Research on Virtual Machine Resources Dynamic Allocation Method Based on Revenue in Cloud Computing
|
|
- Angelica Fisher
- 7 years ago
- Views:
Transcription
1 Journal of Computational Information Systems 9: 22 (2013) Available at Research on Virtual Machine Resources Dynamic Allocation Method Based on Revenue in Cloud Computing Jun GUO, Junkui WU, Qiang LIU, Yongming YAN, Bin ZHANG College of Information Science and Engineering, Northeastern University, Shenyang , China Abstract In order to improve the utilization of the computing resources, to allocate resource to the virtual machine effectively and dynamically, and at the same time, to maximize the revenue that the cloud resources provider obtained. A dynamic allocation process of the virtual machine in cloud environment based on revenue is presented. This method can improve the utilization of resources that allocated to the client service which running on the virtual machine, meanwhile, meet the terms of the cloud resources provider s biggest benefit. For the resource allocation method, the thesis adopts basis cloud computing platform hadoop to do the experience to verify the method is effective and feasible. Keywords: Cloud Computing; Allocate Resource Dynamically; Maximize the Revenue 1 Introduction Cloud computing is based on the virtualization technology, from [1] we can see, virtualization technology can reduce the number of physic servers in the cloud data center or large scale resource pool, [2, 3] tell us that virtualization technology can allow multiple virtual machines with different operating systems running parallel and independent, so we think this technology can be used to the problem of resource allocation. As we know, there are also some people applied it into the resource allocation. Resource allocation refers to the process of adjusting the resource between different computing tasks in a given environment. The author in [4] proposed the resource allocation in Market-Based compute grids, but the process is not dynamic,and they have not proposed a new method, it just adopts a not mature method, so the conclusion is restrained. [5, 6] talks about the dynamic resource allocation method, and they thought the method is considered as the process of scheduling the resource dynamically when the services run on the virtual machine, and their method is indeed effective in that field. Although the allocation of resources to the virtual Project supported by the Key Technologies R&D Program of Shenyang City (F ), and the Fundamental Research Funds for the Central Universities (No. N ), and the Fundamental Research Funds for the Central Universities (No. N ). Corresponding author. address: guojun@mail.neu.edu.cn (Jun GUO) / Copyright 2013 Binary Information Press DOI: /jcis8520 November 15, 2013
2 9236 J. Guo et al. /Journal of Computational Information Systems 9: 22 (2013) machine have certain research at home and abroad,and the related articles are not so much, like [7, 8], they indeed introduced the method which can allocate resources dynamically in cloud, but they all have not talk about the cloud resource provider s revenue. It means that they don t talk about the resource allocation from the viewpoint of the cloud resource provider s revenue. So it is of great significance to put forward a feasible and effective method which is a dynamic allocation process of the virtual machine in cloud environment based on revenue, this method can improve the utilization of the computing resources, allocates resource to the virtual machine effectively and dynamically, and ensures the quality of user services. So firstly, this paper introduces some related concepts about the method to make the readers understand the method better; secondly, analyses the method in detail; thirdly, determines the equilibrium point and gives the algorithm description, at last, adopts basis cloud computing platform hadoop to do the experience to verify the method is effective and feasible. 2 Related Concepts about the Method The dynamic resource allocation method which is based on revenue has three key concepts: the cloud resource provider s returns (shorthand for R), the cloud resource provider s incomes (shorthand for I) and the cloud resource provider s costs (shorthand for C). The R refers to the I (the hardware resources income which is provided for the user in the virtual machine) minus the cost of the hardware resources. The I refers to the benefit which is gained because of the cloud distributes a certain number of hardware resources for the user s virtual machine in accordance with the budget and the quality of service (QoS). The I is evaluated mainly by the user s budget functions and services average response time. The C refers to the cost brought about by deploying the resources. Based on the definition of R, we can get the formula: R = I C, so we can adjust resource in the virtual machine dynamically according to calculating maximum the R. 3 The Resources Dynamic Allocation Method Based on Revenue The virtual machine resources dynamic allocation process mainly divided into three stages: firstly, get the user service request in a virtual machine; secondly, determine the user s service level [9], lastly, send the cloud resources budget (B i (t) refers to the user i s budget when the time is t), the average response time user expected (T i ), and the demand for the resources to the algorithm. So the resource allocation actuators can calculate the amount of resources allocated to the virtual machine respectively. 3.1 Analyze the method and determine the equilibrium point Cloud computing is a multi-user environment, the resources provided by the cloud resource providers are fairly and sharing to every running virtual machine (V M). It is not directly deal with between the users. But once a V M running the user service gain the resources provided by the cloud resource providers, it will affect the service response time on other users V M. The resource allocation method (take a V M i running on user i for an example) is implemented
