Dynamic Adaptive Feedback of Load Balancing Strategy

Size: px
Start display at page:

Download "Dynamic Adaptive Feedback of Load Balancing Strategy"

Transcription

1 Journal of Information & Computational Science 8: 10 (2011) Available at Dynamic Adaptive Feedback of Load Balancing Strategy Hongbin Wang a,b, Zhiyi Fang a,, Shuang Cui a a College of Computer Science and Technology, Jilin University, Changchun , China b School of Computer Science and Technology, Changchun University of Science and Technology Changchun , China Abstract Cluster of load balancing strategy is the key to improve cluster s performance. In this paper, we propose the dynamic adaptive feedback of load balancing strategy. The value of server performance and the value of server nodes dynamic load as assessment the server nodes load capacity indicators. The sub-server nodes adaptively collect their own load information, and then make load information sending to the load balancer. It reduces communication overhead and reduces the burden of the load balancer. To avoid the load balancer and single server node instantaneous overloading, this paper introduces the parameter of server node load redundancy. The load balancer according each node s weight information distributes task; effectively addresses the cluster inner server node s load balancing issue. Keywords: Load Balancer; Dynamic Adaptive; Load Redundancy Value; Binary Sort Tree 1 Introduction At present, the major areas of network, such as the enterprise networks, campus networks and wide area networks, the development of volume of business has gone beyond the past estimate, which is the most considerable estimate. The new network technology rapidly advance, the optimality network configuration is also not well supported the rapid development needs of the network applications. In fact, the internet scale will be doubled in every hundred days. The customers want to get the 7*24 hours of uninterrupted application and faster system response time, and they wouldn t like to frequently see a site system occurs problem. Especially, with the sharp rise in all kinds of traffic, data traffic flow and accessing number rapid expansion, the network data traffic, the solving problem capacity and the calculation relative intensity gradually expanded. The single device unable to undertake such big carrying capacity. How to reasonable and effective distribution the volume of business for network worth consider. It can help to solve a problem, which is some devices are busy, while the other equipments are idle. This problem is needs to be resolved. So, with a series of urgent problems need to be solved, the load balancing mechanism is appearing [1]. Project supported by the SME Innovation Foundation of China (No. SC ). Corresponding author. address: (Zhiyi Fang) / Copyright 2011 Binary Information Press October 2011

2 1902 H. Wang et al. / Journal of Information & Computational Science 8: 10 (2011) The traditional Web requests allocation algorithm is mainly based on access requests Poisson arrival and corresponding time exponential distribution to assumes, the commonly used Round- Robin (Round-robin), Weighted Round-robin (Weighted Round-robin), the Least-connection Scheduling (Least-connection) and the weighted Least-connections Scheduling (Weighted Leastconnection) [3], and so on. As more and more web pages using dynamic embedded object technology and database query tasks, making different task requests have different workloads. Between static pages and dynamic web pages have 10 times or 100 times difference [4]. Meanwhile, the nodes in the cluster have difference performances that the same task request in different servers have different impact. Therefore, how to accurately assess the workload on the server has become the key to achieve load balancing cluster. These algorithms do not take into account the user tasks requests difference and each node server cluster performance differences, these algorithms only use a small amount of static characteristics of information, suitable for small-scale, single-configuration static pages information service system [5]. These algorithms can only be approximate load balanced distribution, and can not effectively solve the cluster server load balancing problems. These load balancing algorithms can not take full advantage of the processing power of each node server. The dynamic load balancing method is more advantages than static load balancing approach [2]. For example, the common dynamic load balancing algorithm which is weighted least-connection algorithm, each server node with the corresponding weight shows the processing performance, the value is W=C/R, where C is the performance of the server node, R is the current request tasks connected number, the algorithm based on the number of server connection request and sub-task performance as server node indicator parameter to assign task. When increasing the number of concurrent connection requests, the load balancer is also increasing its processing tasks, the load balancer will cause excessive load on their own, and result the load balancer becomes bottleneck of the system services. In this paper, we propose the dynamic adaptive feedback of load balancing strategy. The value of server performance and the value of server nodes dynamic load as assessment the server nodes load capacity indicators. The sub-server nodes adaptively collect their own load information, and then make load information sending to the load balancer, avoid load balancer overloading lead to system bottleneck; reducing the complexity of load balancing algorithm. Effectively addresses the cluster inner server node s load balancing issue. The rest of this paper is organized into four sections. In Section 2, the major issues and challenges in designing dynamic adaptive feedback of load balancing strategy is listed. In Section 3, the weight sorting algorithm will be explained briefly. In Section 4, the performance test for dynamic adaptive feedback of load balancing strategy. In Section 5, concludes the paper. 2 Dynamic Adaptive Feedback of Load Balancing Strategy The dynamic load scheduling strategy has two ways: the first is the centralized load scheduling strategy, the second is distributed load scheduling strategy. The load balancer is responsible for collecting sub-server nodes information is centralized scheduling strategy, if the sub-server nodes self-adaptive collects their information, and then according to its own state initiative sends the information to the load balancer, this is called distributed load scheduling strategy. The load scheduler based on the current sending information to execute decision-making and scheduling.

