Minimizing Response Time for Scheduled Tasks Using the Improved Particle Swarm Optimization Algorithm in a Cloud Computing Environment

Save this PDF as:
 WORD  PNG  TXT  JPG

Size: px
Start display at page:

Download "Minimizing Response Time for Scheduled Tasks Using the Improved Particle Swarm Optimization Algorithm in a Cloud Computing Environment"

Transcription

1 Minimizing Response Time for Scheduled Tasks Using the Improved Particle Swarm Optimization Algorithm in a Cloud Computing Environment by Maryam Houtinezhad, Department of Computer Engineering, Artificial Intelligence, Kerman Branch, Islamic Azad University, Kerman, Iran, Amid Khatibi Bardsiri, Department of Computer Engineering, Software Engineering, Bardsir Branch, Islamic Azad University, Kerman, Iran Abstract: Distributed systems, like cloud and grid, offer their services in the form of a service to customers. Cloud computing is a developed model of distributed calculations based on Tcp/ip. This technology, which is an evolving phenomenon in the large and complex information platform, is recognized as an optimum and fast solution. Using optimum scheduling to allocate jobs to different virtual machines is one of the key problems in this environment. Scheduling is a part of management issues that has always drawn researchers' attention due to its effectiveness in the real world. Using effective strategies in scheduling significantly helps the system efficiency. The results of using the proposed scheduling strategy, based on improved particle swarm optimization algorithm, showed achieving minimum time in execution of tasks. 1. Introduction Cloud computing is a pattern of distributed calculations comprised of many virtual machines and requests aiming at sharing resources as a service on the Internet [REF-1] that is created to facilitate sharing large complex of computational calculations, such as networks, servers, storage systems and services. Low cost and cost effectiveness of cloud computing has made it an ideal platform for use in service-oriented architecture. Cloud computing is a developed model of distributed calculations which is significantly paid attention to [REF- 2, REF-3]. Cloud computing provides a new and optimum platform to offer, consume and deliver IT services. The term cloud computing is defined by the National Institute of Standards and Technology (NIST) as follows: cloud computing is a model which performs an operation considering the users' information needs. Users can easily access huge complex of adjustable calculation resources such as networks, servers, storage systems, practical applications and services. This access happens quickly, easily, without server interruption and with the least amount of management and communication with the service provider. Scheduling tasks is one of the key issues in a distributed cloud environment [REF-4]. Data and calculation resources are stored on the user's personal computer in a normal calculation model, but calculation resources in cloud computing are vastly provided in abstract, organized infrastructure with smart servers. Users can easily access these resources [REF-5]. The cloud computing model facilitates the process of installation, operation and management of information systems, decreases costs, and also causes an increase in system reliability and efficiency. Different resources such as IT resources, software, hardware, operating system and storage management and their management can be virtualized in a cloud environment. Virtualization provides the possibility of operating multi-virtual machines on a physical hardware [REF-6]. The cloud environment uses virtualization technologies to better use resources on a virtual machine layer and perform users' applications; therefore, proper scheduling between practical applications and virtual machine resources is crucial. According to different works, we find out that each of the provided methods is only advantageous in its particular practical domain, and none of them can provide for several service needs for several workflows. This paper focuses on moving towards reaching the short response time, as well as other effective parameters such as resource utilization, system overall performance and minimization of the introduced cost function. 1

2 2. Cloud Environment Structure A distributed cloud environment delivers everything as a service, and cloud services are divided into three layers [REF-7, REF-8]: Infrastructure as a Service (IaaS) allows customers to use calculation resources such as storage resources. Customers pay the cost of the service per usage amount. Amazon (wwww.aws.amazon.com/ec2) is an example of this service. Platform as a Service (PaaS) provides a virtual server to customers to develop their applications. Google App Engine and Amazon Web Services are examples of this type of service. Software as a Service (SaaS) is a type of distributed software. The software as a service provides a service to the consumer as a host. Google Apps ( and Salesforce ( are examples of this service. Cloud computing encourages organizations to use these services in order to carry out their activities, emphasizing on financial, technical and time saving [REF-9]. The aforementioned division would cause support of management of different levels of a cloud environment, as well as virtualization technology [REF-10]. 3. Related Work Scheduling policies in a cloud environment highly depend on the development model in the cloud. However, what causes improvement in the quality of service is optimization of time and cost in resource allocation to entering requests to the cloud environment. Scheduling is considered part of NP-Complete problems for which there is no effective solution. Due to importance of scheduling in distributed cloud environment, there are attempts to create an optimum scheduling for execution of tasks and resource allocation. In fact, the priority is to determine a processing resource, out of the group of resources that a task needs for processing, in such a way that more tasks are processed in less time. On the other hand, sharing resources a lot causes a decrease in overall performance. Buyya has introduced an economic framework in cloud environment in which the users pay the service providers in return for resources usage. This rule creates motivation in service providers. Buyya's economic cloud model reminds the importance of cost, time and economic benefit in users and resource owners' eye. Hence, it is necessary to create proper scheduling [REF-11]. Rotary Chaotic Particle Swarm Optimization (RCPSO) was used by Tao et al. for a scheduling problem in distributed grid environment in their study. This algorithm tries to keep the quality of service by focusing on time and cost optimization. The proposed method can do the scheduling in a polynomial and complex space [REF- 12]. 2

