An Efficient Cloud Service Broker Algorithm

Size: px
Start display at page:

Download "An Efficient Cloud Service Broker Algorithm"

Transcription

1 An Efficient Cloud Service Broker Algorithm 1 Gamal I. Selim, 2 Rowayda A. Sadek, 3 Hend Taha 1 College of Engineering and Technology, AAST, 2 Faculty of Computers and Information, Helwan University, 3 College of Engineering and Technology, AAST, Abstract Cloud Computing introduces a new efficient way in providing and delivering the different services to the users who mainly care about two parameters; response time and cost. Since there are many Datacenters hosting the provided applications, the role of a service broker becomes very important in choosing the most suitable data center to serve the received request. Many policies introduced in Cloud Simulation s Service brokerage to choose the appropriate Datacenter based on proximity region, response time, or cost. However, considering one parameter comes at the expense of the other parameters. This paper proposes two algorithms to consider region, response time and cost parameters in order to choose best data center to lower the cost and response time. Two testing scenarios are considered to compare the proposed algorithms with three existing algorithms. The results show enhancements up to 30% in total cost, up to 10% in DC processing time and up to 8% in response time. 1. Introduction Keyword: Cloud Computing; CloudAnalyst; Service Broker. In the era of cloud computing, clients will pay only for the services they need whether it is hardware renting or using an application [1]. Many service providers now upgrade to Cloud Computing technology to lower their foundation and installation costs in addition to providing a fast and guaranteed services to their customers with the lowest price. Cloud services must lower the response time and the overall cost for any user request. Many algorithms were proposed to lower the Datacenter s response time. However to achieve that; tests need to be repeatedly applied to different proposed algorithms and methodologies which highly consumed costs and time in the real cloud environment. Cloud Simulation comes in hand in these situations, as it allows to test cloud computing different algorithms in many areas, control the environment, and totally free of charge[9,10]. Finally, the tested cloud will be ready to deploy in the real life. CloudAnalyst simulation which is based on Cloudsim toolkit is commonly used in cloud simulation [2]. This paper focuses on one main component in cloud architecture; called service broker. Service broker keeps a list of the available data centers with some information about each data center (like region, costs.. etc.) and helps in choosing the suitable data center to serve any received request. Choosing the right data center helps in saving response time and money. Many algorithms discuss how to choose the appropriate datacenter to serve a certain user request based on different parameters like the closest in region, the best response, or the cost[3]-[5]. Algorithms that consider only one of these parameters affect the others. So, an algorithm that considers most of these parameters is needed to select the most effective datacenter. This paper proposes two algorithms to select the data center with the best possible response and yet the lowest in Virtual Machine cost (VmCost) and Data transfer cost to serve the coming user request. The first algorithm chooses the best response time data center, and then finds the data center with the lowest cost in its region to serve the user request. The second algorithm chooses the best data center based on considering many of the data center parameters all together as it considers the total cost (VmCost and Data transfer cost) as well as network delay cost. Two scenarios were conducted to introduce heavy loads (during rush hours or in case of emergencies when a data center drops down) and light loads. These scenarios tested the proposed paper algorithms along with three former algorithms. The proposed algorithms show an enhancement in total cost, response time, and processing time comparing to other algorithms results. International Journal of Advancements in Computing Technology(IJACT) Volume 6, Number 1, January

2 The rest of this paper is as follows, section II discusses the CloudAnalyst main components, section III discusses the current CloudAnalyst algorithms along with some proposed algorithms, section IV discusses the new proposed algorithm, section V investigates scenarios and their results, and section VI reveals the conclusion and the future work. 2. CloudAnalyst CloudAnalyst can model a real world cloud solutions to help cloud providers in testing their proposed environment before going live. CloudAnalyst is built directly over CloudSim toolkit [7]; it extends and adds many features to CloudSim like Internet component, CloudAppServiceBroker Component, GUI interface, etc. A brief discussion of these components and other is coming next to help in understanding later algorithms and scenarios of creating and handling the user requests. As shown in figure 1 the UserBase demonstrates a certain number of users creates and sends their requests, in the form of InternetCloudlet to the Internet component. The Internet component models the internet network around the globe in six regions by introducing transmission latency and data transfer delays. The internet component generates requests out of the InternetCloudlet, then it asks service broker used algorithms (CloudAppServiceBroker), for the selected data center ID. When Internet receives the ID of the chosen Datacenter from Service Broker, it sends the InternetCloudlet (Request) along with the serving data center ID (Chosen Datacenter) to DataCenterController. DataCenterController handles all the activities of one data center, including creating and destroying Vms, and also routes the user requests to the appropriate Vm according to the applied load balancer algorithm. The DataCenterController asks the used VmLoadBalancer algorithms for the appropriate Vm ID to serve the user request. DataCenterController submits the Cloudlet with the chosen data center ID and Vm ID to the simulation environment. Users (UserBase) Generates InternetCloudlet Receives Requests Response Internet Exchange Requests & Response Service Broker - Equal Distributer Service Broker - Cost Performance Service Broker Get Destination Response with Chosen DC/Vm (VmLoadBalancer) Get Vm DataCenter (DataCenterController) 3. Service Broker Algorithms Response with chosen Vm Figure 1. User Request Process Service Broker selects the appropriate Datacenter to serve the coming requests from UserBases. Thus, service broker effects on response time and cost of every Datacenter. Currently, there are only three implemented service broker algorithms in CloudAnalyst simulation [2] Service Proximity based routing Service Proximity selects the closest data center based on the network latency. The network latency comes from the predefined delay metric between different regions. In case there are two closed data centers in region, the algorithm selects a data center randomly [3]. The problem with this algorithm is being not considered the data center cost and it can overload a data center because it is the nearest one. 38

3 3.2. Performance Optimized routing When this service broker receives a new request it chooses the best response data center and send the request to it, the data center with the lowest total delay is considered the best response data center. The total delay is calculated using the following formula: T total =T latency + T transfer (1) Where T latency : is the network delay and it is calculated from the latency metrix. T transfer : is the time taken to transfer the size of data of a single request (D) from source location to the destination. T transfer = D / BW per user (2) BW peruser =BW total / Nr (3) And Nr is the number of user requests currently in transmission [2]. BW total is the total available bandwidth. This algorithm performs very well and gets the best performance if and only if all the data centers costs the same, which is not the case in the real world so algorithms that consider the cost were mostly needed [3] Dynamically reconfiguring router Scaling up and down the cloud environment helps in handling the variant workload user s requests [8]. Based on that a dynamic service broker policy is introduced as it extends any other service broker policy adding to them two options increasing and decreasing the number of the running Vms. In other words, this algorithm monitors the response time of the entire data center and if it exceeds a certain threshold a number of Vms created. Alternatively, reduce the number of Vms if the response time decreased by a certain threshold [2]. The current results are not promising and therefore this algorithm introduced so far by concept only VmCost based Service Broker Policy A new algorithm was proposed in [5] that consider the Vm cost in every datacenter, so the closest datacenter with the less cost will be chosen. The results from this algorithm were compared with the Service Proximity based routing algorithm in Cloud-Analyst. This algorithm gets lower cost but in the expense of response time Cost Effective Selection Cost Effective selection algorithm proposed an enhanced proximity-based routing policy that avoids the direct selection of nearest data center. If more than one data center is located in a region, the data center having less cost (considering only Data transfer cost) will be selected [4] Modified Cost Effective Selection This algorithm chooses the most two cost effective data centers, to have better performance [3]. There are no more information about algorithm implementation and no explanations how the algorithm sends the same user request to two data centers [3]. 4. A hybrid Cost Performance Service Broker algorithms Unlike the mentioned algorithms, the proposed algorithms will consider two parameters (Cost & Network /delay) instead of only one parameter in the developed algorithms. Combining these two parameters is carried out in a way to give a lower cost with acceptable response. Two algorithms 39