3 H. Wang et al. / Journal of Information & Computational Science 8: 10 (2011) The load scheduler does not go to collect their server information, thus reducing the load which comes collecting the additional traffic information overhead, reduces the burden of the load scheduler. Therefore, the load balancer needs to accord a certain algorithm to detect current server node actual load, and forecasts load redundancy for each server node within the time slice T. How to correctly detect each server node s true load? Firstly, we must consider the server node itself performance, where the introduction of server performance indicators, including CPU frequency, memory capacity, the system I/O utilization, number of processes, response time, network bandwidth, and so on. Secondly, we calculate the dynamic load value of each server node, where the load indicator parameters, including the rate of CPU occupy, the memory utilization, the system I/O utilization, total number of processes, response time and the rate of network bandwidth occupy, and so on. Finally, we should consider that every parameter actual change in a certain period of time, given a reasonable algorithm to determine the weight of each server node, achieved the cluster system load balancing. 2.1 Server Node Parameters Definition We assume server performance indicator is P(S i ), where S i is the server node i, iin(1 n), A = 1. P (S i ) = A 1 P cpu (S i ) + A 2 P memory (S i ) + A 3 P io (S i ) + A 4 P process (S i ) + A 5 P net (S i ) + A 6 P response (S i ) (1) where P cpu is CPU frequency, P memory is memory capacity, P io is the speed of system I/O, P process is the total number of processes, P net is network speed, P response is the response time. We want to detect each server node real load information, the sever node must real-time feedback the node load value, as it is real-time feedback information, every time T, the server node collects the node load value, we assume the server load value is L(S i ), and introduces dynamic parameters to calculate the node load value. In experiment, we select the dynamic parameters including the rate of CPU occupy, the memory utilization, the system I/O utilization, total number of processes, response time and the rate of network bandwidth occupy, we also give each parameter set a coefficient A( A=1), the users can set and change the A value in the actual test system environment, the dynamic load information much more real, to achieve the system best. We assume the server real dynamics load value is L(S i ), where S i is the server node i, i (1 n), A = 1. L(S i ) = A 1 L cpu (S i ) + A 2 L memory (S i ) + A 3 L io (S i ) + A 4 L process (S i ) + A 5 L net (S i ) + A 6 L response (S i ) (2) where L cpu is the CPU utilization, L memory is the memory utilization, L io is I/O utilization, L process is the total number for process, L net is the network utilization, L response is response time. When the server node access the cluster system, each node records its own static parameters, calculated the server performance indicator P, submitted P to the load balancer, from now on, every other time slice T, the sub-server nodes sends its load information L to load balancer.

4 1904 H. Wang et al. / Journal of Information & Computational Science 8: 10 (2011) Load Balancing Optimization Idea The load balancing strategy features that a certain moment the load balancer receives request task information, according to sub-node receives the current load information, the load balancer selects the lightest load sub-node to distribute tasks.the sub-node sends its current information in a fixed time interval T, if there are more than one task scheduler request arrive the load balancer at any time within the time slice T, the load scheduler recorded sub-nodes load information have not been updated, the load scheduler stored each service node s machine performance P and load L were not changed, if the load scheduler according this load information distribution tasks requests, all the requests are assigned to a sub-server node, it may cause a single node excessive load, resulting overload. Therefore, when the load balancing strategy takes into considering the server performance and server load information, it must also consider a few questions in the time slice T: (1) Within the time slice T, the sub-server nodes may be part of the job task request has been completed, so that the node load is reduced or even idle, the performance of each sub-server nodes, load state change and the current load information is linked to each other. The sub-server nodes load under a certain circumstances, because of the server machine has different performance, the server node handle the request task ability also difference; (2) Within the time slice T, the task requests sends to load balancer, at this time load information of each node is not updated, every time the load balancer assigned task to each sub-node will increase the node load value. We introduce a parameter load redundant R, to more accurately records the task request, effectively predicts each node load capacity, while minimizing the complexity of the algorithm, and use it to measure each sub-server nodes can increase the load capacity in one time, the load capacity of its own to make effective predictions. The load redundant R and machine performance P, the node load values L, time t and the increase number of requests X n have a certain relationship, we assume the sub-server nodes load redundant values as follows: R(S i ) = R n + K 1 t P (S i )/L(S i ) K 2 X n /P (S i ) (3) where R n is the last time slice arrives, the server node send load redundancy, K 1 t P (S i )/L(S i ) starting from the last time slice, in the time t completion load, K 2 X n /P (S i ) is t time in the sub-server nodes for each request to add new tasks to increase the load, X n is the number of task requests. The load balancer collects each sub-server nodes load information, related to the acquisition time, in the actual experimental environment, if so often to collect the sub-server node load information will also increase the burden on each node. On the other hand, each sub-node collects related parameters of the load information, if the frequency of collection, then all the relevant parameters is changing in real time, which will lead to the sub-server nodes load information appear drastic shake. Therefore, to avoid this from happening, the acquisition time period should not too long or too short, usually the acquisition time period is set to 5 to 10 seconds is appropriate. In this experimental environment, we set the acquisition cycle time slice T=10s, every 10s, each sub-server nodes sends load information to load balancer. According to the above formula for load redundant R, the initial value of R should be: R 0 = 10 K 1 P (S i )/L(S i ) K 2 /P (S i ) (4)