3 J. Guo et al. /Journal of Computational Information Systems 9: 22 (2013) by calculating the maximum of R through the formula: R = I C. The process as follows: 1) Get the user service request, mainly refers to the budget the user proposed, the expectations of the average response time and the demand for each kind of resource; 2) Calculate the R s interval; 3) Increase the amount of a certain resource according to the index or linear form, meanwhile, calculate the marginal revenue (shorthand for M R); 4) Determine whether the marginal revenue (MR) and the marginal cost (MC) is equal or not; 5) Repeat 1) 4), until the R is maximize. In order to meet the R is maximum, we must make the MR = MC, it means the R in the equilibrium point attains the maximum. According to the above, we can know the point which MR = MC is the equilibrium point, at this time, the resource allocated to V M is assumed to be P units. When the resource allocated to V M i is more than P, allocating one more unit of the resource to V M i, then MC > MR, it means the R = I C is reduced; When the resource allocated to V M i is less than P, allocating one more unit of the resource to V M i, then MR > MC, it means the R = I C is increased. So when the resource is P, the R = I C is maximum. 3.2 The resources dynamic allocation method based on revenue In this part, I describe the dynamic allocation method among the multiple V M, it means how to control resource allocation proportion, and make each user willing to pay the corresponding amount. This method is fair for every user, in order to show the strategy s fair, there are some specifications as following: [specif ication1] Each kind of resource s unit costs are fixed, defined as K [specification2] If a V M use p units of resources, then its average response time is: Q T (p) = e p 1 p + 1. (1) Q stands for the time which is the user on the V M takes in the unit of resource. The formula (1) indicates: With the increase of resource allocation, the average response time would reach a saturation point, and after that, the influence degree that the increase of resources on the average response time will be small. [specif ication3] Each user s budget function is: stands for each user i s constant, t is the response time. B i (t) = t. (2) Through the [specification1], the cost of each unit of a certain resource is fixed, according to the definition of MC, we can get the value of MC: MC = K (3) So we can also get the formula: MR = K (4) According to the (1) and (2), we can calculate the B i (t): Q B i (t) = B i (T (p)) = B i ( e p 1 p + 1 ) (5)
4 9238 J. Guo et al. /Journal of Computational Information Systems 9: 22 (2013) B i (t) = Q (ep 1 p + 1) (6) According to the condition of R = I C : MR = MC and the above, we can get the follows: dc(p) = dr(p) (7) dp dp So we get the value of p: K = dr d( dp = Q (ep 1 p + 1)) dp = Q (ep 1 1) (8) p = ln( KQ + 1) + 1 (9) So as for n users, each user has its own budget function (2), the allocation for certain types of resources can also be calculated as above. The Table 1 gives the algorithm: the virtual machine resources dynamic allocation based on revenue in cloud. This algorithm can mainly get the certain resources according to the different budget. Table 1: Algorithm: The virtual machine resources dynamic allocation based on revenue in cloud Input: B i (t), MC Output: Allocated resources to each V M Algorithm description: Q 1. T (p) = e p 1 //Get the service response time of user used v units of resource p + 1 Q 2. B i (t) = B i (T (p)) = B i ( e p 1 p + 1 ) 3. B i (t) = Q (ep 1 p + 1) //Calculate the income of user i 4. R = I C = B i (t) C = Q (ep 1 p + 1) C //Calculate the R 5. dc(p) = dr(p) //According to the definition of the equilibrium point dp dp 6. K = dr d( dp = Q (ep 1 p + 1)) = dp Q (ep 1 1) //Deduce the relationship K and p 7. p = ln( KQ + 1) + 1 //Calculate the value of p 8. Return the value of resources allocated to each V M 4 The Experience In this part, we adopt basis cloud computing platform hadoop to do the experience to verify the method is effective and feasible.
5 J. Guo et al. /Journal of Computational Information Systems 9: 22 (2013) The data of experience The data which is needed by this experience mainly are: The average response time of each user in each time interval, the historical demand of the resources of each user. Taking the user 1 as an example to analyze and verify. 1) The response time The Table 2 shows the response time of user 1 Table 2: The average response time for each time interval of user 1 T ime interval The response time (sec 0.1) [0, 5] 6.67 [5, 10] [10, 15] [15, 20] 7.16 [20, 25] [25, 30] 8.69 [30, 35] [35, 40] [40, 45] 5.43 [45, 50] ) The resources demanded in history The Table 3 shows all types of resources demanded by user 1 in history Table 3: All types of resources demanded by user 1 in history Historical demand number Resources in equilibrium point (CP U/M em/bandw/swap/disk) 1 8(time slices)/512(m)/1(m)/64(m)/20(g) 2 2.5(time slices)/128(m)/0.5(m)/128(m)/10(g) 3 4(time slice)/100(m)/0(m)/200(m)/5(g) 4 0.5(time slice)/300(m)/2.5(m)/512(m)/30(g) 5 5(time slice)/256(m)/1(m)/100(m)/25(g) 6 10(time slice)/1024(m)/2(m)/256(m)/8(g) 7 6(time slice)/500(m)/1(m)/0(m)/15(g) 4.2 The experiences for verifying the allocation method In order to verify the allocation method, we should do the following experiences: The experience for verifying all types of resources demanded by V M, the experience for verifying the process of calculating the MR = MC, the experience for verifying the allocation method.