4 proposed; one divides the load equally on the best performance data center and lowest cost one. The second considers both cost and performance parameters when choosing the datacenter (DC) Equal Distributer Service Broker Equal Distributer algorithm equally distributes the load between two data centers, the best performance and the lowest cost data center. Once the service broker receives a request, it searches for the best performance DC (BRDC) to serve that request, then uses the Vm cost parameter, it search for the DC with the lowest cost (LCDC) with one condition to be in BRDC region as shown in step (4) in the algorithm. In other words, obtain the best performance DC (BRDC) region and then find the DC with the lowest cost within the BRDC region. Figure 2 shows the algorithm flowchart. Next, the algorithm step by step: Algorithm 1: Equal Distributer Service Broker. 1: When Service Broker receives a new user request 2: Using equation (1) get Best Response Data center (BRDC) 3: SET dcregion = BRDC region 4: SET LCDC = Lowest Cost Data Center in dcregion 5: IF LCDC = = NULL 6: SET chosendc = BRDC 7: ELSE 8: SET chosendc = LCDC 9: END IF 10: RETURN chosendc; 4.2. Cost Performance Service Broker This algorithm modifies the former algorithm, and merges between the two parameters so the selection of the appropriate data center will be on both the total cost and the total latency. Cost parameter here differs from the former algorithm as it considers both the Vm cost and data transfer cost using equation (4), the first part of the equation calculates the time taken by VM to process the total number of requests instructions assigned to it (MIPS total / VM MIPS ), multiply it by VmCost to get the total cost. The second part, calculates the data transfer cost by multiplying the request s data size by data transfer cost. C Total = (MIPS total / VM MIPS ) * VmCost + Cl Datasize * DTCost (4) Where, C Total : Total cost, contains total data processing cost and total data transfer cost. MIPS total :Total number of instructions (MIPS) assigned to the data center. VM MIPS : Average assigned processing power to any Vm in the data center. Using average processing power helps in both cases if the Vms assigned a fixed resources or dynamic configuration. VmCost: is the cost of using the Vm per hr. Cl Datasize :User request data size. DTCost: Data transfer Cost. Performance parameter will be based network delay from equation (1). However, delay is measured in time unit and cost in money, so it is assumed that the delay will cost fixed rate for example 0.1 dollar per second. Figure 3 shows the algorithm flowchart. The algorithm is illustrated step by step as follows; Algorithm 2: Cost Performance Service Broker, Figure 3 1: When Service Broker receives a new user request. 2: FOR all DataCenters 3: SET totalmips += Requests MIPS 4: Using equation (1) to calculate the network delay and multiply it by 0.1 the predefined 40

5 value to get cost per sec. 5: Using equation (4) to calculate total cost of the current dc. 6: IF totalcost < mincost 7: SET mincost = totalcost 8: SET chosendc = currentdc 9: END IF 10: END FOR 11: RETURN chosendc; Setting the mincost to the max value available will make sure that the first chosendc will never be null and will take at the first round the value of first data center in the list. NewRequest NewRequest Get BRDC Set Mincost = integer.maxvalue Set dcregion = BRDC sregion Loop on Datacenter s list Get LCDC in dcregion Is it the last DC in list? False If LCDC is not Null False Set ChosenDc = BRDC Calculate TotalCost in Equ (4) True Set ChosenDc =LCDC False If totalcost<m incost True mincost = totalcost ChosenDc = CurrentDC ReturnChosenDC ReturnChosen DC Figure 2: Equal Distributer Service Broker Figure 3: Cost Performance Service Broker 41

6 Both algorithms considers the two parameters cost, and performance. The first one equally distributes the load between the best performance and lowest cost DC. The second put two parameters in one equation and get the best performance with the lowest cost. The reason for using two algorithms is to clarify that even when considering the same parameters, different scenarios need different ways of choosing the best datacenter, to get the best results possible. 5. Results and Discussion Two tests will be carried out to demonstrate two different scenarios in real world, light scenario and Heavy one. The simulated light scenario has two data centers serve five UserBases. The simulation of the heavy one has three datacenters serves twelve userbases. This second scenario is very heavy and choosing the wrong DC may cost a lot Scenario 1 Two DataCenters and Five UserBases The first scenario demonstrates two datacenters serve five UserBases (UB). It is designed as to be a sample of light load for testing Scenario 1 Configurations UserBases (UB) have different loads (Instruction per request), different peak hours and different data sizes per request. UBs are all in the same region as shown in Table 1. The data centers serving them will be in different regions with different VmCost and data transfer cost as shown in Table 2. Simulation duration is 1 day. Name Table 1. Scenario 1 UserBases Configuration Data size/hr Peak hrs MIPS per req. (Bytes) UB UB UB UB UB Table 2. Scenario 1 Datacenters Configuration Name Region Vm Cost Data Transfer Cost DC DC All the UserBases are located in region 2, simulate 1000 users in peak hours and 100 in non-peak hours, each user with 60 request per hour. Each Data center will host 5 VMs with memory 512 MB, BW 1000 Mbits/s, and storage MB Experiments Using Scenario 1 The results compare the two proposed algorithms with three other algorithms as shown in table 3, as it compare the overall cost, Data centers max processing time, and max overall response time. Figure 4, demonstrates the results in table 3. Three algorithms performed almost the same Service Proximity Based, Performance Optimized routing, and Cost-Performance service broker. Because the first algorithm, Service Proximity Based, chooses only the first data center DC1 and sends the entire requests to it, Performance Optimized routing, and Cost-Performance service broker send only 1 or 2% of the load to DC2 leaving DC1 very loaded. However in Vmcost based algorithm it chooses DC2 to serve all the users requests to achieve lowest price but with highest processing and best response time. 42