3 Figure 1 - workflow cycle with 15 tasks [REF-12] In this study, position and velocity of each particle in each frequency is calculated as below: Where xij(t) is the position of each particle and vij(y) is the velocity of each particle. The following equation is used for updating the velocity of each particle: Where Pbestij(y) is the best position for particle I with dimension of j, and Gbestj(y) is the j best dimension of the best particle. Figure (2) shows searching and finding in different dimensions. 3

4 Figure 2 - search How in particles 3D space [REF-12] In [REF-13], ant colony meta-heuristic algorithm and particle swarm optimization have been used in order to schedule resources in cloud computing. Since ant colony algorithm easily gets stuck in local optimization, particle swarm optimization algorithm was used in this study which leads to several groups of solutions with the use of ant colony algorithm, regarding the updated pheromones. Then, effective solutions carry out crossover and mutation operation with the use of particle swarm optimization. As a result, optimum response is achieved. It is reported that not only this algorithm improves convergence rate/speed, but also prevents getting stuck in optimum local responses and also has been able to reach its goal of proper resource allocation in cloud computing and improve the resources efficiency [REF-13]. In another study, resource scheduling in service level agreements has been proposed. Scheduling is recognized as a problem for which there is no solution. In this study, a new method for solving the scheduling problem was provided, using Grobner's theory. Grobner's theory provides a solution in random integers for cloud computing. Scheduling is carried out in two steps in this method. The findings showed the proposed method was optimum, this method has been reported to be proper for multi-goal resource scheduling [REF- 14]. 4. The Proposed Algorithm Scheduling problems with dynamic resources need optimization algorithms that in addition to finding the optimum, can also follow the changing optimums. In recent years, particle swarm optimization has drawn a lot of attention to itself among different optimization algorithms for dynamic environments. Most tasks in cloud computing are dynamic. In other words, the existing optimums change over time, hence there is a need for algorithms in these environments that can find the changing optimum as well as properly following the changing optimum. Therefore, those algorithms are considered eligible in cloud computing in which the average error in solutions found is the least at any point in time. 4

5 4.1. Genetic Algorithm As it's obvious by its name, this algorithm follows genetics. Genetics is a science that discusses heredity and transfer of biological features from one generation to another one. Chromosomes and genes are the main factor in biological feature transfer and they work in such a way those superior and stronger genes and chromosomes survive while weaker genes are eliminated. In other words, mutual operation of genes and chromosomes results in the survival of elite and superior creatures. Genetics work with bit strings; each bit string shows the whole variants while most methods treat special variants independently. // Pseudo Code Algorithm Genetic Begin t=0; initialization pop P(t); Evaluate P(t); While not Termination Condination Do parent Selection P(t); Recombine P(t) to yield C(t); Mutate C(t); Evaluate C(t); Survive (P(t),C(t)) to yield P(t+1); t=t+1; End End Figure 3 - Genetic algorithm pseudo code The genetic algorithm selects the most proper strings out of organized random information. A new group of strings using the best parts of previous sequences and the new random part is comprised to reach a proper response [REF-17] Particle Swarm Optimization Algorithm Particle swarm optimization algorithm (PSO) is a simple technic based on stochastic rules that is increasingly common due to the ability to solve complex problems and different numerical functions. This algorithm is inspired by social behaviors of animal`s such as flocks of birds and shoals of fish, instead of being inspired by evolutionary mechanisms. The PSO is randomly initialized with a population of particles. Then, the algorithm searches for optimum responses by frequently updating generations. In each generation, the position and velocity of particle i is updated using personal best and global best in the population. 5

6 Calculation process, personal best and global best are recorded. Finally, the best global position is considered as the final solution by holding stop conditions. It is worth mentioning that updating position and velocity is achieved using equation (4) and (5). Where r1 and r2 are random numbers between (0, 1) and c1 and c2 are constant acceleration. Appropriation of responses among personal and global amounts shows variety in responses. Optimization policy forces particles to move toward particular areas in which the amount of target function is minimum, so in the end, all particles gather around the points with the highest target function [REF-15, REF-16]. // Pseudo Code Algorithm PSO For each particle Initialize particle End For Do For each particle Calculate fitness value of the particle fp //updating particle's best fitness value so far If fp is better than pbest set current value as the new pbest End For //updating population s best fitness value so far Set gbest to the best fitness value of all particles For each particle Calculate particle velocity according equation (4) Update particle position according equation (5) End For While max iterations or min error criteria is not attained Figure 4 - Particle swarm optimization algorithm pseudo code 6

7 3.4. Combining two meta-heuristic algorithms Particle swarm optimization algorithm has some similarities and differences compared with genetic algorithm. Both particle swarm optimization and genetic method begin with an early random population. The evolution will repeat by investigating each component competency and sharing the information and production of new population until stop conditions are achieved. The difference is that the information division mechanism in particle swarm optimization is different to genetic algorithm; all chromosomes share information with each other in genetic algorithm. Therefore, the entire population moves towards the optimum area as a group. Unfortunately, particle swarm optimization algorithm suffers from early convergence. Hence, combining it with another algorithm is needed in order to solve early convergence problem. Combination of particle swarm optimization and genetic algorithm was used in this study to avoid getting stuck in local optimization. In the end, improved particle swarm optimization algorithm was used to solve scheduling problem in distributed cloud environment. // Pseudo Code Algorithm Proposal Method For each particle t=0; initialization particle P(t); Evaluate P(t); End For Do For each particle Calculate fitness value of the particle fp //updating particle s best fitness value so far If fp is better than pbest set current value as the new pbest End For //updating population s best fitness value so far Set gbest to the best fitness value of all particles parent Selection P(t); Recombine P(t) to yield C(t); Mutate C(t); Evaluate C(t); Survive (P(t),C(t)) to yield P(t+1); t=t+1; For each particle Calculate particle velocity according equation (10) 7