6 9240 J. Guo et al. /Journal of Computational Information Systems 9: 22 (2013) According to all types of resources demanded by user 1 in history, we adopt the grey forecasting method [10], we do the experience for Verifying All Types of Resources Demanded By V M, and get all types of resources demanded by V M. As shown in Table 4. Table 4: All types of resources demanded by V M The sequence All types of resources (CP U/M em/bandw/swap/disk) (time slices)/1972(m)/4.32(m)/534.6(m)/52.64(g) (time slices)1092(m)/2.89(m)/685.2(m)/43.35(g) (time slice)/945.3(m)/2.53(m)/760.6(m)/34.07(g) (time slice)/1385.3(m)/5.75(m)/911.6(m)/61.92(g) (time slice)/1238.7(m)/3.25(m)/609.9(m)/57.28(g) (time slice)/2078(m)/4.67(m)/835.9(m)/38.71(g) (time slice)/1532(m)/3.96(m)/459.2(m)/48(g) Because MR is changing dynamically, so in a time interval, it changes as the allocated resources and the budget function change. Therefor, we do the experience for Verifying the Process of Calculating the MR = MC. Allocating a certain amount of resources, then calculate the MR, until MR = MC. According to the Table 2 and the Table 5, we calculate the changes of CP U. The data is shown in Table 6. Table 5: Parameter settings of the virtual machine resources The types of resources The original value CP U(original/max/unit) 5/20/5$ M emory(original/max/unit) 256/2G/0.15$ Bandwidth(original/max/unit) 1/10/1.2$ Swap(original/max/unit) Disk(original/max/unit) 2G/4G/0.3$ 30G/200G/0.3$ Table 6: He margin revenue of CP U at different time interval Time interval MR [0, 5] [5, 10] [5, 10] [10, 15] [10, 15] [15, 20] [15, 20] [20, 25] [20, 25] [25, 30] [25, 30] [30, 35] [30, 35] [35, 40]
7 J. Guo et al. /Journal of Computational Information Systems 9: 22 (2013) According to the data in Table 6, we can get the Fig. 1. The point (MR = MC) is the maximum revenue point. As is shown in Fig. 1, we get the first equilibrium point in [0, 10], in this process, dynamic allocation method increases the amount of resources in index form firstly, calculating the M R constantly. When the resources increased is more than the required, using the previous increment as the initial increment, converted to a linear form. All the equilibrium points after this interval will be gotten as above. According to the above analysis, we do the Experience for Verifying the Allocation Method, and get the CP U allocation shared, shown as Fig. 2. Fig. 1: Determine the equilibrium point Fig. 2: The CPU assignment According to the process of resources allocation in user 1, we adopt the Algorithm and get all the users (2, 3, 4, 5, 6, 7, 8, 9) process of resources allocation. This experience verifies the method (dynamic allocation process of the virtual machine in cloud environment based on revenue) is effective and feasible. 5 Conclusion In my paper, a dynamic allocation process of the virtual machine in cloud environment based on revenue is presented. Aiming to discuss and analyze the dynamic resource allocation and there source providers revenue in cloud. We adopt the gray prediction method to predict the pre-demand of resources. And then, according to the relationship of the resources allocated to user s V M and the providers revenue, we confirm that the revenue is maximal at equilibrium point. So we can allocate the resource according to the budget and the quality of Service, and at the same time, every V M running on the PM can get reasonable resource. At last, we adopt basis cloud computing platform hadoop to do the experience to verify the method is effective and feasible. Acknowledgement This work was supported by the Key Technologies R&D Program of Shenyang City (F ), and the Fundamental Research Funds for the Central Universities (No. N ), and
8 9242 J. Guo et al. /Journal of Computational Information Systems 9: 22 (2013) the Fundamental Research Funds for the Central Universities (No. N ). References [1] Chen Kang, Zhang Wei-Min, Cloud Computing: System Instances and Current Research [J]. Journal of Software. 2009, Vol. 20 (5), [2] Yanyang ZENG, Fengju KANG, Huizhen YANG, Visualization Algorithm of Realistic Terrain Based on Adaptive Multiple-features Fusion [J]. Journal of Computational Information Systems, 2013 Vol. 9 (12): [3] Da Wang, Cheng Wang. Real-time GPU-based Simulation of Dynamic Terrain in Virtual Battlefield, Journal of Computational Information Systems, vol. 7, no. 6, pp , [4] V. Marbukh and K. Mills, Demand Pricing & Resource Allocation in Market-Based Compute Grids: A Model and Initial Results, in ICN 08: Proceedings of the Seventh International Conference on Networking. Cancun, Mexico: IEEE Computer Society, Apr. 2008, pp [5] Gihun Jung, Kwang Mong Sim, Location-Aware Dynamic Resource Allocation Model for Cloud Computing Environment [C]. Proceedings of 2012 International Conference on Information and Computer Applications (ICICA), vol. 24 (2012). [6] Xiaoyun, Z., W. Zhikui, and S. Singhal, Utility-driven workload management using nested control design [C]. in American Control Conference, : 6. [7] Hu wenxin, Zheng Jun, Zhou Yan, A Computing Capability Allocation Algorithm InDelicate Granularity For Cloud Environment [C]. Proceedings of th IEEE International Conference on Computer Science and Information Technology (ICCSIT 2011) vol [8] Yu Jia, Zong Peng,Dynamic Resource Allocation Scheme Under Traffic Condition In Satellite Systems [J]. Journal of Electronics (CHINA). Vol. 29 (2012). [9] Ludwig, H, A Service Level Agreement Language for Dynamic Electronic Services [J]. Electronic Commerce Research, 2003, 3 (1-2): [10] Wu Guangwei, Luo Huawei, The Application of Several Gray Models in Prediction of Electricity Consumption in Rural Areas [C]. Proceedings of 2011 International Conference on Business Management and Electronic Information (BMEI 2011) vol. 04, 2011.