7 Table 3. Performance with light load; Scenario 1 Overall Cost ($) D.C Processing Time (Max in ms) Service Proximity Based Performance Optimized routing VmCost based Service Equal Distributer Service Broker Cost Performance Service Broker Overall response time (Max in ms) Equal Distributer Service Broker stands in the middle between cost and response/processing time, it lowered the overall cost by 30% than the other three algorithms. It lowers the processing time by 65% and response time by 8% than Vm Cost Based algorithm. Cost Performance Service Broker didd not work well in this scenario as with light loads less calculations algorithms will act fast and better than complex one Service Proximity Based Performancee Optimized routing VmCost based Service Equal Distributer Servicee Broker Cost Performance Service Broker Overall Cost ($) DC Proc. Time (ms) Resp. Time (ms) Figure 4. Performance of the five service broker algorithms. Figures 5-7 show the Datacenters hourly usage for the five algorithms. Almost all the algorithms act the same except Equal Distributer Service Broker it distributes the load on the two datacenters as in Figure 5 and by this way lowering the cost and performance. Figure 7, shows the Datacenters hourly usage for three algorithms, Service Proximity Based, Performance Optimized routing, and Costor the Performance Service Broker, as they load only one DC (as DC1), as it is either the closest DC best performance DC. In Figure 6 VmCost algorithms used DC2 with the lowest cost. 600 Usage Per Hr DC 1 DC hrs Figure 5. DataCenters Hourly Usage for Equal Distributer Service Broker. 43

8 Usage Per Hr DC hrs DC 2 Figure 6. DataCenters Hourly Usage for Vm Cost Based Service Algorithm Usage Per Hr DC hrs DC 1 Figure 7. DataCenters Hourly Usage for Three algorithms Putting the two Data Centers DC1 and DC2 in the same region (Scenario 1 \ ) willl even enhance the performance of the proposed algorithms as shown in table 4. Both algorithms lowered their total costs and response time, high processing time willl not quite affect the end user s requests however it serve in lowering the response time. Table 4. A Performance comparison between Scenario 1 & Scenario Overall Cost D.C Processing Overall response ($) Time (Max in ms) time (Max in ms) Equal Distributer Service Broker Equal Distributer Service Broker Same Region Cost Performance Service Broker Cost Performance Service Broker Same Region Scenario 2 Three DataCenters and Twelve UserBase The second scenario has heavier weight. It has three data centers (DCs) to servee twelve userbases (UBs). It is designed as to be a sample of heavy load for testing 1 \ 44

9 Scenario 2 Configurations UBs configurations are illustrated in table 5. DCs locate in region 0 and 3. DCs configurations are shown in table 6. Table 5. Scenario 2 Userbases Configurations Name Region Data size/hr Peak hrs MIPS per req. (Bytes) UB UB UB UB UB UB UB UB UB UB UB UB Experiments Using Scenario 2 Table 6. Scenario 2 Data centers Configuration Name Region Vm Cost Data Transfer Cost DC DC DC Although, the results in table 7 clarify the targeted enhancement the proposed algorithms, the Vm Cost algorithm still gives a very high response/processing time. Service Proximity ignores the cost factor; the performance optimizer comes in a second after Service Proximity. Cost-Performance and Equal Distributer proposed algorithms give the best possible response/processing time with low cost, lowering percentage is 6% in total cost, and 10% in response/processing time. The tested scenarios show that when comparing the performance of the two algorithms with three former algorithms, the paper algorithms give the best possible response/processing time with low total cost as targeted. Table 7. Performance with heavy load; Scenario 2 Overall Cost ($) D.C Processing Time (Max in ms) Overall response time (Max in ms) Service Proximity Based Performance Optimized routing VmCost based Service Equal Distributer Service Broker Cost Performance Service Broker Conclusions and future work Both algorithms consider the two parameters cost, and performance. The first one equally distribute the load between the best performance and lowest cost DC. The second put two parameters in one equation and get the best performance with the lowest cost. The Hybrid Cost - Performance Service Broker proposed algorithms are proposed to select the destination datacenter by considering two important parameters, the response time and total Cost. Examining the proposed algorithms versus the 45

10 existed algorithms with two different scenarios proved the best response possible with the lowest cost of the proposed ones. Results show enhancements with a percentage up to 30% in total cost, 8% in response time, and 10% in processing time comparing to other already developed algorithms. In future two further steps can be done; first add possibility of introducing different types of user requests and handle them dynamically by proposing a dynamic configuration algorithm that increase or decrease the resources allocated to each Vm running in the data centers to handle those different types. Second, propose a new optimized load balancer to lower the response time for any datacenters. 7. References [1] Wei Zhao, Yong Peng, Feng Xie, Zhonghua Dai, Modeling and Simulation of Cloud Computing: A Review, IEEE Asia Pacific Cloud Computing Congress (APCloudCC2012), pp , [2] B. Wickremasinghe, CloudAnalyst: A CloudSim-based Toolfor Modelling and Analysis of LargeScale Cloud Computing Environments, MEDC Project Report University of Melbourne, [3] D. Limbani, B. Oza, A Proposed Service Broker Policy for Data Center Selection in Cloud Environment with Implementation, International Journal of Computer Technology and Applications, IJCTA, Vol 3 Issue 3, [4] D. Chudasama, N. Trivedi, R. Sinha, Cost effective selection of Data center by Proximity-Based Routing Policy for Service Brokering in Cloud Environment, International Journal of Computer Technology and Applications, IJCTA, Vol 3 Issue 6, [5] J. Park, D. Lee, B. Kim, J. Huh, S. Maeng, Locality-Aware Dynamic VM Reconfiguration on Map Reduce Clouds, 21st international symposium on High-Performance Parallel and Distributed Computing,HPDC 12, pp , [6] Cloud Analyst, link at: [7] R. Buyya, R. Ranjan, and R. N. Calheiros, Modeling and Simulation of Scalable Cloud Computing Environments and the CloudSim Toolkit: Challenges and Opportunities, Processing of the 7thHigh Performance Computing and Simulation Conference (HPCS09), IEEE Computer Society, pp. 1 11, [8] RajkumarBuyya, Rajiv Ranjan, Rodrigo N. Calheiros, InterCloud: utility-oriented federation of cloud computing environments for scaling of application services, 10th international conference on Algorithms and Architectures for Parallel Processing, pp , [9] G. Sakellari, G., Loukas, A survey of mathematical models, simulation approaches and testbeds used for research in cloud computing, Simul. Modell. Pract. Theory 2013, in press. [10] M. Aggarwal, Introduction of Cloud Computing and Survey of Simulation Software for Cloud, Research Journal of Science & IT Management, RJSITM, Vol 3, No 01,

A Proposed Service Broker Strategy in CloudAnalyst for Cost-Effective Data Center Selection

A Proposed Service Broker Strategy in CloudAnalyst for Cost-Effective Data Center Selection A Proposed Service Broker Strategy in CloudAnalyst for Cost-Effective Selection Dhaval Limbani*, Bhavesh Oza** *(Department of Information Technology, S. S. Engineering College, Bhavnagar) ** (Department