5 H. Wang et al. / Journal of Information & Computational Science 8: 10 (2011) where R 0 is the first time slice T completed, the each server current load redundancy. In order to make an effectively predicting load capacity of the sub-server nodes, sets a minimum load redundancy R min, when the load redundant of sub-server node is greater than current load redundant R min, the sub-server node has rights to assigned the task request, so that the sub-server nodes have a load redundant free to avoid itself excessive load appears. When the server node processed a task, it is load redundant value should be amended: R = R i + K 1 t P (S i )/L K 2 /P (S i ) (5) When completed a task for each treatment, the load redundant value is automatically changed once. In the acquisition cycle time slice T, There isn t new load information sending to the load balancer, L(S i ) is the last time slice arrived data, so every time after a task completed, L values will be changed once, estimated the current node value of node load, L value amended as follows: L = L(S i ) K 1 t P (S i )/L(S i ) + K 2 /P (S i ) (6) When a new time slice arrives, the sub-server node sends the current accurate load information to load balancer, then the next time slice will be the new L(S i ) started to re-estimate calculation. The chip in the acquisition cycle time, as much as possible to estimate the node load value and the true load redundancy. 2.3 Weight Calculation The server performance and dynamic load value has a corresponding calculation methods, combination of these two important parameters to more accurately estimate the current server node load capacity, we assume the weight W: W (S i ) = L(S i )/P (S i ) (7) The server performance using the static parameters of P, when the server node access the cluster to collects load information, The dynamic load value L is the nodes current load state, L is larger, the load number of tasks is greater. Therefore, W is greater, the current server node s load is greater, it can be assigned tasks are smaller because of the load capacity is weak. 3 The Weight Sorting Algorithm 3.1 The Binary Sort Tree Feature The properties of BST can be obtained: (1) The any node x of Binary sort tree, its left (right) sub tree of any node y (if it is present) the keywords must be small (large) the x keywords. (2) Binary sort tree, each node keywords is unique. In practice application, we can not guarantee the data set elements keywords different from each other. So, in the binary sort tree definition, we can make the less than amend to greater than or equal of BST s character (1), or make

6 1906 H. Wang et al. / Journal of Information & Computational Science 8: 10 (2011) the greater than amend to less than or equal of BST s character(2), and even to modify these two characters simultaneously. (3) According to middle order traversal the tree resulting middle order sequence which is an increase sequence [7]. 3.2 Algorithm Process (1) Binary sort tree: for (i =0; i< MAX CLIENT;i++) { } if (R(S i ) R min ) { } else Then the node is not inserted into a binary tree; insert the node; All nodes in the left sub tree are less than the root value; All nodes in the right sub tree are greater than the root value; (2) Order traversal binary sort tree According to the nature of binary sort tree, the order traversal results is an increasing sequence, the weight of each server node will order from small to large. (3) Allocation tasks According to the order traversal results, followed allocate tasks to node, after assigned tasks to nodes, the value of load redundancy R and load L is changed Cycle the above operation. The process figure shows as Fig Performance Test We use NAT structure construct a LVS cluster [10], which has 3 node servers. The node server respectively configures with dual-cpu server, single-cpu server and ordinary PC. Another using 2 PC as a client to simulate the stress test for cluster, the stress testing tool is JMeter. We divide 7 groups tested in simulation, the first group of 100 user requests, next is 200, 300, 400, 500, 600, 700 access request, we respectively measure the response time of user request for the current system. The comparison test selects algorithm between weighted Round-Robin and dynamic adaptive feedback method. The users request response time of weighted Round-Robin and dynamic adaptive feedback method are shown as Table 1. By Fig. 2 Comparison of the response time of user requests reflect their impact on system throughput. With the number of user requests increased the dynamic adaptive feedback load

7 H. Wang et al. / Journal of Information & Computational Science 8: 10 (2011) Start N Calculate R(S i ) Allocates task to node N R(S i )>R min? Y Node inserts into binary tree Request is over? Y End Fig. 1: Algorithm process Weighted round-robin Dynamic adaptive feedback Response time (ms) Current connections Fig. 2: Comparison of the response time Table 1: Response time of user requests (ms) 1 group 2 group 3 group 4 group 5 group 6 group 7 group CurrentConnections W eightround Robin DynamicAdaptiveF eedback balancing strategy significantly improves the system throughput, better balance the system load, and effectively improves the overall cluster system performance.

8 1908 H. Wang et al. / Journal of Information & Computational Science 8: 10 (2011) Conclusion This paper proposed dynamic feedback load balancing strategy. The traditional collected load information of all nodes by load balancer in a centralized scheduling working was improved, every node server submits its own dynamic load information to load balancer, and it reduces communication overhead and reduces the burden of the load balancer. To avoid the load balancer and single server node instantaneous overloading, this paper introduced the parameter of server node load redundancy, effectively predicts the load capacity of each node. We utilize the middle order traversal binary tree sorting algorithm sorts the nodes weight, then the load balancer distributes task to sub-server nodes. By experiment comparing between weighted Round-Robin and dynamic adaptive feedback load balancing strategy, the dynamic adaptive feedback load balancing strategy effectively improved the system s load balancing capabilities. References [1] Mark Baker, Cluster Computing White Paper, University of Portsmouth, UK, 2000 [2] V. Cardellini, M. Colajanni, P. S. Yu, Dynamic load balancing on web server systems. IEEE internet Computing, 8(6), 1999, [3] M. Colajanni, et al, Dynamic load balancing in geographically distributed heterogeneous web servers, in: Proc. of 18th IEEE Int 1 Conf. On Distributed Computing System (ICDCS 1998), Amsterdam, the Netherlands, 1998, [4] Iyengar Arun, MacNair Ed, Nguyen Thao, An analysis of web server performance, In: Proceeding of Global Telecommunications Conference, 1997, [5] Casslicchio Emiliano, Tucci Salvatore, Static and dynamic scheduling algorithm for scalable web server farm, In: Proceedings of the IEEE 9th Euro micro Workshop on Parallel and Distributed Proceeding, 2001, [6] Hideo Taniguchi, Parallel Processing and Distributed Processing, Japan: Corona Publishing Co, LTD, 2003, [7] Mark Allen Weiss, Data Structures and Algorithm Analysis in C (Second Edition), [8] Xiaofang Zhang, Guozheng Hu, High-availability clustering technology research and application, Computer Engineering, 29(4), 2003, [9] R. B. Bunt, D. L. Eager, F. M. Sstart, et al, Archiving load balancing and effective caching in clustered web servers, Proc of the 4th International Web Caching Workshop, 1999, [10]

