One of the most important issues

More information

packet retransmitting based on dynamic route table technology, as shown in fig. 2 and 3.

packet retransmitting based on dynamic route table technology, as shown in fig. 2 and 3. Implementation of an Emulation Environment for Large Scale Network Security Experiments Cui Yimin, Liu Li, Jin Qi, Kuang Xiaohui National Key Laboratory of Science and Technology on Information System

More information

A Load Balancing Method in SiCo Hierarchical DHT-based P2P Network

A Load Balancing Method in SiCo Hierarchical DHT-based P2P Network 1 Shuang Kai, 2 Qu Zheng *1, Shuang Kai Beijing University of Posts and Telecommunications, shuangk@bupt.edu.cn 2, Qu Zheng Beijing University of Posts and Telecommunications, buptquzheng@gmail.com Abstract

More information

Design and Implementation of IaaS platform based on tool migration Wei Ding

Design and Implementation of IaaS platform based on tool migration Wei Ding 4th International Conference on Mechatronics, Materials, Chemistry and Computer Engineering (ICMMCCE 2015) Design and Implementation of IaaS platform based on tool migration Wei Ding State Key Laboratory

More information

International Journal of Computer Science Trends and Technology (IJCST) Volume 3 Issue 3, May-June 2015

International Journal of Computer Science Trends and Technology (IJCST) Volume 3 Issue 3, May-June 2015 RESEARCH ARTICLE OPEN ACCESS Ensuring Reliability and High Availability in Cloud by Employing a Fault Tolerance Enabled Load Balancing Algorithm G.Gayathri [1], N.Prabakaran [2] Department of Computer

More information

International Journal of Computer Science Trends and Technology (IJCST) Volume 2 Issue 3, May-Jun 2014

International Journal of Computer Science Trends and Technology (IJCST) Volume 2 Issue 3, May-Jun 2014 RESEARCH ARTICLE OPEN ACCESS Survey of Optimization of Scheduling in Cloud Computing Environment Er.Mandeep kaur 1, Er.Rajinder kaur 2, Er.Sughandha Sharma 3 Research Scholar 1 & 2 Department of Computer

More information

A QoS-driven Resource Allocation Algorithm with Load balancing for

A QoS-driven Resource Allocation Algorithm with Load balancing for A QoS-driven Resource Allocation Algorithm with Load balancing for Device Management 1 Lanlan Rui, 2 Yi Zhou, 3 Shaoyong Guo State Key Laboratory of Networking and Switching Technology, Beijing University

More information

Abstract. 1. Introduction

Abstract. 1. Introduction A REVIEW-LOAD BALANCING OF WEB SERVER SYSTEM USING SERVICE QUEUE LENGTH Brajendra Kumar, M.Tech (Scholor) LNCT,Bhopal 1; Dr. Vineet Richhariya, HOD(CSE)LNCT Bhopal 2 Abstract In this paper, we describe

More information

CLOUD DATABASE ROUTE SCHEDULING USING COMBANATION OF PARTICLE SWARM OPTIMIZATION AND GENETIC ALGORITHM

CLOUD DATABASE ROUTE SCHEDULING USING COMBANATION OF PARTICLE SWARM OPTIMIZATION AND GENETIC ALGORITHM CLOUD DATABASE ROUTE SCHEDULING USING COMBANATION OF PARTICLE SWARM OPTIMIZATION AND GENETIC ALGORITHM *Shabnam Ghasemi 1 and Mohammad Kalantari 2 1 Deparment of Computer Engineering, Islamic Azad University,

More information

Round Robin with Server Affinity: A VM Load Balancing Algorithm for Cloud Based Infrastructure

Round Robin with Server Affinity: A VM Load Balancing Algorithm for Cloud Based Infrastructure J Inf Process Syst, Vol.9, No.3, September 2013 pissn 1976-913X eissn 2092-805X http://dx.doi.org/10.3745/jips.2013.9.3.379 Round Robin with Server Affinity: A VM Load Balancing Algorithm for Cloud Based