More information

A Proposed Service Broker Policy for Data Center Selection in Cloud Environment with Implementation

A Proposed Service Broker Policy for Data Center Selection in Cloud Environment with Implementation A Service Broker Policy for Data Center Selection in Cloud Environment with Implementation Dhaval Limbani*, Bhavesh Oza** *(Department of Information Technology, S. S. Engineering College, Bhavnagar) **

More information

Service Broker Algorithm for Cloud-Analyst

Service Broker Algorithm for Cloud-Analyst Service Broker Algorithm for Cloud-Analyst Rakesh Kumar Mishra, Sreenu Naik Bhukya Department of Computer Science & Engineering National Institute of Technology Calicut, India Abstract Cloud computing

More information

International Journal of Computer Science Trends and Technology (IJCST) Volume 2 Issue 4, July-Aug 2014

International Journal of Computer Science Trends and Technology (IJCST) Volume 2 Issue 4, July-Aug 2014 RESEARCH ARTICLE An Efficient Service Broker Policy for Cloud Computing Environment Kunal Kishor 1, Vivek Thapar 2 Research Scholar 1, Assistant Professor 2 Department of Computer Science and Engineering,

More information

Efficient Service Broker Policy For Large-Scale Cloud Environments

Efficient Service Broker Policy For Large-Scale Cloud Environments www.ijcsi.org 85 Efficient Service Broker Policy For Large-Scale Cloud Environments Mohammed Radi Computer Science Department, Faculty of Applied Science Alaqsa University, Gaza Palestine Abstract Algorithms,

More information

LOAD BALANCING OF USER PROCESSES AMONG VIRTUAL MACHINES IN CLOUD COMPUTING ENVIRONMENT

LOAD BALANCING OF USER PROCESSES AMONG VIRTUAL MACHINES IN CLOUD COMPUTING ENVIRONMENT LOAD BALANCING OF USER PROCESSES AMONG VIRTUAL MACHINES IN CLOUD COMPUTING ENVIRONMENT 1 Neha Singla Sant Longowal Institute of Engineering and Technology, Longowal, Punjab, India Email: 1 neha.singla7@gmail.com

More information

Performance Evaluation of Round Robin Algorithm in Cloud Environment

Performance Evaluation of Round Robin Algorithm in Cloud Environment Performance Evaluation of Round Robin Algorithm in Cloud Environment Asha M L 1 Neethu Myshri R 2 Sowmyashree C.S 3 1,3 AP, Dept. of CSE, SVCE, Bangalore. 2 M.E(dept. of CSE) Student, UVCE, Bangalore.

More information

Cloud Analyst: An Insight of Service Broker Policy

Cloud Analyst: An Insight of Service Broker Policy Cloud Analyst: An Insight of Service Broker Policy Hetal V. Patel 1, Ritesh Patel 2 Student, U & P U. Patel Department of Computer Engineering, CSPIT, CHARUSAT, Changa, Gujarat, India Associate Professor,

More information

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

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

More information

WEIGHTED ROUND ROBIN POLICY FOR SERVICE BROKERS IN A CLOUD ENVIRONMENT

WEIGHTED ROUND ROBIN POLICY FOR SERVICE BROKERS IN A CLOUD ENVIRONMENT WEIGHTED ROUND ROBIN POLICY FOR SERVICE BROKERS IN A CLOUD ENVIRONMENT MOHAMMED RADI Computer Science Department,Faculty of Applied Science Alaqsa University, Gaza Moh_radi@alaqsa.edu.ps ABSTRACT Cloud

More information

CloudAnalyst: A CloudSim-based Tool for Modelling and Analysis of Large Scale Cloud Computing Environments

CloudAnalyst: A CloudSim-based Tool for Modelling and Analysis of Large Scale Cloud Computing Environments 433-659 DISTRIBUTED COMPUTING PROJECT, CSSE DEPT., UNIVERSITY OF MELBOURNE CloudAnalyst: A CloudSim-based Tool for Modelling and Analysis of Large Scale Cloud Computing Environments MEDC Project Report

More information

Dr. J. W. Bakal Principal S. S. JONDHALE College of Engg., Dombivli, India

Dr. J. W. Bakal Principal S. S. JONDHALE College of Engg., Dombivli, India Volume 5, Issue 6, June 2015 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com Factor based Resource

More information

CloudAnalyst: A CloudSim-based Visual Modeller for Analysing Cloud Computing Environments and Applications

CloudAnalyst: A CloudSim-based Visual Modeller for Analysing Cloud Computing Environments and Applications CloudAnalyst: A CloudSim-based Visual Modeller for Analysing Cloud Computing Environments and Applications Bhathiya Wickremasinghe 1, Rodrigo N. Calheiros 2, and Rajkumar Buyya 1 1 The Cloud Computing

More information

SERVICE BROKER ROUTING POLICES IN CLOUD ENVIRONMENT: A SURVEY

SERVICE BROKER ROUTING POLICES IN CLOUD ENVIRONMENT: A SURVEY SERVICE BROKER ROUTING POLICES IN CLOUD ENVIRONMENT: A SURVEY Rekha P M 1 and M Dakshayini 2 1 Department of Information Science & Engineering, VTU, JSS academy of technical Education, Bangalore, Karnataka

More information

Throtelled: An Efficient Load Balancing Policy across Virtual Machines within a Single Data Center

Throtelled: An Efficient Load Balancing Policy across Virtual Machines within a Single Data Center Throtelled: An Efficient Load across Virtual Machines within a Single ata Center Mayanka Gaur, Manmohan Sharma epartment of Computer Science and Engineering, Mody University of Science and Technology,

More information

Keywords: Cloudsim, MIPS, Gridlet, Virtual machine, Data center, Simulation, SaaS, PaaS, IaaS, VM. Introduction

Keywords: Cloudsim, MIPS, Gridlet, Virtual machine, Data center, Simulation, SaaS, PaaS, IaaS, VM. Introduction Vol. 3 Issue 1, January-2014, pp: (1-5), Impact Factor: 1.252, Available online at: www.erpublications.com Performance evaluation of cloud application with constant data center configuration and variable

More information

A Comparative Study on Load Balancing Algorithms with Different Service Broker Policies in Cloud Computing

A Comparative Study on Load Balancing Algorithms with Different Service Broker Policies in Cloud Computing A Comparative Study on Load Balancing Algorithms with Different Service Broker Policies in Cloud Computing Sonia Lamba, Dharmendra Kumar United College of Engineering and Research,Allahabad, U.P, India.