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

A Method of Cloud Resource Load Balancing Scheduling Based on Improved Adaptive Genetic Algorithm Journal of Information & Computational Science 9: 16 (2012) 4801 4809 Available at http://www.joics.com A Method of Cloud Resource Load Balancing Scheduling Based on Improved Adaptive Genetic Algorithm

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

Implementing Parameterized Dynamic Load Balancing Algorithm Using CPU and Memory

Implementing Parameterized Dynamic Load Balancing Algorithm Using CPU and Memory Implementing Parameterized Dynamic Balancing Algorithm Using CPU and Memory Pradip Wawge 1, Pritish Tijare 2 Master of Engineering, Information Technology, Sipna college of Engineering, Amravati, Maharashtra,

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

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

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

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

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

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

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

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

Load Balancing of Web Server System Using Service Queue Length

Load Balancing of Web Server System Using Service Queue Length Load Balancing of Web Server System Using Service Queue Length Brajendra Kumar 1, Dr. Vineet Richhariya 2 1 M.tech Scholar (CSE) LNCT, Bhopal 2 HOD (CSE), LNCT, Bhopal Abstract- In this paper, we describe

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

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

A Load Balancing Model Based on Cloud Partitioning for the Public Cloud

A Load Balancing Model Based on Cloud Partitioning for the Public Cloud IEEE TRANSACTIONS ON CLOUD COMPUTING YEAR 2013 A Load Balancing Model Based on Cloud Partitioning for the Public Cloud Gaochao Xu, Junjie Pang, and Xiaodong Fu Abstract: Load balancing in the cloud computing

More information

HyLARD: A Hybrid Locality-Aware Request Distribution Policy in Cluster-based Web Servers

HyLARD: A Hybrid Locality-Aware Request Distribution Policy in Cluster-based Web Servers TANET2007 臺 灣 網 際 網 路 研 討 會 論 文 集 二 HyLARD: A Hybrid Locality-Aware Request Distribution Policy in Cluster-based Web Servers Shang-Yi Zhuang, Mei-Ling Chiang Department of Information Management National

More information

A New Hybrid Load Balancing Algorithm in Grid Computing Systems

A New Hybrid Load Balancing Algorithm in Grid Computing Systems A New Hybrid Load Balancing Algorithm in Grid Computing Systems Leyli Mohammad Khanli 1, Behnaz Didevar 2 1 University of Tabriz, Department of Computer Science, 2 Department of Technical and Engineering,

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

Scheduling and Load Balancing in the Parallel ROOT Facility (PROOF)

Scheduling and Load Balancing in the Parallel ROOT Facility (PROOF) Scheduling and Load Balancing in the Parallel ROOT Facility (PROOF) Gerardo Ganis CERN E-mail: Gerardo.Ganis@cern.ch CERN Institute of Informatics, University of Warsaw E-mail: Jan.Iwaszkiewicz@cern.ch

More information

High Performance Cluster Support for NLB on Window

High Performance Cluster Support for NLB on Window High Performance Cluster Support for NLB on Window [1]Arvind Rathi, [2] Kirti, [3] Neelam [1]M.Tech Student, Department of CSE, GITM, Gurgaon Haryana (India) arvindrathi88@gmail.com [2]Asst. Professor,

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 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

LOAD BALANCING STRATEGY BASED ON CLOUD PARTITIONING CONCEPT

LOAD BALANCING STRATEGY BASED ON CLOUD PARTITIONING CONCEPT Journal homepage: www.mjret.in ISSN:2348-6953 LOAD BALANCING STRATEGY BASED ON CLOUD PARTITIONING CONCEPT Ms. Shilpa D.More 1, Prof. Arti Mohanpurkar 2 1,2 Department of computer Engineering DYPSOET, Pune,India

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

CHAPTER 3 LOAD BALANCING MECHANISM USING MOBILE AGENTS

CHAPTER 3 LOAD BALANCING MECHANISM USING MOBILE AGENTS 48 CHAPTER 3 LOAD BALANCING MECHANISM USING MOBILE AGENTS 3.1 INTRODUCTION Load balancing is a mechanism used to assign the load effectively among the servers in a distributed environment. These computers

More information

CHAPTER 5 WLDMA: A NEW LOAD BALANCING STRATEGY FOR WAN ENVIRONMENT

CHAPTER 5 WLDMA: A NEW LOAD BALANCING STRATEGY FOR WAN ENVIRONMENT 81 CHAPTER 5 WLDMA: A NEW LOAD BALANCING STRATEGY FOR WAN ENVIRONMENT 5.1 INTRODUCTION Distributed Web servers on the Internet require high scalability and availability to provide efficient services to

More information

@IJMTER-2015, All rights Reserved 355

@IJMTER-2015, All rights Reserved 355 e-issn: 2349-9745 p-issn: 2393-8161 Scientific Journal Impact Factor (SJIF): 1.711 International Journal of Modern Trends in Engineering and Research www.ijmter.com A Model for load balancing for the Public

More information

Research of Railway Wagon Flow Forecast System Based on Hadoop-Hazelcast

Research of Railway Wagon Flow Forecast System Based on Hadoop-Hazelcast International Conference on Civil, Transportation and Environment (ICCTE 2016) Research of Railway Wagon Flow Forecast System Based on Hadoop-Hazelcast Xiaodong Zhang1, a, Baotian Dong1, b, Weijia Zhang2,

More information

Multilevel Communication Aware Approach for Load Balancing

Multilevel Communication Aware Approach for Load Balancing Multilevel Communication Aware Approach for Load Balancing 1 Dipti Patel, 2 Ashil Patel Department of Information Technology, L.D. College of Engineering, Gujarat Technological University, Ahmedabad 1