More information

The International Journal Of Science & Technoledge (ISSN 2321 919X) www.theijst.com

The International Journal Of Science & Technoledge (ISSN 2321 919X) www.theijst.com THE INTERNATIONAL JOURNAL OF SCIENCE & TECHNOLEDGE Efficient Parallel Processing on Public Cloud Servers using Load Balancing Manjunath K. C. M.Tech IV Sem, Department of CSE, SEA College of Engineering

More information

An Optimization Model of Load Balancing in P2P SIP Architecture

An Optimization Model of Load Balancing in P2P SIP Architecture An Optimization Model of Load Balancing in P2P SIP Architecture 1 Kai Shuang, 2 Liying Chen *1, First Author, Corresponding Author Beijing University of Posts and Telecommunications, shuangk@bupt.edu.cn

More information

LOAD BALANCING IN CLOUD COMPUTING

LOAD BALANCING IN CLOUD COMPUTING LOAD BALANCING IN CLOUD COMPUTING Neethu M.S 1 PG Student, Dept. of Computer Science and Engineering, LBSITW (India) ABSTRACT Cloud computing is emerging as a new paradigm for manipulating, configuring,

More information

http://www.paper.edu.cn

http://www.paper.edu.cn 5 10 15 20 25 30 35 A platform for massive railway information data storage # SHAN Xu 1, WANG Genying 1, LIU Lin 2** (1. Key Laboratory of Communication and Information Systems, Beijing Municipal Commission

More information

Study of Various Load Balancing Techniques in Cloud Environment- A Review

Study of Various Load Balancing Techniques in Cloud Environment- A Review International Journal of Computer Sciences and Engineering Open Access Review Paper Volume-4, Issue-04 E-ISSN: 2347-2693 Study of Various Load Balancing Techniques in Cloud Environment- A Review Rajdeep

More information

Analysis of Job Scheduling Algorithms in Cloud Computing

Analysis of Job Scheduling Algorithms in Cloud Computing Analysis of Job Scheduling s in Cloud Computing Rajveer Kaur 1, Supriya Kinger 2 1 Research Fellow, Department of Computer Science and Engineering, SGGSWU, Fatehgarh Sahib, India, Punjab (140406) 2 Asst.Professor,

More information

Load Balancing in Fault Tolerant Video Server

Load Balancing in Fault Tolerant Video Server Load Balancing in Fault Tolerant Video Server # D. N. Sujatha*, Girish K*, Rashmi B*, Venugopal K. R*, L. M. Patnaik** *Department of Computer Science and Engineering University Visvesvaraya College of

More information

Object Request Reduction in Home Nodes and Load Balancing of Object Request in Hybrid Decentralized Web Caching

Object Request Reduction in Home Nodes and Load Balancing of Object Request in Hybrid Decentralized Web Caching 2012 2 nd International Conference on Information Communication and Management (ICICM 2012) IPCSIT vol. 55 (2012) (2012) IACSIT Press, Singapore DOI: 10.7763/IPCSIT.2012.V55.5 Object Request Reduction

More information

Towards Heuristic Web Services Composition Using Immune Algorithm

Towards Heuristic Web Services Composition Using Immune Algorithm Towards Heuristic Web Services Composition Using Immune Algorithm Jiuyun Xu School of Computer & Communication Engineering China University of Petroleum xujiuyun@ieee.org Stephan Reiff-Marganiec Department

More information

2. Research and Development on the Autonomic Operation. Control Infrastructure Technologies in the Cloud Computing Environment

2. Research and Development on the Autonomic Operation. Control Infrastructure Technologies in the Cloud Computing Environment R&D supporting future cloud computing infrastructure technologies Research and Development on Autonomic Operation Control Infrastructure Technologies in the Cloud Computing Environment DEMPO Hiroshi, KAMI