Task Scheduling in Hadoop
Task Scheduling in Hadoop Sagar Mamdapure Munira Ginwala Neha Papat SAE,Kondhwa SAE,Kondhwa SAE,Kondhwa Abstract Hadoop is widely used for storing large datasets and processing them efficiently under distributed
More informationFigure 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 informationOpen 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 informationLoad 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 informationCONCEPTUAL MODEL OF MULTI-AGENT BUSINESS COLLABORATION BASED ON CLOUD WORKFLOW
CONCEPTUAL MODEL OF MULTI-AGENT BUSINESS COLLABORATION BASED ON CLOUD WORKFLOW 1 XINQIN GAO, 2 MINGSHUN YANG, 3 YONG LIU, 4 XIAOLI HOU School of Mechanical and Precision Instrument Engineering, Xi'an University
More informationResearch and realization of Resource Cloud Encapsulation in Cloud Manufacturing
www.ijcsi.org 579 Research and realization of Resource Cloud Encapsulation in Cloud Manufacturing Zhang Ming 1, Hu Chunyang 2 1 Department of Teaching and Practicing, Guilin University of Electronic Technology
More informationGame Theory Based Iaas Services Composition in Cloud Computing
Game Theory Based Iaas Services Composition in Cloud Computing Environment 1 Yang Yang, *2 Zhenqiang Mi, 3 Jiajia Sun 1, First Author School of Computer and Communication Engineering, University of Science
More informationTasks 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 informationFLAWLESS DISPENSATION PRICING FOR A CLOUD CACHE
NAKKA G BHAVANI, et al, [IJRSAE] TM Volume 2, Issue 7, PP:, SEPTEMBER 2014. FLAWLESS DISPENSATION PRICING FOR A CLOUD CACHE NAKKA GANGA BHAVANI 1*, N.ANJANEYULU 2* 1. II.M.Tech, Dept of CSE, AM Reddy Memorial
More informationInternational 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 informationA 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 informationA 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 informationEnhancing Data Security in Cloud Storage Auditing With Key Abstraction
Enhancing Data Security in Cloud Storage Auditing With Key Abstraction 1 Priyadharshni.A, 2 Geo Jenefer.G 1 Master of engineering in computer science, Ponjesly College of Engineering 2 Assistant Professor,
More informationAN 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 informationDatabase Modeling and Visualization Simulation technology Based on Java3D Hongxia Liu
International Conference on Information Sciences, Machinery, Materials and Energy (ICISMME 05) Database Modeling and Visualization Simulation technology Based on Java3D Hongxia Liu Department of Electronic
More informationA Testing Technique of Microgrid EMS using the Hardware-in-the Loop Simulation (HILS) System
, pp.53-60 http://dx.doi.org/10.14257/ijeic.2014.5.2.04 A Testing Technique of Microgrid EMS using the Hardware-in-the Loop Simulation (HILS) System Ji-Hye Lee, Nam-Dae Kim and Hak-Man Kim Incheon National
More informationAn 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 informationAdvanced Load Balancing Mechanism on Mixed Batch and Transactional Workloads
Advanced Load Balancing Mechanism on Mixed Batch and Transactional Workloads G. Suganthi (Member, IEEE), K. N. Vimal Shankar, Department of Computer Science and Engineering, V.S.B. Engineering College,
More informationBig 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 informationResearch on Digital Agricultural Information Resources Sharing Plan Based on Cloud Computing *
Research on Digital Agricultural Information Resources Sharing Plan Based on Cloud Computing * Guifen Chen 1,**, Xu Wang 2, Hang Chen 1, Chunan Li 1, Guangwei Zeng 1, Yan Wang 1, and Peixun Liu 1 1 College
More informationEfficient Scheduling Of On-line Services in Cloud Computing Based on Task Migration
Efficient Scheduling Of On-line Services in Cloud Computing Based on Task Migration 1 Harish H G, 2 Dr. R Girisha 1 PG Student, 2 Professor, Department of CSE, PESCE Mandya (An Autonomous Institution under
More informationModeling 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 informationEfficient Qos Based Resource Scheduling Using PAPRIKA Method for Cloud Computing
Efficient Qos Based Resource Scheduling Using PAPRIKA Method for Cloud Computing Hilda Lawrance* Post Graduate Scholar Department of Information Technology, Karunya University Coimbatore, Tamilnadu, India
More informationGeneric Reliability Evaluation Method for Industrial Grids with Variable Frequency Drives