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

A Content-based Dynamic Load-Balancing Algorithm for Heterogeneous Web Server Cluster

A Content-based Dynamic Load-Balancing Algorithm for Heterogeneous Web Server Cluster UDC 004.738, DOI: 10.2298/CSIS1001153Z A Content-based Dynamic Load-Balancing Algorithm for Heterogeneous Web Server Cluster Zhang Lin 1, Li Xiao-ping 2, and Su Yuan 2 1 School of Electricity and Information

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

Statistics Analysis for Cloud Partitioning using Load Balancing Model in Public Cloud

Statistics Analysis for Cloud Partitioning using Load Balancing Model in Public Cloud Statistics Analysis for Cloud Partitioning using Load Balancing Model in Public Cloud V. DIVYASRI 1, M.THANIGAVEL 2, T. SUJILATHA 3 1, 2 M. Tech (CSE) GKCE, SULLURPETA, INDIA v.sridivya91@gmail.com thaniga10.m@gmail.com

More information

Fault-Tolerant Framework for Load Balancing System

Fault-Tolerant Framework for Load Balancing System Fault-Tolerant Framework for Load Balancing System Y. K. LIU, L.M. CHENG, L.L.CHENG Department of Electronic Engineering City University of Hong Kong Tat Chee Avenue, Kowloon, Hong Kong SAR HONG KONG Abstract:

More information

A Low Cost Two-tier Architecture Model Implementation for High Availability Clusters For Application Load Balancing

A Low Cost Two-tier Architecture Model Implementation for High Availability Clusters For Application Load Balancing A Low Cost Two-tier Architecture Model Implementation for High Availability Clusters For Application Load Balancing A B M Moniruzzaman 1, Syed Akther Hossain IEEE Department of Computer Science and Engineering

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

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

The Improved Job Scheduling Algorithm of Hadoop Platform

The Improved Job Scheduling Algorithm of Hadoop Platform The Improved Job Scheduling Algorithm of Hadoop Platform Yingjie Guo a, Linzhi Wu b, Wei Yu c, Bin Wu d, Xiaotian Wang e a,b,c,d,e University of Chinese Academy of Sciences 100408, China b Email: wulinzhi1001@163.com

More information

MEASURING PERFORMANCE OF DYNAMIC LOAD BALANCING ALGORITHMS IN DISTRIBUTED COMPUTING APPLICATIONS

MEASURING PERFORMANCE OF DYNAMIC LOAD BALANCING ALGORITHMS IN DISTRIBUTED COMPUTING APPLICATIONS MEASURING PERFORMANCE OF DYNAMIC LOAD BALANCING ALGORITHMS IN DISTRIBUTED COMPUTING APPLICATIONS Priyesh Kanungo 1 Professor and Senior Systems Engineer (Computer Centre), School of Computer Science and

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

Comparison of PBRR Scheduling Algorithm with Round Robin and Heuristic Priority Scheduling Algorithm in Virtual Cloud Environment

Comparison of PBRR Scheduling Algorithm with Round Robin and Heuristic Priority Scheduling Algorithm in Virtual Cloud Environment www.ijcsi.org 99 Comparison of PBRR Scheduling Algorithm with Round Robin and Heuristic Priority Scheduling Algorithm in Cloud Environment Er. Navreet Singh 1 1 Asst. Professor, Computer Science Department

More information

Performance Modeling and Analysis of a Database Server with Write-Heavy Workload

Performance Modeling and Analysis of a Database Server with Write-Heavy Workload Performance Modeling and Analysis of a Database Server with Write-Heavy Workload Manfred Dellkrantz, Maria Kihl 2, and Anders Robertsson Department of Automatic Control, Lund University 2 Department of

More information

A Game Theory Modal Based On Cloud Computing For Public Cloud

A Game Theory Modal Based On Cloud Computing For Public Cloud IOSR Journal of Computer Engineering (IOSR-JCE) e-issn: 2278-0661, p- ISSN: 2278-8727Volume 16, Issue 2, Ver. XII (Mar-Apr. 2014), PP 48-53 A Game Theory Modal Based On Cloud Computing For Public Cloud

More information

A Survey Of Various Load Balancing Algorithms In Cloud Computing

A Survey Of Various Load Balancing Algorithms In Cloud Computing A Survey Of Various Load Balancing Algorithms In Cloud Computing Dharmesh Kashyap, Jaydeep Viradiya Abstract: Cloud computing is emerging as a new paradigm for manipulating, configuring, and accessing

More information

A Game Theoretic Approach for Cloud Computing Infrastructure to Improve the Performance

A Game Theoretic Approach for Cloud Computing Infrastructure to Improve the Performance P.Bhanuchand and N. Kesava Rao 1 A Game Theoretic Approach for Cloud Computing Infrastructure to Improve the Performance P.Bhanuchand, PG Student [M.Tech, CS], Dep. of CSE, Narayana Engineering College,

More information

The Load Balancing Strategy to Improve the Efficiency in the Public Cloud Environment

The Load Balancing Strategy to Improve the Efficiency in the Public Cloud Environment The Load Balancing Strategy to Improve the Efficiency in the Public Cloud Environment Majjaru Chandra Babu Assistant Professor, Priyadarsini College of Engineering, Nellore. Abstract: Load balancing in

More information

A SURVEY ON LOAD BALANCING ALGORITHMS FOR CLOUD COMPUTING

A SURVEY ON LOAD BALANCING ALGORITHMS FOR CLOUD COMPUTING A SURVEY ON LOAD BALANCING ALGORITHMS FOR CLOUD COMPUTING Avtar Singh #1,Kamlesh Dutta #2, Himanshu Gupta #3 #1 Department of Computer Science and Engineering, Shoolini University, avtarz@gmail.com #2