More information

International Journal of Computer & Organization Trends Volume21 Number1 June 2015 A Study on Load Balancing in Cloud Computing

International Journal of Computer & Organization Trends Volume21 Number1 June 2015 A Study on Load Balancing in Cloud Computing A Study on Load Balancing in Cloud Computing * Parveen Kumar * Er.Mandeep Kaur Guru kashi University,Talwandi Sabo Guru kashi University,Talwandi Sabo Abstract: Load Balancing is a computer networking

More information

This is an author-deposited version published in : http://oatao.univ-toulouse.fr/ Eprints ID : 12902

This is an author-deposited version published in : http://oatao.univ-toulouse.fr/ Eprints ID : 12902 Open Archive TOULOUSE Archive Ouverte (OATAO) OATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible. This is an author-deposited

More information

Genetic Algorithm Based Interconnection Network Topology Optimization Analysis

Genetic Algorithm Based Interconnection Network Topology Optimization Analysis Genetic Algorithm Based Interconnection Network Topology Optimization Analysis 1 WANG Peng, 2 Wang XueFei, 3 Wu YaMing 1,3 College of Information Engineering, Suihua University, Suihua Heilongjiang, 152061

More information

A Network Simulation Experiment of WAN Based on OPNET

A Network Simulation Experiment of WAN Based on OPNET A Network Simulation Experiment of WAN Based on OPNET 1 Yao Lin, 2 Zhang Bo, 3 Liu Puyu 1, Modern Education Technology Center, Liaoning Medical University, Jinzhou, Liaoning, China,yaolin111@sina.com *2

More information

Heterogeneous Workload Consolidation for Efficient Management of Data Centers in Cloud Computing

Heterogeneous Workload Consolidation for Efficient Management of Data Centers in Cloud Computing Heterogeneous Workload Consolidation for Efficient Management of Data Centers in Cloud Computing Deep Mann ME (Software Engineering) Computer Science and Engineering Department Thapar University Patiala-147004

More information

QoS EVALUATION OF CLOUD SERVICE ARCHITECTURE BASED ON ANP

QoS EVALUATION OF CLOUD SERVICE ARCHITECTURE BASED ON ANP QoS EVALUATION OF CLOUD SERVICE ARCHITECTURE BASED ON ANP Mingzhe Wang School of Automation Huazhong University of Science and Technology Wuhan 430074, P.R.China E-mail: mingzhew@gmail.com Yu Liu School

More information

The Power Marketing Information System Model Based on Cloud Computing

The Power Marketing Information System Model Based on Cloud Computing 2011 International Conference on Computer Science and Information Technology (ICCSIT 2011) IPCSIT vol. 51 (2012) (2012) IACSIT Press, Singapore DOI: 10.7763/IPCSIT.2012.V51.96 The Power Marketing Information

More information

Keywords Load balancing, Dispatcher, Distributed Cluster Server, Static Load balancing, Dynamic Load balancing.

Keywords Load balancing, Dispatcher, Distributed Cluster Server, Static Load balancing, Dynamic Load balancing. Volume 5, Issue 7, July 2015 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com A Hybrid Algorithm

More information

Efficient Parallel Processing on Public Cloud Servers Using Load Balancing

Efficient Parallel Processing on Public Cloud Servers Using Load Balancing Efficient Parallel Processing on Public Cloud Servers Using Load Balancing Valluripalli Srinath 1, Sudheer Shetty 2 1 M.Tech IV Sem CSE, Sahyadri College of Engineering & Management, Mangalore. 2 Asso.