Energy and Power Engineering, 2013, 5, 83-88 doi:10.4236/epe.2013.54b016 Published Online July 2013 (http://www.scirp.org/journal/epe) Generic Reliability Evaluation Method for Industrial Grids with Variable
More informationDynamic 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 informationCapability Service Management System for Manufacturing Equipments in
Capability Service Management System for Manufacturing Equipments in Cloud Manufacturing 1 Junwei Yan, 2 Sijin Xin, 3 Quan Liu, 4 Wenjun Xu *1, Corresponding Author School of Information Engineering, Wuhan
More informationThe 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 informationResearch on the UHF RFID Channel Coding Technology based on Simulink
Vol. 6, No. 7, 015 Research on the UHF RFID Channel Coding Technology based on Simulink Changzhi Wang Shanghai 0160, China Zhicai Shi* Shanghai 0160, China Dai Jian Shanghai 0160, China Li Meng Shanghai
More informationOptimization 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 informationReallocation and Allocation of Virtual Machines in Cloud Computing Manan D. Shah a, *, Harshad B. Prajapati b
Proceedings of International Conference on Emerging Research in Computing, Information, Communication and Applications (ERCICA-14) Reallocation and Allocation of Virtual Machines in Cloud Computing Manan
More informationUPS 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 informationResearch on Cloud Computing Network Architecture Based on SDN Technology Weibo Li
4th International Conference on Machinery, Materials and Computing Technology (ICMMCT 2016) Research on Cloud Computing Network Architecture Based on SDN Technology Weibo Li wuhan textile university, Wuhan,
More informationA 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 informationWireless 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 informationResearch on Clustering Analysis of Big Data Yuan Yuanming 1, 2, a, Wu Chanle 1, 2
Advanced Engineering Forum Vols. 6-7 (2012) pp 82-87 Online: 2012-09-26 (2012) Trans Tech Publications, Switzerland doi:10.4028/www.scientific.net/aef.6-7.82 Research on Clustering Analysis of Big Data
More informationA 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 informationResearch 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 informationTelecom 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 informationReal Time Network Server Monitoring using Smartphone with Dynamic Load Balancing
www.ijcsi.org 227 Real Time Network Server Monitoring using Smartphone with Dynamic Load Balancing Dhuha Basheer Abdullah 1, Zeena Abdulgafar Thanoon 2, 1 Computer Science Department, Mosul University,
More informationResearch on Network Attack-Defense Training Based on Virtual Machine
Research on Network Attack-Defense Training Based on Virtual Machine 1 Zhang Hui, 2 Sun Yanwei *1, School of Computer Science and Technology, HuBei University of Education, zhanghuiwuhan@sina.com 2, College
More informationContent Distribution Scheme for Efficient and Interactive Video Streaming Using Cloud
Content Distribution Scheme for Efficient and Interactive Video Streaming Using Cloud Pramod Kumar H N Post-Graduate Student (CSE), P.E.S College of Engineering, Mandya, India Abstract: Now days, more
More informationThe Application and Development of Software Testing in Cloud Computing Environment
2012 International Conference on Computer Science and Service System The Application and Development of Software Testing in Cloud Computing Environment Peng Zhenlong Ou Yang Zhonghui School of Business
More informationAchieve Better Ranking Accuracy Using CloudRank Framework for Cloud Services
Achieve Better Ranking Accuracy Using CloudRank Framework for Cloud Services Ms. M. Subha #1, Mr. K. Saravanan *2 # Student, * Assistant Professor Department of Computer Science and Engineering Regional
More informationIntegration of B2B E-commerce and ERP in Manufacturing Enterprise and. its Application. Cai Ting 1 ; Liu Lei 2
3rd International Conference on Management, Education, Information and Control (MEICI 2015) Integration of B2B E-commerce and ERP in Manufacturing Enterprise and its Application Cai Ting 1 ; Liu Lei 2
More informationOn 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 informationDevelopment of Software As a Service Based GIS Cloud for Academic Institutes. Singh, Pushpraj 1 and Gupta, R. D. 2
Development of Software As a Service Based GIS Cloud for Academic Institutes Singh, Pushpraj 1 and Gupta, R. D. 2 1 Student, M. Tech. (GIS & Remote Sensing); GIS Cell; Motilal Nehru National Institute