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

Development of Software Dispatcher Based. for Heterogeneous. Cluster Based Web Systems

Development of Software Dispatcher Based. for Heterogeneous. Cluster Based Web Systems ISSN: 0974-3308, VO L. 5, NO. 2, DECEMBER 2012 @ SRIMC A 105 Development of Software Dispatcher Based B Load Balancing AlgorithmsA for Heterogeneous Cluster Based Web Systems S Prof. Gautam J. Kamani,

More information

Proposal of Dynamic Load Balancing Algorithm in Grid System

Proposal of Dynamic Load Balancing Algorithm in Grid System www.ijcsi.org 186 Proposal of Dynamic Load Balancing Algorithm in Grid System Sherihan Abu Elenin Faculty of Computers and Information Mansoura University, Egypt Abstract This paper proposed dynamic load

More information

LOAD BALANCING AS A STRATEGY LEARNING TASK

LOAD BALANCING AS A STRATEGY LEARNING TASK LOAD BALANCING AS A STRATEGY LEARNING TASK 1 K.KUNGUMARAJ, 2 T.RAVICHANDRAN 1 Research Scholar, Karpagam University, Coimbatore 21. 2 Principal, Hindusthan Institute of Technology, Coimbatore 32. ABSTRACT

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

Big Data Storage Architecture Design in Cloud Computing

Big Data Storage Architecture Design in Cloud Computing Big Data Storage Architecture Design in Cloud Computing Xuebin Chen 1, Shi Wang 1( ), Yanyan Dong 1, and Xu Wang 2 1 College of Science, North China University of Science and Technology, Tangshan, Hebei,

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

A Low Cost Two-Tier Architecture Model For High Availability Clusters Application Load Balancing

A Low Cost Two-Tier Architecture Model For High Availability Clusters Application Load Balancing A Low Cost Two-Tier Architecture Model For High Availability Clusters Application Load Balancing A B M Moniruzzaman, StudentMember, IEEE Department of Computer Science and Engineering Daffodil International

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

Energy Efficient MapReduce

Energy Efficient MapReduce Energy Efficient MapReduce Motivation: Energy consumption is an important aspect of datacenters efficiency, the total power consumption in the united states has doubled from 2000 to 2005, representing

More information

Load Balancing in the Cloud Computing Using Virtual Machine Migration: A Review

Load Balancing in the Cloud Computing Using Virtual Machine Migration: A Review Load Balancing in the Cloud Computing Using Virtual Machine Migration: A Review 1 Rukman Palta, 2 Rubal Jeet 1,2 Indo Global College Of Engineering, Abhipur, Punjab Technical University, jalandhar,india

More information

Purpose-Built Load Balancing The Advantages of Coyote Point Equalizer over Software-based Solutions

Purpose-Built Load Balancing The Advantages of Coyote Point Equalizer over Software-based Solutions Purpose-Built Load Balancing The Advantages of Coyote Point Equalizer over Software-based Solutions Abstract Coyote Point Equalizer appliances deliver traffic management solutions that provide high availability,

More information

Utilizing Round Robin Concept for Load Balancing Algorithm at Virtual Machine Level in Cloud Environment

Utilizing Round Robin Concept for Load Balancing Algorithm at Virtual Machine Level in Cloud Environment Utilizing Round Robin Concept for Load Balancing Algorithm at Virtual Machine Level in Cloud Environment Stuti Dave B H Gardi College of Engineering & Technology Rajkot Gujarat - India Prashant Maheta

More information

Mobile Storage and Search Engine of Information Oriented to Food Cloud

Mobile Storage and Search Engine of Information Oriented to Food Cloud Advance Journal of Food Science and Technology 5(10): 1331-1336, 2013 ISSN: 2042-4868; e-issn: 2042-4876 Maxwell Scientific Organization, 2013 Submitted: May 29, 2013 Accepted: July 04, 2013 Published:

More information

Research on Job Scheduling Algorithm in Hadoop

Research on Job Scheduling Algorithm in Hadoop Journal of Computational Information Systems 7: 6 () 5769-5775 Available at http://www.jofcis.com Research on Job Scheduling Algorithm in Hadoop Yang XIA, Lei WANG, Qiang ZHAO, Gongxuan ZHANG School of

More information

Recommendations for Performance Benchmarking

Recommendations for Performance Benchmarking Recommendations for Performance Benchmarking Shikhar Puri Abstract Performance benchmarking of applications is increasingly becoming essential before deployment. This paper covers recommendations and best

More information

AN EFFICIENT LOAD BALANCING ALGORITHM FOR A DISTRIBUTED COMPUTER SYSTEM. Dr. T.Ravichandran, B.E (ECE), M.E(CSE), Ph.D., MISTE.,

AN EFFICIENT LOAD BALANCING ALGORITHM FOR A DISTRIBUTED COMPUTER SYSTEM. Dr. T.Ravichandran, B.E (ECE), M.E(CSE), Ph.D., MISTE., AN EFFICIENT LOAD BALANCING ALGORITHM FOR A DISTRIBUTED COMPUTER SYSTEM K.Kungumaraj, M.Sc., B.L.I.S., M.Phil., Research Scholar, Principal, Karpagam University, Hindusthan Institute of Technology, Coimbatore

More information

CDBMS Physical Layer issue: Load Balancing

CDBMS Physical Layer issue: Load Balancing CDBMS Physical Layer issue: Load Balancing Shweta Mongia CSE, School of Engineering G D Goenka University, Sohna Shweta.mongia@gdgoenka.ac.in Shipra Kataria CSE, School of Engineering G D Goenka University,

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

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

The Key Technology Research of Virtual Laboratory based On Cloud Computing Ling Zhang