8 Update particle position according equation (11) End For While not Termination Condination Figure 5 - Proposed algorithm pseudo code 4.3. Proposed Scheduling Policy The main motivation to use cloud computing is decreasing resources costs. Calculation resources in cloud computing systems are recognized as virtual machines. Scheduling algorithms are of great importance, since the scheduling priority is to reduce response time and improve resource exploitation. Tasks are assigned to virtual machines considering the priority [REF-18]. Expected Time to Compute (ETC) matrix was used to estimate execution of tasks approximate time. Each row of the matrix shows the execution of tasks time on different resources and each column of the matrix shows the needed time for a resource to execute different tasks. This matrix is of T*M size in which T is number of tasks and M is number of resources. In this study, all three ETC matrices consistent, inconsistent and semiconsistent were used in order to create a distributed platform. First, the initiation of task execution is shown by ST and execution finish time (task completion) is shown by FT. the following equation is used to calculate initiation and completion time for task i: 8

9 ST and FT for next tasks in each job is carried out recessively and in accordance with the first entry task initiation time. Where pred(tj) is the set of predecessor task i. FT(tp) is completion time of predecessor task. Total task completion time can be calculated by using maximum completion time after completion of all tasks: Here C max was considered equal to f1. This criterion was used to calculate final cost function with certain weight. Hence, f1 is considered the first criterion. Another considerable criterion is deadlines and delivery time for each task which is shown by f2. If the user announces maximum time for task completion to the system but the scheduler cannot complete the task at hand in the designated time period, it will be subject to a penalty [REF-19]; total delay penalty for tasks can be calculated by equation (9). FTi is the scheduler's completion time and di is the time period announced by the user for each task. The other goal is maximizing resources utilization. The expected utilization for each resource should be calculated based on its attribution in the work cycle in order to achieve this goal. FTJout is completion time of tasks in each job and Cmax is completion time of all jobs. By increasing resources utilization, another one of our goals which is maintaining the quality of service is met[ref-20]. For resources utilization to be eligible enough, its average is calculated based on equation (7): 5. Simulation Results In order to evaluate the proposed compound algorithm, we compared it with similar prevalent algorithms. The evaluation was carried out in comparison with Particle Swarm standard algorithm, Imperialist Competitive 9

10 Algorithm and Differential Evolutionary algorithm. The implementation in three platforms; consistent, semiconsistent and inconsistent was evaluated with high heterogeneity and same proposed policy was applied for all algorithms. Finding in table (1_4) represent 4 important criteria including Cost Function, Overall Performance, Utilization and total completion time of tasks(makespan). Algorithm Inconsistence Consistence Semi consistence Proposal Method e e e+05 PSO e e e+05 ICA e e e+05 Cost Function DE e e e+5 Table 1 - Comparing The Cost Function Hybrid Method On Consistence, Inconsistence & Semi consistence Environment Algorithm Inconsistence Consistence Semi consistence Proposal Method e e e+06 PSO e e e+06 ICA e e e+06 DE e e e+06 Overall Performance Table 2 - Comparing The Overall Performance Hybrid Method On Consistence, Inconsistence & Semi consistence Environment 10

11 Algorithm Inconsistence Consistence Semi consistence Proposal Method PSO ICA e+03 Utilization DE Table 3 - Comparing The Utilization Hybrid Method On Consistence, Inconsistence & Semi consistence Environment Algorithm Inconsistence Consistence Semi consistence Proposal Method PSO ICA MakeSpan DE Table 4 - Comparing The Makespan Hybrid Method On Consistence, Inconsistence & Semi consistence Environment 6. Conclusion and Future Work In order to prevent early convergence, particle swarm optimization algorithm was combined with genetic algorithm operators in this paper. Also, to optimize tasks allocation to virtual machines in distributed cloud computing environment, a scheduling policy was provided which calculated important criteria in utilization, completion time, cost function and imposed penalty simultaneously. Most researches consider only minimization or maximization of a single criterion while in this study, different mentioned criteria were separately calculated and finally applied in cost function and scheduler's algorithm overall performance. Implementation results and their comparison with other meta-heuristic algorithms separately represent achieving better results in different criteria. Combining three meta-heuristic algorithms to minimize response time and other arbitrary parameters in other distributed environments such as grid can be used as future solution. 11