More informationA 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 informationMobile 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 informationComparison of Request Admission Based Performance Isolation Approaches in Multi-tenant SaaS Applications
Comparison of Request Admission Based Performance Isolation Approaches in Multi-tenant SaaS Applications Rouven Kreb 1 and Manuel Loesch 2 1 SAP AG, Walldorf, Germany 2 FZI Research Center for Information
More informationDesign of Electric Energy Acquisition System on Hadoop
, pp.47-54 http://dx.doi.org/10.14257/ijgdc.2015.8.5.04 Design of Electric Energy Acquisition System on Hadoop Yi Wu 1 and Jianjun Zhou 2 1 School of Information Science and Technology, Heilongjiang University
More informationThe WAMS Power Data Processing based on Hadoop
Proceedings of 2012 4th International Conference on Machine Learning and Computing IPCSIT vol. 25 (2012) (2012) IACSIT Press, Singapore The WAMS Power Data Processing based on Hadoop Zhaoyang Qu 1, Shilin
More informationData Security Strategy Based on Artificial Immune Algorithm for Cloud Computing
Appl. Math. Inf. Sci. 7, No. 1L, 149-153 (2013) 149 Applied Mathematics & Information Sciences An International Journal Data Security Strategy Based on Artificial Immune Algorithm for Cloud Computing Chen
More informationen fi lm af Zhang Yang instruktøren bag den kinesiske fi lmperle Badeanstalten Premiere 31. oktober
produced by Harvey Wong, Liu Qiang producer Stanley Tong co-producer Michael J. Werner, Wouter Barendrecht producer Er Yong, Zhang Yang screenplay by Zhang Yang, Wang Yao director Zhang Yang Line producer
More informationResearch on Trust Management Strategies in Cloud Computing Environment
Journal of Computational Information Systems 8: 4 (2012) 1757 1763 Available at http://www.jofcis.com Research on Trust Management Strategies in Cloud Computing Environment Wenjuan LI 1,2,, Lingdi PING
More informationHow To Calculate Tunnel Longitudinal Structure
Calculation and Analysis of Tunnel Longitudinal Structure under Effect of Uneven Settlement of Weak Layer 1,2 Li Zhong, 2Chen Si-yang, 3Yan Pei-wu, 1Zhu Yan-peng School of Civil Engineering, Lanzhou University
More informationSetting deadlines and priorities to the tasks to improve energy efficiency in cloud computing
Setting deadlines and priorities to the tasks to improve energy efficiency in cloud computing Problem description Cloud computing is a technology used more and more every day, requiring an important amount
More informationInternational Journal of Computer Sciences and Engineering Open Access. Hybrid Approach to Round Robin and Priority Based Scheduling Algorithm
International Journal of Computer Sciences and Engineering Open Access Review Paper Volume-4, Issue-2 E-ISSN: 2347-2693 Hybrid Approach to Round Robin and Priority Based Scheduling Algorithm Garima Malik
More informationFault 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 informationThe Comprehensive Performance Rating for Hadoop Clusters on Cloud Computing Platform
The Comprehensive Performance Rating for Hadoop Clusters on Cloud Computing Platform Fong-Hao Liu, Ya-Ruei Liou, Hsiang-Fu Lo, Ko-Chin Chang, and Wei-Tsong Lee Abstract Virtualization platform solutions
More informationTowards a Content Delivery Load Balance Algorithm Based on Probability Matching in Cloud Storage
Send Orders for Reprints to reprints@benthamscience.ae The Open Cybernetics & Systemics Journal, 2015, 9, 2211-2217 2211 Open Access Towards a Content Delivery Load Balance Algorithm Based on Probability
More informationDynamic 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 informationA 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 informationU.P.B. Sci. Bull., Series C, Vol. 77, Iss. 1, 2015 ISSN 2286 3540
U.P.B. Sci. Bull., Series C, Vol. 77, Iss. 1, 2015 ISSN 2286 3540 ENTERPRISE FINANCIAL DISTRESS PREDICTION BASED ON BACKWARD PROPAGATION NEURAL NETWORK: AN EMPIRICAL STUDY ON THE CHINESE LISTED EQUIPMENT
More informationA Virtual Machine Consolidation Framework for MapReduce Enabled Computing Clouds
A Virtual Machine Consolidation Framework for MapReduce Enabled Computing Clouds Zhe Huang, Danny H.K. Tsang, James She Department of Electronic & Computer Engineering The Hong Kong University of Science
More informationTopology Aware Analytics for Elastic Cloud Services
Topology Aware Analytics for Elastic Cloud Services athafoud@cs.ucy.ac.cy Master Thesis Presentation May 28 th 2015, Department of Computer Science, University of Cyprus In Brief.. a Tool providing Performance