The Key Technology Research of Virtual Laboratory based On Cloud Computing Ling Zhang International Conference on Advances in Mechanical Engineering and Industrial Informatics (AMEII 2015) The Key Technology Research of Virtual Laboratory based On Cloud Computing Ling Zhang Nanjing Communications

More information

Effective Load Balancing Based on Cloud Partitioning for the Public Cloud

Effective Load Balancing Based on Cloud Partitioning for the Public Cloud Effective Load Balancing Based on Cloud Partitioning for the Public Cloud 1 T.Satya Nagamani, 2 D.Suseela Sagar 1,2 Dept. of IT, Sir C R Reddy College of Engineering, Eluru, AP, India Abstract Load balancing

More information

Monitoring Large Flows in Network

Monitoring Large Flows in Network Monitoring Large Flows in Network Jing Li, Chengchen Hu, Bin Liu Department of Computer Science and Technology, Tsinghua University Beijing, P. R. China, 100084 { l-j02, hucc03 }@mails.tsinghua.edu.cn,

More information

Web Server Software Architectures

Web Server Software Architectures Web Server Software Architectures Author: Daniel A. Menascé Presenter: Noshaba Bakht Web Site performance and scalability 1.workload characteristics. 2.security mechanisms. 3. Web cluster architectures.

More information

A Hybrid Load Balancing Policy underlying Cloud Computing Environment

A Hybrid Load Balancing Policy underlying Cloud Computing Environment A Hybrid Load Balancing Policy underlying Cloud Computing Environment S.C. WANG, S.C. TSENG, S.S. WANG*, K.Q. YAN* Chaoyang University of Technology 168, Jifeng E. Rd., Wufeng District, Taichung 41349

More information

Performance Analysis of Session-Level Load Balancing Algorithms

Performance Analysis of Session-Level Load Balancing Algorithms Performance Analysis of Session-Level Load Balancing Algorithms Dennis Roubos, Sandjai Bhulai, and Rob van der Mei Vrije Universiteit Amsterdam Faculty of Sciences De Boelelaan 1081a 1081 HV Amsterdam

More information

Process Scheduling CS 241. February 24, 2012. Copyright University of Illinois CS 241 Staff

Process Scheduling CS 241. February 24, 2012. Copyright University of Illinois CS 241 Staff Process Scheduling CS 241 February 24, 2012 Copyright University of Illinois CS 241 Staff 1 Announcements Mid-semester feedback survey (linked off web page) MP4 due Friday (not Tuesday) Midterm Next Tuesday,

More information

International Journal of Advancements in Research & Technology, Volume 3, Issue 8, August-2014 68 ISSN 2278-7763

International Journal of Advancements in Research & Technology, Volume 3, Issue 8, August-2014 68 ISSN 2278-7763 International Journal of Advancements in Research & Technology, Volume 3, Issue 8, August-2014 68 A Survey of Load Balancing Algorithms using VM B.KalaiSelvi 1 and Dr.L.Mary Immaculate Sheela 2 1 Research

More information

POSIX and Object Distributed Storage Systems

POSIX and Object Distributed Storage Systems 1 POSIX and Object Distributed Storage Systems Performance Comparison Studies With Real-Life Scenarios in an Experimental Data Taking Context Leveraging OpenStack Swift & Ceph by Michael Poat, Dr. Jerome

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

Group Based Load Balancing Algorithm in Cloud Computing Virtualization

Group Based Load Balancing Algorithm in Cloud Computing Virtualization Group Based Load Balancing Algorithm in Cloud Computing Virtualization Rishi Bhardwaj, 2 Sangeeta Mittal, Student, 2 Assistant Professor, Department of Computer Science, Jaypee Institute of Information

More information

Load Balancing Scheduling with Shortest Load First

Load Balancing Scheduling with Shortest Load First , pp. 171-178 http://dx.doi.org/10.14257/ijgdc.2015.8.4.17 Load Balancing Scheduling with Shortest Load First Ranjan Kumar Mondal 1, Enakshmi Nandi 2 and Debabrata Sarddar 3 1 Department of Computer Science

More information

Load balancing as a strategy learning task

Load balancing as a strategy learning task Scholarly Journal of Scientific Research and Essay (SJSRE) Vol. 1(2), pp. 30-34, April 2012 Available online at http:// www.scholarly-journals.com/sjsre ISSN 2315-6163 2012 Scholarly-Journals Review Load

More information

A Data Cleaning Model for Electric Power Big Data Based on Spark Framework 1

A Data Cleaning Model for Electric Power Big Data Based on Spark Framework 1 , pp.405-411 http://dx.doi.org/10.14257/astl.2016. A Data Cleaning Model for Electric Power Big Data Based on Spark Framework 1 Zhao-Yang Qu 1, Yong-Wen Wang 2,2, Chong Wang 3, Nan Qu 4 and Jia Yan 5 1,

More information

Performance Comparison of Assignment Policies on Cluster-based E-Commerce Servers

Performance Comparison of Assignment Policies on Cluster-based E-Commerce Servers Performance Comparison of Assignment Policies on Cluster-based E-Commerce Servers Victoria Ungureanu Department of MSIS Rutgers University, 180 University Ave. Newark, NJ 07102 USA Benjamin Melamed Department

More information

The Three-level Approaches for Differentiated Service in Clustering Web Server

The Three-level Approaches for Differentiated Service in Clustering Web Server The Three-level Approaches for Differentiated Service in Clustering Web Server Myung-Sub Lee and Chang-Hyeon Park School of Computer Science and Electrical Engineering, Yeungnam University Kyungsan, Kyungbuk