More information

Analysis of Service Broker Policies in Cloud Analyst Framework

Analysis of Service Broker Policies in Cloud Analyst Framework Journal of The International Association of Advanced Technology and Science Analysis of Service Broker Policies in Cloud Analyst Framework Ashish Sankla G.B Pant Govt. Engineering College, Computer Science

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

International Journal of Computer Science Trends and Technology (IJCST) Volume 2 Issue 4, July-Aug 2014

International Journal of Computer Science Trends and Technology (IJCST) Volume 2 Issue 4, July-Aug 2014 RESEARCH ARTICLE An Efficient Priority Based Load Balancing Algorithm for Cloud Environment Harmandeep Singh Brar 1, Vivek Thapar 2 Research Scholar 1, Assistant Professor 2, Department of Computer Science

More information

Profit Based Data Center Service Broker Policy for Cloud Resource Provisioning

Profit Based Data Center Service Broker Policy for Cloud Resource Provisioning I J E E E C International Journal of Electrical, Electronics ISSN No. (Online): 2277-2626 and Computer Engineering 5(1): 54-60(2016) Profit Based Data Center Service Broker Policy for Cloud Resource Provisioning

More information

EFFICIENT VM LOAD BALANCING ALGORITHM FOR A CLOUD COMPUTING ENVIRONMENT

EFFICIENT VM LOAD BALANCING ALGORITHM FOR A CLOUD COMPUTING ENVIRONMENT EFFICIENT VM LOAD BALANCING ALGORITHM FOR A CLOUD COMPUTING ENVIRONMENT Jasmin James, 38 Sector-A, Ambedkar Colony, Govindpura, Bhopal M.P Email:james.jasmin18@gmail.com Dr. Bhupendra Verma, Professor

More information

CloudSim: A Toolkit for Modeling and Simulation of Cloud Computing Environments and Evaluation of Resource Provisioning Algorithms

CloudSim: A Toolkit for Modeling and Simulation of Cloud Computing Environments and Evaluation of Resource Provisioning Algorithms CloudSim: A Toolkit for Modeling and Simulation of Cloud Computing Environments and Evaluation of Resource Provisioning Algorithms Rodrigo N. Calheiros, Rajiv Ranjan, Anton Beloglazov, César A. F. De Rose,

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

A Comparative Study of Load Balancing Algorithms in Cloud Computing

A Comparative Study of Load Balancing Algorithms in Cloud Computing A Comparative Study of Load Balancing Algorithms in Cloud Computing Reena Panwar M.Tech CSE Scholar Department of CSE, Galgotias College of Engineering and Technology, Greater Noida, India Bhawna Mallick,

More information

Simulation-based Evaluation of an Intercloud Service Broker

Simulation-based Evaluation of an Intercloud Service Broker Simulation-based Evaluation of an Intercloud Service Broker Foued Jrad, Jie Tao and Achim Streit Steinbuch Centre for Computing, SCC Karlsruhe Institute of Technology, KIT Karlsruhe, Germany {foued.jrad,

More information

Load Balancing using DWARR Algorithm in Cloud Computing

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

More information

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

Dynamic resource management for energy saving in the cloud computing environment

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

More information

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

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

More information

Study and Comparison of CloudSim Simulators in the Cloud Computing

Study and Comparison of CloudSim Simulators in the Cloud Computing Study and Comparison of CloudSim Simulators in the Cloud Computing Dr. Rahul Malhotra* & Prince Jain** *Director-Principal, Adesh Institute of Technology, Ghauran, Mohali, Punjab, INDIA. E-Mail: blessurahul@gmail.com

More information

An Implementation of Load Balancing Policy for Virtual Machines Associated With a Data Center

An Implementation of Load Balancing Policy for Virtual Machines Associated With a Data Center An Implementation of Load Balancing Policy for Virtual Machines Associated With a Data Center B.SANTHOSH KUMAR Assistant Professor, Department Of Computer Science, G.Pulla Reddy Engineering College. Kurnool-518007,

More information

Dr. Ravi Rastogi Associate Professor Sharda University, Greater Noida, India

Dr. Ravi Rastogi Associate Professor Sharda University, Greater Noida, India Volume 4, Issue 5, May 2014 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com Round Robin Approach

More information

International Journal of Scientific & Engineering Research, Volume 6, Issue 3, March-2015 575 ISSN 2229-5518

International Journal of Scientific & Engineering Research, Volume 6, Issue 3, March-2015 575 ISSN 2229-5518 International Journal of Scientific & Engineering Research, Volume 6, Issue 3, March-2015 575 Simulation-Based Approaches For Evaluating Load Balancing In Cloud Computing With Most Significant Broker Policy

More information

Response Time Minimization of Different Load Balancing Algorithms in Cloud Computing Environment

Response Time Minimization of Different Load Balancing Algorithms in Cloud Computing Environment Response Time Minimization of Different Load Balancing Algorithms in Cloud Computing Environment ABSTRACT Soumya Ranjan Jena Asst. Professor M.I.E.T Dept of CSE Bhubaneswar In the vast complex world the

More information

Cloud Computing Simulation Using CloudSim

Cloud Computing Simulation Using CloudSim Cloud Computing Simulation Using CloudSim Ranjan Kumar #1, G.Sahoo *2 # Assistant Professor, Computer Science & Engineering, Ranchi University, India Professor & Head, Information Technology, Birla Institute

More information

Comparative Study of Scheduling and Service Broker Algorithms in Cloud Computing

Comparative Study of Scheduling and Service Broker Algorithms in Cloud Computing Comparative Study of Scheduling and Service Broker Algorithms in Cloud Computing Santhosh B 1, Raghavendra Naik 2, Balkrishna Yende 3, Dr D.H Manjaiah 4 Assistant Professor, Department of MCA, AIMIT, St

More information

Payment minimization and Error-tolerant Resource Allocation for Cloud System Using equally spread current execution load

Payment minimization and Error-tolerant Resource Allocation for Cloud System Using equally spread current execution load Payment minimization and Error-tolerant Resource Allocation for Cloud System Using equally spread current execution load Pooja.B. Jewargi Prof. Jyoti.Patil Department of computer science and engineering,

More information

Comparison of Dynamic Load Balancing Policies in Data Centers

Comparison of Dynamic Load Balancing Policies in Data Centers Comparison of Dynamic Load Balancing Policies in Data Centers Sunil Kumar Department of Computer Science, Faculty of Science, Banaras Hindu University, Varanasi- 221005, Uttar Pradesh, India. Manish Kumar

More information

CloudAnalyzer: A cloud based deployment framework for Service broker and VM load balancing policies

CloudAnalyzer: A cloud based deployment framework for Service broker and VM load balancing policies CloudAnalyzer: A cloud based deployment framework for Service broker and VM load balancing policies Komal Mahajan 1, Deepak Dahiya 1 1 Dept. of CSE & ICT, Jaypee University Of Information Technology, Waknaghat,

More information

Performance Gathering and Implementing Portability on Cloud Storage Data

Performance Gathering and Implementing Portability on Cloud Storage Data International Journal of Information & Computation Technology. ISSN 0974-2239 Volume 4, Number 17 (2014), pp. 1815-1823 International Research Publications House http://www. irphouse.com Performance Gathering