More information

DECENTRALIZED LOAD BALANCING IN HETEROGENEOUS SYSTEMS USING DIFFUSION APPROACH

DECENTRALIZED LOAD BALANCING IN HETEROGENEOUS SYSTEMS USING DIFFUSION APPROACH DECENTRALIZED LOAD BALANCING IN HETEROGENEOUS SYSTEMS USING DIFFUSION APPROACH P.Neelakantan Department of Computer Science & Engineering, SVCET, Chittoor pneelakantan@rediffmail.com ABSTRACT The grid

More information

AN EFFICIENT LOAD BALANCING APPROACH IN CLOUD SERVER USING ANT COLONY OPTIMIZATION

AN EFFICIENT LOAD BALANCING APPROACH IN CLOUD SERVER USING ANT COLONY OPTIMIZATION AN EFFICIENT LOAD BALANCING APPROACH IN CLOUD SERVER USING ANT COLONY OPTIMIZATION Shanmuga Priya.J 1, Sridevi.A 2 1 PG Scholar, Department of Information Technology, J.J College of Engineering and Technology

More information

Sla Aware Load Balancing Algorithm Using Join-Idle Queue for Virtual Machines in Cloud Computing

Sla Aware Load Balancing Algorithm Using Join-Idle Queue for Virtual Machines in Cloud Computing Sla Aware Load Balancing Using Join-Idle Queue for Virtual Machines in Cloud Computing Mehak Choudhary M.Tech Student [CSE], Dept. of CSE, SKIET, Kurukshetra University, Haryana, India ABSTRACT: Cloud

More information

Tasks Scheduling Game Algorithm Based on Cost Optimization in Cloud Computing

Tasks Scheduling Game Algorithm Based on Cost Optimization in Cloud Computing Journal of Computational Information Systems 11: 16 (2015) 6037 6045 Available at http://www.jofcis.com Tasks Scheduling Game Algorithm Based on Cost Optimization in Cloud Computing Renfeng LIU 1, Lijun

More information

Open Access Research on Database Massive Data Processing and Mining Method based on Hadoop Cloud Platform

Open Access Research on Database Massive Data Processing and Mining Method based on Hadoop Cloud Platform Send Orders for Reprints to reprints@benthamscience.ae The Open Automation and Control Systems Journal, 2014, 6, 1463-1467 1463 Open Access Research on Database Massive Data Processing and Mining Method

More information

SCHEDULING IN CLOUD COMPUTING

SCHEDULING IN CLOUD COMPUTING SCHEDULING IN CLOUD COMPUTING Lipsa Tripathy, Rasmi Ranjan Patra CSA,CPGS,OUAT,Bhubaneswar,Odisha Abstract Cloud computing is an emerging technology. It process huge amount of data so scheduling mechanism

More information

Cost Effective Selection of Data Center in Cloud Environment

Cost Effective Selection of Data Center in Cloud Environment Cost Effective Selection of Data Center in Cloud Environment Manoranjan Dash 1, Amitav Mahapatra 2 & Narayan Ranjan Chakraborty 3 1 Institute of Business & Computer Studies, Siksha O Anusandhan University,

More information

An Architecture Model of Sensor Information System Based on Cloud Computing

An Architecture Model of Sensor Information System Based on Cloud Computing An Architecture Model of Sensor Information System Based on Cloud Computing Pengfei You, Yuxing Peng National Key Laboratory for Parallel and Distributed Processing, School of Computer Science, National

More information

CHAPTER 1 INTRODUCTION

CHAPTER 1 INTRODUCTION CHAPTER 1 INTRODUCTION 1.1 Background The command over cloud computing infrastructure is increasing with the growing demands of IT infrastructure during the changed business scenario of the 21 st Century.