More information

Enlarge Bandwidth of Multimedia Server with Network Attached Storage System

Enlarge Bandwidth of Multimedia Server with Network Attached Storage System Enlarge Bandwidth of Multimedia Server with Network Attached Storage System Dan Feng, Yuhui Deng, Ke Zhou, Fang Wang Key Laboratory of Data Storage System, Ministry of Education College of Computer, Huazhong

More information

Adaptable Load Balancing

Adaptable Load Balancing Adaptable Load Balancing Sung Kim, Youngsu Son, Gaeyoung Lee Home Solution Group Samsung Electronics ABSTRACT The proposed load balancing system includes multiple counts of servers for processing network

More information

Load Balancing in Cloud Computing using Observer's Algorithm with Dynamic Weight Table

Load Balancing in Cloud Computing using Observer's Algorithm with Dynamic Weight Table Load Balancing in Cloud Computing using Observer's Algorithm with Dynamic Weight Table Anjali Singh M. Tech Scholar (CSE) SKIT Jaipur, 27.anjali01@gmail.com Mahender Kumar Beniwal Reader (CSE & IT), SKIT

More information

[Laddhad, 4(8): August, 2015] ISSN: 2277-9655 (I2OR), Publication Impact Factor: 3.785

[Laddhad, 4(8): August, 2015] ISSN: 2277-9655 (I2OR), Publication Impact Factor: 3.785 IJESRT INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY CLOUD COMPUTING LOAD BALANCING MODEL WITH HETEROGENEOUS PARTITION Ms. Pranita Narayandas Laddhad *, Prof. Nitin Raut, Prof. Shyam

More information

Cluster Computing. ! Fault tolerance. ! Stateless. ! Throughput. ! Stateful. ! Response time. Architectures. Stateless vs. Stateful.

Cluster Computing. ! Fault tolerance. ! Stateless. ! Throughput. ! Stateful. ! Response time. Architectures. Stateless vs. Stateful. Architectures Cluster Computing Job Parallelism Request Parallelism 2 2010 VMware Inc. All rights reserved Replication Stateless vs. Stateful! Fault tolerance High availability despite failures If one

More information

Journal of Chemical and Pharmaceutical Research, 2015, 7(3):1388-1392. Research Article. E-commerce recommendation system on cloud computing

Journal of Chemical and Pharmaceutical Research, 2015, 7(3):1388-1392. Research Article. E-commerce recommendation system on cloud computing Available online www.jocpr.com Journal of Chemical and Pharmaceutical Research, 2015, 7(3):1388-1392 Research Article ISSN : 0975-7384 CODEN(USA) : JCPRC5 E-commerce recommendation system on cloud computing

More information

This is an author-deposited version published in : http://oatao.univ-toulouse.fr/ Eprints ID : 12518

This is an author-deposited version published in : http://oatao.univ-toulouse.fr/ Eprints ID : 12518 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

Research Article Dynamic Server Cluster Load Balancing in Virtualization Environment with OpenFlow

Research Article Dynamic Server Cluster Load Balancing in Virtualization Environment with OpenFlow International Journal of Distributed Sensor Networks Volume 215, Article ID 531538, 9 pages http://dx.doi.org/1.1155/215/531538 Research Article Dynamic Server Cluster Load Balancing in Virtualization

More information

Application of Predictive Analytics for Better Alignment of Business and IT

Application of Predictive Analytics for Better Alignment of Business and IT Application of Predictive Analytics for Better Alignment of Business and IT Boris Zibitsker, PhD bzibitsker@beznext.com July 25, 2014 Big Data Summit - Riga, Latvia About the Presenter Boris Zibitsker

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

The Design and Implementation of Dynamic Load Balancing for Web-Based GIS Services

The Design and Implementation of Dynamic Load Balancing for Web-Based GIS Services The Design and Implementation of Dynamic Load Balancing for Web-Based GIS Services Myung-Hee Jo* Yun-Won Jo* Jeong-Soo Oh** Si-Young Lee** * Department of Geodetic Engineering, Kyungil University 33 Buho-ri,

More information

Comparative Study of Load Balancing Algorithms

Comparative Study of Load Balancing Algorithms IOSR Journal of Engineering (IOSRJEN) e-issn: 2250-3021, p-issn: 2278-8719 Vol. 3, Issue 3 (Mar. 2013), V2 PP 45-50 Comparative Study of Load Balancing Algorithms Jyoti Vashistha 1, Anant Kumar Jayswal

More information

Binary search tree with SIMD bandwidth optimization using SSE

Binary search tree with SIMD bandwidth optimization using SSE Binary search tree with SIMD bandwidth optimization using SSE Bowen Zhang, Xinwei Li 1.ABSTRACT In-memory tree structured index search is a fundamental database operation. Modern processors provide tremendous

More information

Keywords: Dynamic Load Balancing, Process Migration, Load Indices, Threshold Level, Response Time, Process Age.

Keywords: Dynamic Load Balancing, Process Migration, Load Indices, Threshold Level, Response Time, Process Age. Volume 3, Issue 10, October 2013 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com Load Measurement

More information

Performance Assessment of High Availability Clustered Computing using LVS-NAT

Performance Assessment of High Availability Clustered Computing using LVS-NAT Performance Assessment of High Availability Clustered Computing using LVS-NAT *Muhammad Kashif Shaikh, **Muzammil Ahmad Khan and ***Mumtaz-ul-Imam Abstract High availability cluster computing environment

More information

Business white paper. HP Process Automation. Version 7.0. Server performance

Business white paper. HP Process Automation. Version 7.0. Server performance Business white paper HP Process Automation Version 7.0 Server performance Table of contents 3 Summary of results 4 Benchmark profile 5 Benchmark environmant 6 Performance metrics 6 Process throughput 6

More information