More informationResearch on Task Planning Based on Activity Period in Manufacturing Grid
Research on Task Planning Based on Activity Period in Manufacturing Grid He Yu an, Yu Tao, Hu Da chao Abstract In manufacturing grid (MG), activities of the manufacturing task need to be planed after the
More informationThe 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 informationDevelopment of Bio-Cloud Service for Genomic Analysis Based on Virtual
Development of Bio-Cloud Service for Genomic Analysis Based on Virtual Infrastructure 1 Jung-Ho Um, 2 Sang Bae Park, 3 Hoon Choi, 4 Hanmin Jung 1, First Author Korea Institute of Science and Technology
More informationA 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 informationCopula model estimation and test of inventory portfolio pledge rate
International Journal of Business and Economics Research 2014; 3(4): 150-154 Published online August 10, 2014 (http://www.sciencepublishinggroup.com/j/ijber) doi: 10.11648/j.ijber.20140304.12 ISS: 2328-7543
More informationInternational 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 informationResearch 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 informationParallel Data Selection Based on Neurodynamic Optimization in the Era of Big Data
Parallel Data Selection Based on Neurodynamic Optimization in the Era of Big Data Jun Wang Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Shatin, New Territories,
More informationStudy on Architecture and Implementation of Port Logistics Information Service Platform Based on Cloud Computing 1
, pp. 331-342 http://dx.doi.org/10.14257/ijfgcn.2015.8.2.27 Study on Architecture and Implementation of Port Logistics Information Service Platform Based on Cloud Computing 1 Changming Li, Jie Shen and
More informationThe Compound Operations of Uncertain Cloud Concepts
Journal of Computational Information Systems 11: 13 (2015) 4881 4888 Available at http://www.jofcis.com The Compound Operations of Uncertain Cloud Concepts Longjun YIN 1,, Junjie XU 1, Guansheng ZHANG
More informationAdaptive Scheduling for QoS-based Virtual Machine Management in Cloud Computing
Yang Cao, Cheul Woo Ro : Adaptive Scheduling for QoS-based Virtual Machine Management in Cloud Computing 7 http://dx.doi.org/10.5392/ijoc.2012.8.7 Adaptive Scheduling for QoS-based Virtual Machine Management
More informationMap-Parallel Scheduling (mps) using Hadoop environment for job scheduler and time span for Multicore Processors
Map-Parallel Scheduling (mps) using Hadoop environment for job scheduler and time span for Sudarsanam P Abstract G. Singaravel Parallel computing is an base mechanism for data process with scheduling task,
More informationThe Design and Application of Water Jet Propulsion Boat Weibo Song, Junhai Jiang3, a, Shuping Zhao, Kaiyan Zhu, Qihua Wang
International Conference on Automation, Mechanical Control and Computational Engineering (AMCCE 2015) The Design and Application of Water Jet Propulsion Boat Weibo Song, Junhai Jiang3, a, Shuping Zhao,
More informationA 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 informationA Prediction-Based Transcoding System for Video Conference in Cloud Computing
A Prediction-Based Transcoding System for Video Conference in Cloud Computing Yongquan Chen 1 Abstract. We design a transcoding system that can provide dynamic transcoding services for various types of
More informationA New Method for Traffic Forecasting Based on the Data Mining Technology with Artificial Intelligent Algorithms
Research Journal of Applied Sciences, Engineering and Technology 5(12): 3417-3422, 213 ISSN: 24-7459; e-issn: 24-7467 Maxwell Scientific Organization, 213 Submitted: October 17, 212 Accepted: November
More informationAutomatic Mining of Internet Translation Reference Knowledge Based on Multiple Search Engines
, 22-24 October, 2014, San Francisco, USA Automatic Mining of Internet Translation Reference Knowledge Based on Multiple Search Engines Baosheng Yin, Wei Wang, Ruixue Lu, Yang Yang Abstract With the increasing
More informationInternational Journal of Advance Research in Computer Science and Management Studies
Volume 3, Issue 6, June 2015 ISSN: 2321 7782 (Online) International Journal of Advance Research in Computer Science and Management Studies Research Article / Survey Paper / Case Study Available online
More informationResearch of PROFIBUS PA s integration in PROFINET IO
3rd International Conference on Material, Mechanical and Manufacturing Engineering (IC3ME 2015) Research of PROFIBUS PA s integration in PROFINET IO Zhijia Yang 1, a *, Zhongsheng Li 1,2,b, Feng Qiao 2