More information

Optimization of Distributed Crawler under Hadoop

Optimization of Distributed Crawler under Hadoop MATEC Web of Conferences 22, 0202 9 ( 2015) DOI: 10.1051/ matecconf/ 2015220202 9 C Owned by the authors, published by EDP Sciences, 2015 Optimization of Distributed Crawler under Hadoop Xiaochen Zhang*

More information

Profit-driven Cloud Service Request Scheduling Under SLA Constraints

Profit-driven Cloud Service Request Scheduling Under SLA Constraints Journal of Information & Computational Science 9: 14 (2012) 4065 4073 Available at http://www.joics.com Profit-driven Cloud Service Request Scheduling Under SLA Constraints Zhipiao Liu, Qibo Sun, Shangguang

More information

Load Balancing Strategy of Cloud Computing based on Artificial Bee

Load Balancing Strategy of Cloud Computing based on Artificial Bee Load Balancing Strategy of Cloud Computing based on Artificial Bee Algorithm 1 Jing Yao*, 2 Ju-hou He 1 *, Dept. of Computer Science Shaanxi Normal University Xi'an, China, ruirui8718@163.com 2, Dept.

More information

LOAD BALANCING IN CLOUD COMPUTING USING PARTITIONING METHOD

LOAD BALANCING IN CLOUD COMPUTING USING PARTITIONING METHOD LOAD BALANCING IN CLOUD COMPUTING USING PARTITIONING METHOD Mitesh Patel 1, Kajal Isamaliya 2, Hardik kadia 3, Vidhi Patel 4 CE Department, MEC, Surat, Gujarat, India 1 Asst.Professor, CSE Department,

More information

Load Balancing Algorithms in Cloud Environment

Load Balancing Algorithms in Cloud Environment International Conference on Systems, Science, Control, Communication, Engineering and Technology 50 International Conference on Systems, Science, Control, Communication, Engineering and Technology 2015

More information

A Hybrid Scheduling Approach for Scalable Heterogeneous Hadoop Systems

A Hybrid Scheduling Approach for Scalable Heterogeneous Hadoop Systems A Hybrid Scheduling Approach for Scalable Heterogeneous Hadoop Systems Aysan Rasooli Department of Computing and Software McMaster University Hamilton, Canada Email: rasooa@mcmaster.ca Douglas G. Down

More information

Back-End Forwarding Scheme in Server Load Balancing using Client Virtualization

Back-End Forwarding Scheme in Server Load Balancing using Client Virtualization Back-End Forwarding Scheme in Server Load Balancing using Client Virtualization Shreyansh Kumar School of Computing Science and Engineering VIT University Chennai Campus Parvathi.R, Ph.D Associate Professor-

More information

Dynamic resource management for energy saving in the cloud computing environment

Dynamic resource management for energy saving in the cloud computing environment Dynamic resource management for energy saving in the cloud computing environment Liang-Teh Lee, Kang-Yuan Liu, and Hui-Yang Huang Department of Computer Science and Engineering, Tatung University, Taiwan

More information

A Survey on Load Balancing Algorithms in Cloud Environment

A Survey on Load Balancing Algorithms in Cloud Environment A Survey on Load s in Cloud Environment M.Aruna Assistant Professor (Sr.G)/CSE Erode Sengunthar Engineering College, Thudupathi, Erode, India D.Bhanu, Ph.D Associate Professor Sri Krishna College of Engineering

More information

Multiobjective Multicast Routing Algorithm

Multiobjective Multicast Routing Algorithm Multiobjective Multicast Routing Algorithm Jorge Crichigno, Benjamín Barán P. O. Box 9 - National University of Asunción Asunción Paraguay. Tel/Fax: (+9-) 89 {jcrichigno, bbaran}@cnc.una.py http://www.una.py

More information

Phoenix Cloud: Consolidating Different Computing Loads on Shared Cluster System for Large Organization

Phoenix Cloud: Consolidating Different Computing Loads on Shared Cluster System for Large Organization Phoenix Cloud: Consolidating Different Computing Loads on Shared Cluster System for Large Organization Jianfeng Zhan, Lei Wang, Bibo Tu, Yong Li, Peng Wang, Wei Zhou, Dan Meng Institute of Computing Technology

More information

HUAWEI OceanStor 9000. Load Balancing Technical White Paper. Issue 01. Date 2014-06-20 HUAWEI TECHNOLOGIES CO., LTD.