12 7. References [REF-1] Yu J, Buyya R, "Workflow Scheduling Algorithms for Grid Computing", Metaheuristics for Scheduling in Distributed Computing Environments, Springer, Vol 146, PP , [REF-2] Zhang S, Chen X, Huo X, "Cloud Computing Research Development Trend", Proceedings of the second International Conference On Future Networks, IEEE Computer Society, PP 93-97, January [REF-3] Buyya R, Yeo CS, Venugopal S, Broberg J, Brandic I, "Cloud Computing And Emerging IT Platforms: Vision, Hype, And Reality For Delivering Computing As The 5th Utility", Future Generation Computer Systems,Vol 25, No 6, PP , June [REF-4] Kaur Sh, Verma A, "An Efficiant Approach to Genetic Algorithm for Task Scheduling in Cloud Computing Environment", Information Technology and Computer Science, Vol. 10, pp , [REF-5] Topcuouglu H, Hariri S, Wu M, "Performance-Effective And Low Complexity Task Scheduling For Heterogeneous Computing",IEEE Transactions on Parallel and Distribution Systems, Vol 13, No 3, PP , March [REF-6] IBM " Point of View:Security and Cloud Computing", Cloud omputingwhite papernovember [REF-7] P. Mell, T. Grance, The NIST Definition of Cloud Computing, (Draft)- Recommendations of the National Institute of Standards and Technology,Special publication (draft), Gaithersburg (MD). [REF-8] R. Buyya, S. Pandey and C. Vecchiola, Cloudbus toolkit for marketoriented cloud computing, In CloudCom'09: Proceedings of the 1st International Conference on Cloud Computing, volume 5931 of LNCS, pp , Springer, Germany, December [REF-9] Chandra Misra S, Mondal A, "Identification Of A Company Suitability For The Adoption Of Cloud Computing And Modeling Its Corresponding Return On Investment", Mathematical and Computer Modeling, Vol 53, No 4, PP , February [REF-10] Hayes B, "Cloud computing", Communications Of The ACM, [REF-11] Buyya R, Krauter K, Maheswaran M," A Taxonomy And Survey Of Grid Resource Managment System For Distributed Computing", Journal Of Software Practice And Experience, Vol 32, No 2, PP , February [REF-12] Tao Q, Chang HY, Yi Y, Gu C, Wen jie, Li WJ," A Rotary Chaotic PSO Algorithm For Trustworthy Scheduling Of A Grid Workflow ",Computers And Operations Research, Vol 38, No 5, PP , May [REF-13] Xiaotang Wen, Minghe Huang, Jianhua Shi," Study on Resources Scheduling Based on ACO Algorithm and PSO Algorithm in Cloud Computing", Distributed Computing and Applications to Business, Engineering & Science (DCABES), 11th International Symposium, [REF-14] Qiang Li, Yike Guo," Optimization of Resource Scheduling in Cloud Computing ", Symbolic and Numeric Algorithms for Scientific Computing (SYNASC), 12th International Symposium on, / SYNASC , IEEE, pp ,23-26 Sept [REF-15] Kennedy J, Eberhart R, "Particle swarm optimization", Proceedings Of IEEE International Conference On Neural Networks, Piscataway: IEEE, [REF-16] Jones K.O, "Comparison Of Genetic Algorithm And Particle Swarm Optimisation", International Conference on Computer Systems and Technologies CompSysTech,

13 [REF-17] Goldberg, D.E., Genetic Algorithms in Search. Optimization & Machine Learning. Addison-Wesley Reading,1989. [REF-18] Lakshmi V, Prathibha S," ANovel Approach For Task Scheduling In Cloud", IEEE, 4 Th ICCCNT, [REF-19] Zhao.C, Zhang. S, Liu.Q,." Independent Tasks Scheduling Based on Genetic Algorithm in Cloud Computing", Sponsored by the National Innovative Project, IEEE, /09/$25.00,2009. [REF-20] Bessai.K, Youcef.s, Oulamara.A, Claude Godart, Nurcan.A, "Bicriteria workow tasks allocation and scheduling in Cloud computing environments", IEEE, International Conference on Cloud Computing, Hawaii, United States. IEEE, pp , IEEE Fifth International Conference on Cloud Computing. Jun 2012.In fact, where commoditization of service management pushed the topic down in the decision tree, the trend is reversing to a point where service management leaders can play a very important role in influencing the workforce enablement strategy. In an environment of agile workforce enablement, making things easier for the organization is the most important for enterprise IT leaders, making it easier for the employees to get supported is more important than ever. 13

14 Maryam Houtinezhad Maryam Houtinezhad received her M.Sc. degree in Artificial Intelligence from Science and Research branch of Islamic Azad University, Kerman, Iran in She has been teaching since 2011 and is currently teaching at Islamic Azad University. Her research interests lie in computational intelligence, metaheuristic algorithms, distributed systems, and cloud computing, especially in resource allocation and scheduling tasks. Contributions Minimizing Response Time for Scheduled Tasks Using the Improved Particle Swarm Optimization Algorithm in a Cloud Computing Environment Amid Khatibi Bardsiri Amid received his BSc degree in Computer Software Enginering from Shahid Bahonar University in Kerman, Iran in 2008, and his MSc degree in Software Engineering from Islamic Azad University in Tehran, Iran in He is currently undertaking his PhD in the Science and Research branch of Islamic Azad University, focusing on software service metrics and measurement. He has published about 30 research papers in international journals and conference proceedings. His areas of research include software systems engineering, software service development, software service metrics, SOA, and cloud computing. Contributions Minimizing Response Time for Scheduled Tasks Using the Improved Particle Swarm Optimization Algorithm in a Cloud Computing Environment Development Effort Estimation Metrics Suite for Global Village Service 14

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

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

More information

ACO Based Dynamic Resource Scheduling for Improving Cloud Performance

ACO Based Dynamic Resource Scheduling for Improving Cloud Performance ACO Based Dynamic Resource Scheduling for Improving Cloud Performance Priyanka Mod 1, Prof. Mayank Bhatt 2 Computer Science Engineering Rishiraj Institute of Technology 1 Computer Science Engineering Rishiraj