More information

High performance computing network for cloud environment using simulators

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

More information

A Comparison of Four Popular Heuristics for Load Balancing of Virtual Machines in Cloud Computing

A Comparison of Four Popular Heuristics for Load Balancing of Virtual Machines in Cloud Computing A Comparison of Four Popular Heuristics for Load Balancing of Virtual Machines in Cloud Computing Subasish Mohapatra Department Of CSE NIT, ROURKELA K.Smruti Rekha Department Of CSE ITER, SOA UNIVERSITY

More information

Cost Effective Selection of Data Center in Cloud Environment

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

More information

Effective Virtual Machine Scheduling in Cloud Computing

Effective Virtual Machine Scheduling in Cloud Computing Effective Virtual Machine Scheduling in Cloud Computing Subhash. B. Malewar 1 and Prof-Deepak Kapgate 2 1,2 Department of C.S.E., GHRAET, Nagpur University, Nagpur, India Subhash.info24@gmail.com and deepakkapgate32@gmail.com

More information

Efficient and Enhanced Load Balancing Algorithms in Cloud Computing

Efficient and Enhanced Load Balancing Algorithms in Cloud Computing , pp.9-14 http://dx.doi.org/10.14257/ijgdc.2015.8.2.02 Efficient and Enhanced Load Balancing Algorithms in Cloud Computing Prabhjot Kaur and Dr. Pankaj Deep Kaur M. Tech, CSE P.H.D prabhjotbhullar22@gmail.com,

More information

Comparative Analysis of Load Balancing Algorithms in Cloud Computing

Comparative Analysis of Load Balancing Algorithms in Cloud Computing Comparative Analysis of Load Balancing Algorithms in Cloud Computing Ms.NITIKA Computer Science & Engineering, LPU, Phagwara Punjab, India Abstract- Issues with the performance of business applications

More information

004.738.5:378.091.214.18 ADJUSTING THE MASSIVELY OPEN ONLINE COURSES IN CLOUD COMPUTING ENVIRONMENT 9

004.738.5:378.091.214.18 ADJUSTING THE MASSIVELY OPEN ONLINE COURSES IN CLOUD COMPUTING ENVIRONMENT 9 004.738.5:378.091.214.18 ADJUSTING THE MASSIVELY OPEN ONLINE COURSES IN CLOUD COMPUTING ENVIRONMENT 9 Aleksandar Karadimce, MSc University of information science and technology St. Paul the Apostle Ohrid,

More information

Scheduling Virtual Machines for Load balancing in Cloud Computing Platform

Scheduling Virtual Machines for Load balancing in Cloud Computing Platform Scheduling Virtual Machines for Load balancing in Cloud Computing Platform Supreeth S 1, Shobha Biradar 2 1, 2 Department of Computer Science and Engineering, Reva Institute of Technology and Management

More information

Modeling Local Broker Policy Based on Workload Profile in Network Cloud

Modeling Local Broker Policy Based on Workload Profile in Network Cloud Modeling Local Broker Policy Based on Workload Profile in Network Cloud Amandeep Sandhu 1, Maninder Kaur 2 1 Swami Vivekanand Institute of Engineering and Technology, Banur, Punjab, India 2 Swami Vivekanand

More information

Efficient and Enhanced Algorithm in Cloud Computing

Efficient and Enhanced Algorithm in Cloud Computing International Journal of Soft Computing and Engineering (IJSCE) ISSN: 2231-2307, Volume-3, Issue-1, March 2013 Efficient and Enhanced Algorithm in Cloud Computing Tejinder Sharma, Vijay Kumar Banga Abstract

More information

Load Balancing Algorithm Based on Estimating Finish Time of Services in Cloud Computing

Load Balancing Algorithm Based on Estimating Finish Time of Services in Cloud Computing Load Balancing Algorithm Based on Estimating Finish Time of Services in Cloud Computing Nguyen Khac Chien*, Nguyen Hong Son**, Ho Dac Loc*** * University of the People's Police, Ho Chi Minh city, Viet

More information

SLA BASED SERVICE BROKERING IN INTERCLOUD ENVIRONMENTS

SLA BASED SERVICE BROKERING IN INTERCLOUD ENVIRONMENTS SLA BASED SERVICE BROKERING IN INTERCLOUD ENVIRONMENTS Foued Jrad, Jie Tao and Achim Streit Steinbuch Centre for Computing, Karlsruhe Institute of Technology, Karlsruhe, Germany {foued.jrad, jie.tao, achim.streit}@kit.edu

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

An efficient VM load balancer for Cloud

An efficient VM load balancer for Cloud An efficient VM load balancer for Cloud Ansuyia Makroo 1, Deepak Dahiya 1 1 Dept. of CSE & ICT, Jaypee University Of Information Technology, Waknaghat, HP, India {komal.mahajan, deepak.dahiya}@juit.ac.in

More information

A NOVEL LOAD BALANCING STRATEGY FOR EFFECTIVE UTILIZATION OF VIRTUAL MACHINES IN CLOUD

A NOVEL LOAD BALANCING STRATEGY FOR EFFECTIVE UTILIZATION OF VIRTUAL MACHINES IN CLOUD Available Online at www.ijcsmc.com International Journal of Computer Science and Mobile Computing A Monthly Journal of Computer Science and Information Technology IJCSMC, Vol. 4, Issue. 6, June 2015, pg.862

More information

Increasing QoS in SaaS for low Internet speed connections in cloud

Increasing QoS in SaaS for low Internet speed connections in cloud Proceedings of the 9 th International Conference on Applied Informatics Eger, Hungary, January 29 February 1, 2014. Vol. 1. pp. 195 200 doi: 10.14794/ICAI.9.2014.1.195 Increasing QoS in SaaS for low Internet

More information

International Journal of Engineering Research & Management Technology

International Journal of Engineering Research & Management Technology International Journal of Engineering Research & Management Technology March- 2015 Volume 2, Issue-2 Survey paper on cloud computing with load balancing policy Anant Gaur, Kush Garg Department of CSE SRM

More information

SLA-Driven Simulation of Multi-Tenant Scalable Cloud-Distributed Enterprise Information Systems

SLA-Driven Simulation of Multi-Tenant Scalable Cloud-Distributed Enterprise Information Systems SLA-Driven Simulation of Multi-Tenant Scalable Cloud-Distributed Enterprise Information Systems Alexandru-Florian Antonescu 2, Torsten Braun 2 alexandru-florian.antonescu@sap.com, braun@iam.unibe.ch SAP

More information

A Secure Strategy using Weighted Active Monitoring Load Balancing Algorithm for Maintaining Privacy in Multi-Cloud Environments