More informationA Health Degree Evaluation Algorithm for Equipment Based on Fuzzy Sets and the Improved SVM
Journal of Computational Information Systems 10: 17 (2014) 7629 7635 Available at http://www.jofcis.com A Health Degree Evaluation Algorithm for Equipment Based on Fuzzy Sets and the Improved SVM Tian
More informationThe 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 informationImproving Performance and Reliability Using New Load Balancing Strategy with Large Public Cloud
Improving Performance and Reliability Using New Load Balancing Strategy with Large Public Cloud P.Gayathri Atchuta*1, L.Prasanna Kumar*2, Amarendra Kothalanka*3 M.Tech Student*1, Associate Professor*2,
More informationKnowledge Acquisition Approach Based on Rough Set in Online Aided Decision System for Food Processing Quality and Safety
, pp. 381-388 http://dx.doi.org/10.14257/ijunesst.2014.7.6.33 Knowledge Acquisition Approach Based on Rough Set in Online Aided ecision System for Food Processing Quality and Safety Liu Peng, Liu Wen,
More informationEFFICIENT JOB SCHEDULING OF VIRTUAL MACHINES IN CLOUD COMPUTING
EFFICIENT JOB SCHEDULING OF VIRTUAL MACHINES IN CLOUD COMPUTING Ranjana Saini 1, Indu 2 M.Tech Scholar, JCDM College of Engineering, CSE Department,Sirsa 1 Assistant Prof., CSE Department, JCDM College
More informationImplementation of High Step-Up Solar Power Optimizer for DC Micro Grid Application
Implementation of High tepup olar Power Optimizer for C Micro Grid Application hihming Chen, KeRen Hu, TsorngJuu Liang, and YiHsun Hsieh Advanced Optoelectronic Technology Center epartment of Electrical
More informationDemonstration of Runtime Model Based Management of Diverse Cloud Resources
Demonstration of Runtime Model Based Management of Diverse Cloud Resources Xiaodong Zhang,2, Xing Chen,2, Ying Zhang *,2, Yihan Wu,2, Gang Huang,2, Qiang Lin 3 Key Laboratory of High Confidence Software
More informationDynamic Virtual Machine Scheduling for Resource Sharing In the Cloud Environment
Dynamic Virtual Machine Scheduling for Resource Sharing In the Cloud Environment Karthika.M 1 P.G Student, Department of Computer Science and Engineering, V.S.B Engineering College, Tamil Nadu 1 ABSTRACT:
More informationPresentation of Multi Level Data Replication Distributed Decision Making Strategy for High Priority Tasks in Real Time Data Grids
Presentation of Multi Level Data Replication Distributed Decision Making Strategy for High Priority Tasks in Real Time Data Grids Naghmeh Esmaieli Esmaily.naghmeh@gmail.com Mahdi Jafari Ser_jafari@yahoo.com
More informationSimulating Optimum Design of Handling Service Center System Based on WITNESS
Advances in Natural Science Vol. 6, No. 4, 2013, pp. 64-68 DOI:10.3968/j.ans.1715787020130604.2958 ISSN 1715-7862 [PRINT] ISSN 1715-7870 [ONLINE] www.cscanada.net www.cscanada.org Simulating Optimum Design
More informationA Proxy-Based Data Security Solution in Mobile Cloud
, pp. 77-84 http://dx.doi.org/10.14257/ijsia.2015.9.5.08 A Proxy-Based Data Security Solution in Mobile Cloud Xiaojun Yu 1,2 and Qiaoyan Wen 1 1 State Key Laboratory of Networking and Switching Technology,
More informationEnhancing On-Line Conferencing Ba with Human-Machine Interaction CorMap Analysis
62 International Journal of Knowledge and Systems Science, 1(2), 62-70, April-June 2010 Enhancing On-Line Conferencing Ba with Human-Machine Interaction CorMap Analysis Bin Luo, Chinese Academy of Sciences,
More informationDDSS: Dynamic Dedicated Servers Scheduling for Multi Priority Level Classes in Cloud Computing
DDSS: Dynamic Dedicated Servers Scheduling for Multi Priority Level Classes in Cloud Computing Husnu S. Narman husnu@ou.edu Md. Shohrab Hossain mshohrabhossain@cse.buet.ac.bd Mohammed Atiquzzaman atiq@ou.edu
More informationModeling of Knowledge Transfer in logistics Supply Chain Based on System Dynamics
, pp.377-388 http://dx.doi.org/10.14257/ijunesst.2015.8.12.38 Modeling of Knowledge Transfer in logistics Supply Chain Based on System Dynamics Yang Bo School of Information Management Jiangxi University
More informationDynamic 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 informationA Novel Switch Mechanism for Load Balancing in Public Cloud
International OPEN ACCESS Journal Of Modern Engineering Research (IJMER) A Novel Switch Mechanism for Load Balancing in Public Cloud Kalathoti Rambabu 1, M. Chandra Sekhar 2 1 M. Tech (CSE), MVR College
More information