More information

A Binary Model on the Basis of Imperialist Competitive Algorithm in Order to Solve the Problem of Knapsack 1-0

A Binary Model on the Basis of Imperialist Competitive Algorithm in Order to Solve the Problem of Knapsack 1-0 212 International Conference on System Engineering and Modeling (ICSEM 212) IPCSIT vol. 34 (212) (212) IACSIT Press, Singapore A Binary Model on the Basis of Imperialist Competitive Algorithm in Order

More information

14.10.2014. Overview. Swarms in nature. Fish, birds, ants, termites, Introduction to swarm intelligence principles Particle Swarm Optimization (PSO)

14.10.2014. Overview. Swarms in nature. Fish, birds, ants, termites, Introduction to swarm intelligence principles Particle Swarm Optimization (PSO) Overview Kyrre Glette kyrrehg@ifi INF3490 Swarm Intelligence Particle Swarm Optimization Introduction to swarm intelligence principles Particle Swarm Optimization (PSO) 3 Swarms in nature Fish, birds,

More information

Performance Evaluation of Task Scheduling in Cloud Environment Using Soft Computing Algorithms

Performance Evaluation of Task Scheduling in Cloud Environment Using Soft Computing Algorithms 387 Performance Evaluation of Task Scheduling in Cloud Environment Using Soft Computing Algorithms 1 R. Jemina Priyadarsini, 2 Dr. L. Arockiam 1 Department of Computer science, St. Joseph s College, Trichirapalli,

More information

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

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

More information

A SURVEY ON WORKFLOW SCHEDULING IN CLOUD USING ANT COLONY OPTIMIZATION

A SURVEY ON WORKFLOW SCHEDULING IN CLOUD USING ANT COLONY OPTIMIZATION 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. 2, February 2014,

More information

Resource Provisioning in Single Tier and Multi-Tier Cloud Computing: State-of-the-Art

Resource Provisioning in Single Tier and Multi-Tier Cloud Computing: State-of-the-Art Resource Provisioning in Single Tier and Multi-Tier Cloud Computing: State-of-the-Art Marwah Hashim Eawna Faculty of Computer and Information Sciences Salma Hamdy Mohammed Faculty of Computer and Information

More information

A RANDOMIZED LOAD BALANCING ALGORITHM IN GRID USING MAX MIN PSO ALGORITHM

A RANDOMIZED LOAD BALANCING ALGORITHM IN GRID USING MAX MIN PSO ALGORITHM International Journal of Research in Computer Science eissn 2249-8265 Volume 2 Issue 3 (212) pp. 17-23 White Globe Publications A RANDOMIZED LOAD BALANCING ALGORITHM IN GRID USING MAX MIN ALGORITHM C.Kalpana

More information

A Service Revenue-oriented Task Scheduling Model of Cloud Computing

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

More information

Research on the Performance Optimization of Hadoop in Big Data Environment

Research on the Performance Optimization of Hadoop in Big Data Environment Vol.8, No.5 (015), pp.93-304 http://dx.doi.org/10.1457/idta.015.8.5.6 Research on the Performance Optimization of Hadoop in Big Data Environment Jia Min-Zheng Department of Information Engineering, Beiing

More information

Comparison of Various Particle Swarm Optimization based Algorithms in Cloud Computing

Comparison of Various Particle Swarm Optimization based Algorithms in Cloud Computing Comparison of Various Particle Swarm Optimization based Algorithms in Cloud Computing Er. Talwinder Kaur M.Tech (CSE) SSIET, Dera Bassi, Punjab, India Email- talwinder_2@yahoo.co.in Er. Seema Pahwa Department

More information

A TunableWorkflow Scheduling AlgorithmBased on Particle Swarm Optimization for Cloud Computing

A TunableWorkflow Scheduling AlgorithmBased on Particle Swarm Optimization for Cloud Computing A TunableWorkflow Scheduling AlgorithmBased on Particle Swarm Optimization for Cloud Computing Jing Huang, Kai Wu, Lok Kei Leong, Seungbeom Ma, and Melody Moh Department of Computer Science San Jose State

More information

Fig. 1 WfMC Workflow reference Model

Fig. 1 WfMC Workflow reference Model International Journal of Information & Computation Technology. ISSN 0974-2239 Volume 4, Number 10 (2014), pp. 997-1002 International Research Publications House http://www. irphouse.com Survey Paper on

More information

Efficient Cloud Computing Scheduling: Comparing Classic Algorithms with Generic Algorithm

Efficient Cloud Computing Scheduling: Comparing Classic Algorithms with Generic Algorithm International Journal of Computer Networks and Communications Security VOL., NO. 7, JULY 2015, 271 276 Available online at: www.ijcncs.org E-ISSN 208-980 (Online) / ISSN 2410-0595 (Print) Efficient Cloud

More information

Target Based Resource Allocation and Scheduling Algorithm for Systematic Workflow in Cloud

Target Based Resource Allocation and Scheduling Algorithm for Systematic Workflow in Cloud Target Based Resource Allocation and Scheduling Algorithm for Systematic Workflow in Cloud Lokesha K. R. 1, Dr. K Raghuveer 2 1 MTech, CNE Prof & HOD 2 NIE, Mysuru Information Science and Engineering,

More information

Resource Scheduling in Cloud using Bacterial Foraging Optimization Algorithm

Resource Scheduling in Cloud using Bacterial Foraging Optimization Algorithm Resource Scheduling in Cloud using Bacterial Foraging Optimization Algorithm Liji Jacob Department of computer science Karunya University Coimbatore V.Jeyakrishanan Department of computer science Karunya