A Secure Strategy using Weighted Active Monitoring Load Balancing Algorithm for Maintaining Privacy in Multi-Cloud Environments IJSTE - International Journal of Science Technology & Engineering Volume 1 Issue 10 April 2015 ISSN (online): 2349-784X A Secure Strategy using Weighted Active Monitoring Load Balancing Algorithm for Maintaining

More information

Nutan. N PG student. Girish. L Assistant professor Dept of CSE, CIT GubbiTumkur

Nutan. N PG student. Girish. L Assistant professor Dept of CSE, CIT GubbiTumkur Cloud Data Partitioning For Distributed Load Balancing With Map Reduce Nutan. N PG student Dept of CSE,CIT GubbiTumkur Girish. L Assistant professor Dept of CSE, CIT GubbiTumkur Abstract-Cloud computing

More information

Dynamic Round Robin for Load Balancing in a Cloud Computing

Dynamic Round Robin for Load Balancing in a Cloud Computing Available Online at www.ijcsmc.com International Journal of Computer Science and Mobile Computing A Monthly Journal of Computer Science and Information Technology IJCSMC, Vol. 2, Issue. 6, June 2013, pg.274

More information

VM Provisioning Policies to Improve the Profit of Cloud Infrastructure Service Providers

VM Provisioning Policies to Improve the Profit of Cloud Infrastructure Service Providers VM Provisioning Policies to mprove the Profit of Cloud nfrastructure Service Providers Komal Singh Patel Electronics and Computer Engineering Department nd ian nstitute of Technology Roorkee Roorkee, ndia

More information

Performance Analysis of VM Scheduling Algorithm of CloudSim in Cloud Computing

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

More information

Dynamically optimized cost based task scheduling in Cloud Computing

Dynamically optimized cost based task scheduling in Cloud Computing Dynamically optimized cost based task scheduling in Cloud Computing Yogita Chawla 1, Mansi Bhonsle 2 1,2 Pune university, G.H Raisoni College of Engg & Mgmt, Gate No.: 1200 Wagholi, Pune 412207 Abstract:

More information

Comparative Study of Load Balancing Algorithms in Cloud Environment using Cloud Analyst

Comparative Study of Load Balancing Algorithms in Cloud Environment using Cloud Analyst Comparative Study of Load Balancing Algorithms in Cloud Environment using Cloud Analyst Veerawali Behal Mtech(SS) Student Department of Computer Science & Engineering Guru Nanak Dev University, Amritsar

More information

CloudSimDisk: Energy-Aware Storage Simulation in CloudSim

CloudSimDisk: Energy-Aware Storage Simulation in CloudSim CloudSimDisk: Energy-Aware Storage Simulation in CloudSim Baptiste Louis, Karan Mitra, Saguna Saguna and Christer Åhlund Department of Computer Science, Electrical and Space Engineering Luleå University

More information

CSE LOVELY PROFESSIONAL UNIVERSITY

CSE LOVELY PROFESSIONAL UNIVERSITY Comparison of load balancing algorithms in a Cloud Jaspreet kaur M.TECH CSE LOVELY PROFESSIONAL UNIVERSITY Jalandhar, punjab ABSTRACT This paper presents an approach for scheduling algorithms that can

More information

Design of Simulator for Cloud Computing Infrastructure and Service

Design of Simulator for Cloud Computing Infrastructure and Service , pp. 27-36 http://dx.doi.org/10.14257/ijsh.2014.8.6.03 Design of Simulator for Cloud Computing Infrastructure and Service Changhyeon Kim, Junsang Kim and Won Joo Lee * Dept. of Computer Science and Engineering,

More information

Exploring Inter-Cloud Load Balancing by Utilizing Historical Service Submission Records

Exploring Inter-Cloud Load Balancing by Utilizing Historical Service Submission Records 72 International Journal of Distributed Systems and Technologies, 3(3), 72-81, July-September 2012 Exploring Inter-Cloud Load Balancing by Utilizing Historical Service Submission Records Stelios Sotiriadis,

More information

CLOUD COMPUTING PERFORMANCE EVALUATION: ISSUES AND CHALLENGES

CLOUD COMPUTING PERFORMANCE EVALUATION: ISSUES AND CHALLENGES CLOUD COMPUTING PERFORMANCE EVALUATION: ISSUES AND CHALLENGES Niloofar Khanghahi and Reza Ravanmehr Department of Computer Engineering, Islamic Azad University, Central Tehran Branch, Tehran, Iran ABSTRACT

More information

Webpage: www.ijaret.org Volume 3, Issue XI, Nov. 2015 ISSN 2320-6802

Webpage: www.ijaret.org Volume 3, Issue XI, Nov. 2015 ISSN 2320-6802 An Effective VM scheduling using Hybrid Throttled algorithm for handling resource starvation in Heterogeneous Cloud Environment Er. Navdeep Kaur 1 Er. Pooja Nagpal 2 Dr.Vinay Guatum 3 1 M.Tech Student,

More information

PERFORMANCE ANALYSIS OF PaaS CLOUD COMPUTING SYSTEM

PERFORMANCE ANALYSIS OF PaaS CLOUD COMPUTING SYSTEM PERFORMANCE ANALYSIS OF PaaS CLOUD COMPUTING SYSTEM Akmal Basha 1 Krishna Sagar 2 1 PG Student,Department of Computer Science and Engineering, Madanapalle Institute of Technology & Science, India. 2 Associate

More information

Load Balancing for Improved Quality of Service in the Cloud

Load Balancing for Improved Quality of Service in the Cloud Load Balancing for Improved Quality of Service in the Cloud AMAL ZAOUCH Mathématique informatique et traitement de l information Faculté des Sciences Ben M SIK CASABLANCA, MORROCO FAOUZIA BENABBOU Mathématique

More information

Roulette Wheel Selection Model based on Virtual Machine Weight for Load Balancing in Cloud Computing

Roulette Wheel Selection Model based on Virtual Machine Weight for Load Balancing in Cloud Computing IOSR Journal of Computer Engineering (IOSR-JCE) e-issn: 2278-0661,p-ISSN: 2278-8727, Volume 16, Issue 5, Ver. VII (Sep Oct. 2014), PP 65-70 Roulette Wheel Selection Model based on Virtual Machine Weight

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

Hierarchical Trust Model to Rate Cloud Service Providers based on Infrastructure as a Service

Hierarchical Trust Model to Rate Cloud Service Providers based on Infrastructure as a Service Hierarchical Model to Rate Cloud Service Providers based on Infrastructure as a Service Supriya M 1, Sangeeta K 1, G K Patra 2 1 Department of CSE, Amrita School of Engineering, Amrita Vishwa Vidyapeetham,

More information

A Real-Time Cloud Based Model for Mass Email Delivery

A Real-Time Cloud Based Model for Mass Email Delivery A Real-Time Cloud Based Model for Mass Email Delivery Nyirabahizi Assouma, Mauricio Gomez, Seung-Bae Yang, and Eui-Nam Huh Department of Computer Engineering Kyung Hee University Suwon, South Korea {assouma,mgomez,johnhuh}@khu.ac.kr,