HUAWEI OceanStor 9000. Load Balancing Technical White Paper. Issue 01. Date 2014-06-20 HUAWEI TECHNOLOGIES CO., LTD. HUAWEI OceanStor 9000 Load Balancing Technical Issue 01 Date 2014-06-20 HUAWEI TECHNOLOGIES CO., LTD. Copyright Huawei Technologies Co., Ltd. 2014. All rights reserved. No part of this document may be

More information

A Novel Approach of Load Balancing Strategy in Cloud Computing

A Novel Approach of Load Balancing Strategy in Cloud Computing A Novel Approach of Load Balancing Strategy in Cloud Computing Antony Thomas 1, Krishnalal G 2 PG Scholar, Dept of Computer Science, Amal Jyothi College of Engineering, Kanjirappally, Kerala, India 1 Assistant

More information

High performance computing network for cloud environment using simulators

High performance computing network for cloud environment using simulators High performance computing network for cloud environment using simulators Ajith Singh. N 1 and M. Hemalatha 2 1 Ph.D, Research Scholar (CS), Karpagam University, Coimbatore, India 2 Prof & Head, Department

More information

Performance Analysis of VM Scheduling Algorithm of CloudSim in Cloud Computing

Performance Analysis of VM Scheduling Algorithm of CloudSim in Cloud Computing IJECT Vo l. 6, Is s u e 1, Sp l-1 Ja n - Ma r c h 2015 ISSN : 2230-7109 (Online) ISSN : 2230-9543 (Print) Performance Analysis Scheduling Algorithm CloudSim in Cloud Computing 1 Md. Ashifuddin Mondal,

More information

A Scheme for Implementing Load Balancing of Web Server

A Scheme for Implementing Load Balancing of Web Server Journal of Information & Computational Science 7: 3 (2010) 759 765 Available at http://www.joics.com A Scheme for Implementing Load Balancing of Web Server Jianwu Wu School of Politics and Law and Public

More information

Telecom Data processing and analysis based on Hadoop

Telecom Data processing and analysis based on Hadoop COMPUTER MODELLING & NEW TECHNOLOGIES 214 18(12B) 658-664 Abstract Telecom Data processing and analysis based on Hadoop Guofan Lu, Qingnian Zhang *, Zhao Chen Wuhan University of Technology, Wuhan 4363,China

More information

A Review of Load Balancing Algorithms for Cloud Computing

A Review of Load Balancing Algorithms for Cloud Computing www.ijecs.in International Journal Of Engineering And Computer Science ISSN:2319-7242 Volume - 3 Issue -9 September, 2014 Page No. 8297-8302 A Review of Load Balancing Algorithms for Cloud Computing Dr.G.N.K.Sureshbabu

More information

Dynamic Load Balancing of Virtual Machines using QEMU-KVM

Dynamic Load Balancing of Virtual Machines using QEMU-KVM Dynamic Load Balancing of Virtual Machines using QEMU-KVM Akshay Chandak Krishnakant Jaju Technology, College of Engineering, Pune. Maharashtra, India. Akshay Kanfade Pushkar Lohiya Technology, College

More information

A Virtual Machine Dynamic Migration Scheduling Model Based on MBFD Algorithm

A Virtual Machine Dynamic Migration Scheduling Model Based on MBFD Algorithm International Journal of Comuter Theory and Engineering, Vol. 7, No. 4, August 2015 A Virtual Machine Dynamic Migration Scheduling Model Based on MBFD Algorithm Xin Lu and Zhuanzhuan Zhang Abstract This

More information

Improved PSO-based Task Scheduling Algorithm in Cloud Computing

Improved PSO-based Task Scheduling Algorithm in Cloud Computing Journal of Information & Computational Science 9: 13 (2012) 3821 3829 Available at http://www.joics.com Improved PSO-based Tas Scheduling Algorithm in Cloud Computing Shaobin Zhan, Hongying Huo Shenzhen

More information