More information

CLOUD COMPUTING: A NEW VISION OF THE DISTRIBUTED SYSTEM

CLOUD COMPUTING: A NEW VISION OF THE DISTRIBUTED SYSTEM CLOUD COMPUTING: A NEW VISION OF THE DISTRIBUTED SYSTEM Taha Chaabouni 1 and Maher Khemakhem 2 1 MIRACL Lab, FSEG, University of Sfax, Sfax, Tunisia chaabounitaha@yahoo.fr 2 MIRACL Lab, FSEG, University

More information

SCORE BASED DEADLINE CONSTRAINED WORKFLOW SCHEDULING ALGORITHM FOR CLOUD SYSTEMS

SCORE BASED DEADLINE CONSTRAINED WORKFLOW SCHEDULING ALGORITHM FOR CLOUD SYSTEMS SCORE BASED DEADLINE CONSTRAINED WORKFLOW SCHEDULING ALGORITHM FOR CLOUD SYSTEMS Ranjit Singh and Sarbjeet Singh Computer Science and Engineering, Panjab University, Chandigarh, India ABSTRACT Cloud Computing

More information

Dynamic Task Scheduling with Load Balancing using Hybrid Particle Swarm Optimization

Dynamic Task Scheduling with Load Balancing using Hybrid Particle Swarm Optimization Int. J. Open Problems Compt. Math., Vol. 2, No. 3, September 2009 ISSN 1998-6262; Copyright ICSRS Publication, 2009 www.i-csrs.org Dynamic Task Scheduling with Load Balancing using Hybrid Particle Swarm

More information

Optimal PID Controller Design for AVR System

Optimal PID Controller Design for AVR System Tamkang Journal of Science and Engineering, Vol. 2, No. 3, pp. 259 270 (2009) 259 Optimal PID Controller Design for AVR System Ching-Chang Wong*, Shih-An Li and Hou-Yi Wang Department of Electrical Engineering,

More information

Cloud Template, a Big Data Solution

Cloud Template, a Big Data Solution Template, a Big Data Solution Mehdi Bahrami Electronic Engineering and Computer Science Department University of California, Merced, USA MBahrami@UCMerced.edu Abstract. Today cloud computing has become

More information

Hybrid Algorithm using the advantage of ACO and Cuckoo Search for Job Scheduling

Hybrid Algorithm using the advantage of ACO and Cuckoo Search for Job Scheduling Hybrid Algorithm using the advantage of ACO and Cuckoo Search for Job Scheduling R.G. Babukartik 1, P. Dhavachelvan 1 1 Department of Computer Science, Pondicherry University, Pondicherry, India {r.g.babukarthik,

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

Cost Minimized PSO based Workflow Scheduling Plan for Cloud Computing

Cost Minimized PSO based Workflow Scheduling Plan for Cloud Computing I.J. Information Technology and Computer Science, 5, 8, 7-4 Published Online July 5 in MECS (http://www.mecs-press.org/) DOI: 85/ijitcs.5.8.6 Cost Minimized PSO based Workflow Scheduling Plan for Cloud

More information

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

A Load Balancing Model Based on Cloud Partitioning for the Public Cloud International Journal of Information & Computation Technology. ISSN 0974-2239 Volume 4, Number 16 (2014), pp. 1605-1610 International Research Publications House http://www. irphouse.com A Load Balancing

More information

Optimizing Resource Consumption in Computational Cloud Using Enhanced ACO Algorithm

Optimizing Resource Consumption in Computational Cloud Using Enhanced ACO Algorithm Optimizing Resource Consumption in Computational Cloud Using Enhanced ACO Algorithm Preeti Kushwah, Dr. Abhay Kothari Department of Computer Science & Engineering, Acropolis Institute of Technology and

More information

A Hybrid Model of Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) Algorithm for Test Case Optimization

A Hybrid Model of Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) Algorithm for Test Case Optimization A Hybrid Model of Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) Algorithm for Test Case Optimization Abraham Kiran Joseph a, Dr. G. Radhamani b * a Research Scholar, Dr.G.R Damodaran

More information

FEDERATED CLOUD: A DEVELOPMENT IN CLOUD COMPUTING AND A SOLUTION TO EDUCATIONAL NEEDS

FEDERATED CLOUD: A DEVELOPMENT IN CLOUD COMPUTING AND A SOLUTION TO EDUCATIONAL NEEDS International Journal of Computer Engineering and Applications, Volume VIII, Issue II, November 14 FEDERATED CLOUD: A DEVELOPMENT IN CLOUD COMPUTING AND A SOLUTION TO EDUCATIONAL NEEDS Saju Mathew 1, Dr.