More information

Load Testing on Web Application using Automated Testing Tool: Load Complete

Load Testing on Web Application using Automated Testing Tool: Load Complete Load Testing on Web Application using Automated Testing Tool: Load Complete Neha Thakur, Dr. K.L. Bansal Research Scholar, Department of Computer Science, Himachal Pradesh University, Shimla, India Professor,

More information

Efficient Qos Based Resource Scheduling Using PAPRIKA Method for Cloud Computing

Efficient 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 information

Environments, Services and Network Management for Green Clouds

Environments, Services and Network Management for Green Clouds Environments, Services and Network Management for Green Clouds Carlos Becker Westphall Networks and Management Laboratory Federal University of Santa Catarina MARCH 3RD, REUNION ISLAND IARIA GLOBENET 2012

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

International Journal of Digital Application & Contemporary research Website: www.ijdacr.com (Volume 2, Issue 9, April 2014)

International Journal of Digital Application & Contemporary research Website: www.ijdacr.com (Volume 2, Issue 9, April 2014) Green Cloud Computing: Greedy Algorithms for Virtual Machines Migration and Consolidation to Optimize Energy Consumption in a Data Center Rasoul Beik Islamic Azad University Khomeinishahr Branch, Isfahan,

More information

Efficient Service Broker Algorithm for Data Center Selection in Cloud Computing

Efficient Service Broker Algorithm for Data Center Selection in Cloud Computing Available Online at www.ijcsmc.com International Journal of Computer Science and Mobile Computing A Monthly Journal of Computer Science and Information Technology IJCSMC, Vol. 3, Issue. 1, January 2014,

More information

Enhanced Load Balancing Approach to Avoid Deadlocks in Cloud

Enhanced Load Balancing Approach to Avoid Deadlocks in Cloud Enhanced Load Balancing Approach to Avoid Deadlocks in Cloud Rashmi K S Post Graduate Programme, Computer Science and Engineering, Department of Information Science and Engineering, Dayananda Sagar College

More information

An Efficient Adaptive Load Balancing Algorithm for Cloud Computing Under Bursty Workloads

An Efficient Adaptive Load Balancing Algorithm for Cloud Computing Under Bursty Workloads Engineering, Technology & Applied Science Research Vol. 5, No. 3, 2015, 795-800 795 An Efficient Adaptive Load Balancing Algorithm for Cloud Computing Under Bursty Workloads Sally F. Issawi Faculty of

More information

Storage CloudSim: A Simulation Environment for Cloud Object Storage Infrastructures

Storage CloudSim: A Simulation Environment for Cloud Object Storage Infrastructures Storage CloudSim: A Simulation Environment for Cloud Object Storage Infrastructures http://github.com/toebbel/storagecloudsim tobias.sturm@student.kit.edu, {foud.jrad, achim.streit}@kit.edu STEINBUCH CENTRE

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

Energy Constrained Resource Scheduling for Cloud Environment

Energy Constrained Resource Scheduling for Cloud Environment Energy Constrained Resource Scheduling for Cloud Environment 1 R.Selvi, 2 S.Russia, 3 V.K.Anitha 1 2 nd Year M.E.(Software Engineering), 2 Assistant Professor Department of IT KSR Institute for Engineering

More information

NetworkCloudSim: Modelling Parallel Applications in Cloud Simulations

NetworkCloudSim: Modelling Parallel Applications in Cloud Simulations 2011 Fourth IEEE International Conference on Utility and Cloud Computing NetworkCloudSim: Modelling Parallel Applications in Cloud Simulations Saurabh Kumar Garg and Rajkumar Buyya Cloud Computing and

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

Load Balancing in Cloud Computing using Stochastic Hill Climbing-A Soft Computing Approach

Load Balancing in Cloud Computing using Stochastic Hill Climbing-A Soft Computing Approach Available online at www.sciencedirect.com Procedia Technology 4 (2012 ) 783 789 C3IT-2012 Load Balancing in Cloud Computing Stochastic Hill Climbing-A Soft Computing Approach Brototi Mondal a,, Kousik

More information

ENERGY EFFICIENT VIRTUAL MACHINE ASSIGNMENT BASED ON ENERGY CONSUMPTION AND RESOURCE UTILIZATION IN CLOUD NETWORK

ENERGY EFFICIENT VIRTUAL MACHINE ASSIGNMENT BASED ON ENERGY CONSUMPTION AND RESOURCE UTILIZATION IN CLOUD NETWORK International Journal of Computer Engineering & Technology (IJCET) Volume 7, Issue 1, Jan-Feb 2016, pp. 45-53, Article ID: IJCET_07_01_006 Available online at http://www.iaeme.com/ijcet/issues.asp?jtype=ijcet&vtype=7&itype=1

More information

An Efficient Hybrid P2P MMOG Cloud Architecture for Dynamic Load Management. Ginhung Wang, Kuochen Wang

An Efficient Hybrid P2P MMOG Cloud Architecture for Dynamic Load Management. Ginhung Wang, Kuochen Wang 1 An Efficient Hybrid MMOG Cloud Architecture for Dynamic Load Management Ginhung Wang, Kuochen Wang Abstract- In recent years, massively multiplayer online games (MMOGs) become more and more popular.

More information

Estimating Trust Value for Cloud Service Providers using Fuzzy Logic

Estimating Trust Value for Cloud Service Providers using Fuzzy Logic Estimating Trust Value for Cloud Service Providers using Fuzzy Logic Supriya M, Venkataramana L.J, K Sangeeta Department of Computer Science and Engineering, Amrita School of Engineering Kasavanahalli,

More information

HOST SCHEDULING ALGORITHM USING GENETIC ALGORITHM IN CLOUD COMPUTING ENVIRONMENT

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

More information

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

Cloud Performance and Load Balancing Algorithm

Cloud Performance and Load Balancing Algorithm Cloud Performance and Load Balancing Algorithm 1 Cloud Performance and Load Balancing Algorithm In cloud computing paradigm, application and data are stored in data center of cloud provider which is located

More information

Profit Maximization Of SAAS By Reusing The Available VM Space In Cloud Computing

Profit Maximization Of SAAS By Reusing The Available VM Space In Cloud Computing www.ijecs.in International Journal Of Engineering And Computer Science ISSN: 2319-7242 Volume 4 Issue 8 Aug 2015, Page No. 13822-13827 Profit Maximization Of SAAS By Reusing The Available VM Space In Cloud

More information

Simulation of Dynamic Load Balancing Algorithms

Simulation of Dynamic Load Balancing Algorithms Bonfring International Journal of Software Engineering and Soft Computing, Vol. 5, No.1, July 2015 1 Simulation of Dynamic Load Balancing Algorithms Dr.S. Suguna and R. Barani Abstract--- Cloud computing

More information