More information

A Multi-Objective Performance Evaluation in Grid Task Scheduling using Evolutionary Algorithms

A Multi-Objective Performance Evaluation in Grid Task Scheduling using Evolutionary Algorithms A Multi-Objective Performance Evaluation in Grid Task Scheduling using Evolutionary Algorithms MIGUEL CAMELO, YEZID DONOSO, HAROLD CASTRO Systems and Computer Engineering Department Universidad de los

More information

Inertia Weight Strategies in Particle Swarm Optimization

Inertia Weight Strategies in Particle Swarm Optimization Inertia Weight Strategies in Particle Swarm Optimization 1 J. C. Bansal, 2 P. K. Singh 3 Mukesh Saraswat, 4 Abhishek Verma, 5 Shimpi Singh Jadon, 6,7 Ajith Abraham 1,2,3,4,5 ABV-Indian Institute of Information

More information

A Survey on Cloud Computing

A Survey on Cloud Computing A Survey on Cloud Computing Poulami dalapati* Department of Computer Science Birla Institute of Technology, Mesra Ranchi, India dalapati89@gmail.com G. Sahoo Department of Information Technology Birla

More information

Dynamic Generation of Test Cases with Metaheuristics

Dynamic Generation of Test Cases with Metaheuristics Dynamic Generation of Test Cases with Metaheuristics Laura Lanzarini, Juan Pablo La Battaglia III-LIDI (Institute of Research in Computer Science LIDI) Faculty of Computer Sciences. National University

More information

Optimization of PID parameters with an improved simplex PSO

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

More information

A Novel Binary Particle Swarm Optimization

A Novel Binary Particle Swarm Optimization Proceedings of the 5th Mediterranean Conference on T33- A Novel Binary Particle Swarm Optimization Motaba Ahmadieh Khanesar, Member, IEEE, Mohammad Teshnehlab and Mahdi Aliyari Shoorehdeli K. N. Toosi

More information

WORKFLOW ENGINE FOR CLOUDS

WORKFLOW ENGINE FOR CLOUDS WORKFLOW ENGINE FOR CLOUDS By SURAJ PANDEY, DILEBAN KARUNAMOORTHY, and RAJKUMAR BUYYA Prepared by: Dr. Faramarz Safi Islamic Azad University, Najafabad Branch, Esfahan, Iran. Workflow Engine for clouds

More information

Presenter: Hamed Vahdat-Nejad

Presenter: Hamed Vahdat-Nejad Hussein Shirvani Pervasive and Cloud Computing Lab University of Birjand, Birjand, Iran hussein.shirvani.1992@ieee.org Hamed Vahdat-Nejad Pervasive and Cloud Computing Lab University of Birjand, Birjand,

More information

Permanent Link: http://espace.library.curtin.edu.au/r?func=dbin-jump-full&local_base=gen01-era02&object_id=154091

Permanent Link: http://espace.library.curtin.edu.au/r?func=dbin-jump-full&local_base=gen01-era02&object_id=154091 Citation: Alhamad, Mohammed and Dillon, Tharam S. and Wu, Chen and Chang, Elizabeth. 2010. Response time for cloud computing providers, in Kotsis, G. and Taniar, D. and Pardede, E. and Saleh, I. and Khalil,

More information

A Genetic Algorithm Approach for Solving a Flexible Job Shop Scheduling Problem

A Genetic Algorithm Approach for Solving a Flexible Job Shop Scheduling Problem A Genetic Algorithm Approach for Solving a Flexible Job Shop Scheduling Problem Sayedmohammadreza Vaghefinezhad 1, Kuan Yew Wong 2 1 Department of Manufacturing & Industrial Engineering, Faculty of Mechanical

More information

Genetic Algorithm Based Interconnection Network Topology Optimization Analysis

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

More information

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

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

More information

Comparative Study of Ant Colony Optimization and Particle Swarm Optimization for Grid Scheduling

Comparative Study of Ant Colony Optimization and Particle Swarm Optimization for Grid Scheduling R. Shakerian, S. H. Kamali, M. Hedayati, M. Alipour/ TJMCS Vol.2 No.3 (2011) 469-474 The Journal of Mathematics and Computer Science Available online at http://www.tjmcs.com The Journal of Mathematics

More information

International Journal of Software and Web Sciences (IJSWS) www.iasir.net

International Journal of Software and Web Sciences (IJSWS) www.iasir.net International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research) ISSN (Print): 2279-0063 ISSN (Online): 2279-0071 International

More information

A SURVEY ON LOAD BALANCING ALGORITHMS IN CLOUD COMPUTING

A SURVEY ON LOAD BALANCING ALGORITHMS IN CLOUD COMPUTING A SURVEY ON LOAD BALANCING ALGORITHMS IN CLOUD COMPUTING Harshada Raut 1, Kumud Wasnik 2 1 M.Tech. Student, Dept. of Computer Science and Tech., UMIT, S.N.D.T. Women s University, (India) 2 Professor,

More information

An Efficient Approach for Task Scheduling Based on Multi-Objective Genetic Algorithm in Cloud Computing Environment

An Efficient Approach for Task Scheduling Based on Multi-Objective Genetic Algorithm in Cloud Computing Environment IJCSC VOLUME 5 NUMBER 2 JULY-SEPT 2014 PP. 110-115 ISSN-0973-7391 An Efficient Approach for Task Scheduling Based on Multi-Objective Genetic Algorithm in Cloud Computing Environment 1 Sourabh Budhiraja,

More information

An ACO-LB Algorithm for Task Scheduling in the Cloud Environment

An ACO-LB Algorithm for Task Scheduling in the Cloud Environment 466 JOURNAL OF SOFTWARE, VOL. 9, NO. 2, FEBRUARY 2014 An ACO-LB Algorithm for Task Scheduling in the Cloud Environment Shengjun Xue, Mengying Li, Xiaolong Xu, and Jingyi Chen Nanjing University of Information

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

Web Service Selection using Particle Swarm Optimization and Genetic Algorithms

Web Service Selection using Particle Swarm Optimization and Genetic Algorithms Web Service Selection using Particle Swarm Optimization and Genetic Algorithms Simone A. Ludwig Department of Computer Science North Dakota State University Fargo, ND, USA simone.ludwig@ndsu.edu Thomas

More information

An Evolutionary Algorithm in Grid Scheduling by multiobjective Optimization using variants of NSGA

An Evolutionary Algorithm in Grid Scheduling by multiobjective Optimization using variants of NSGA International Journal of Scientific and Research Publications, Volume 2, Issue 9, September 2012 1 An Evolutionary Algorithm in Grid Scheduling by multiobjective Optimization using variants of NSGA Shahista

More information

LOAD BALANCING IN CLOUD COMPUTING

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

More information

ISSN (Print): 2328-3491, ISSN (Online): 2328-3580, ISSN (CD-ROM): 2328-3629

ISSN (Print): 2328-3491, ISSN (Online): 2328-3580, ISSN (CD-ROM): 2328-3629 American International Journal of Research in Science, Technology, Engineering & Mathematics Available online at http://www.iasir.net ISSN (Print): 2328-3491, ISSN (Online): 2328-3580, ISSN (CD-ROM): 2328-3629

More information

ISSN: 2321-7782 (Online) Volume 2, Issue 3, March 2014 International Journal of Advance Research in Computer Science and Management Studies

ISSN: 2321-7782 (Online) Volume 2, Issue 3, March 2014 International Journal of Advance Research in Computer Science and Management Studies ISSN: 2321-7782 (Online) Volume 2, Issue 3, March 2014 International Journal of Advance Research in Computer Science and Management Studies Research Article / Paper / Case Study Available online at: www.ijarcsms.com

More information

A hybrid Approach of Genetic Algorithm and Particle Swarm Technique to Software Test Case Generation

A hybrid Approach of Genetic Algorithm and Particle Swarm Technique to Software Test Case Generation A hybrid Approach of Genetic Algorithm and Particle Swarm Technique to Software Test Case Generation Abhishek Singh Department of Information Technology Amity School of Engineering and Technology Amity

More information

BMOA: Binary Magnetic Optimization Algorithm

BMOA: Binary Magnetic Optimization Algorithm International Journal of Machine Learning and Computing Vol. 2 No. 3 June 22 BMOA: Binary Magnetic Optimization Algorithm SeyedAli Mirjalili and Siti Zaiton Mohd Hashim Abstract Recently the behavior of

More information

International 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. 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 information

Swinburne Research Bank http://researchbank.swinburne.edu.au

Swinburne Research Bank http://researchbank.swinburne.edu.au Swinburne Research Bank http://researchbank.swinburne.edu.au Wu, Z., Liu, X., Ni, Z., Yuan, D., & Yang, Y. (2013). A market-oriented hierarchical scheduling strategy in cloud workflow systems. Originally

More information

GENETIC-BASED SOLUTIONS FOR INDEPENDENT BATCH SCHEDULING IN DATA GRIDS

GENETIC-BASED SOLUTIONS FOR INDEPENDENT BATCH SCHEDULING IN DATA GRIDS GENETIC-BASED SOLUTIONS FOR INDEPENDENT BATCH SCHEDULING IN DATA GRIDS Joanna Ko lodziej Cracow University of Technology, Poland Email: jokolodziej@pk.edu.pl Samee U. Khan North Dakota State University

More information

GA as a Data Optimization Tool for Predictive Analytics

GA as a Data Optimization Tool for Predictive Analytics GA as a Data Optimization Tool for Predictive Analytics Chandra.J 1, Dr.Nachamai.M 2,Dr.Anitha.S.Pillai 3 1Assistant Professor, Department of computer Science, Christ University, Bangalore,India, chandra.j@christunivesity.in

More information

BRCM College of Computer Science & Tech., Bahal, India

BRCM College of Computer Science & Tech., Bahal, India Level Based Optimized Workflow Scheduling In Cloud Environment Divya Kataria 1, Dr. Sudesh Kumar 2 1 PG Scholar, 2 Head of Department, CSE BRCM College of Computer Science & Tech., Bahal, India Abstract-

More information

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

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

More information

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

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

More information

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

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

More information

An ACO Approach to Solve a Variant of TSP

An ACO Approach to Solve a Variant of TSP An ACO Approach to Solve a Variant of TSP Bharat V. Chawda, Nitesh M. Sureja Abstract This study is an investigation on the application of Ant Colony Optimization to a variant of TSP. This paper presents

More information

Comparison among four Modified Discrete Particle Swarm Optimization for Task Scheduling in Heterogeneous Computing Systems

Comparison among four Modified Discrete Particle Swarm Optimization for Task Scheduling in Heterogeneous Computing Systems International Journal of Soft Computing and Engineering (IJSCE) ISSN: 3-37, Volume-3, Issue-, May 3 Comparison among four Modified Discrete Particle Swarm Optimization for Task Scheduling in Heterogeneous

More information

A* Algorithm Based Optimization for Cloud Storage

A* Algorithm Based Optimization for Cloud Storage International Journal of Digital Content Technology and its Applications Volume 4, Number 8, November 21 A* Algorithm Based Optimization for Cloud Storage 1 Ren Xun-Yi, 2 Ma Xiao-Dong 1* College of Computer

More information

An Explorative Model for B2B Cloud Service Adoption in Korea - Focusing on IaaS Adoption

An Explorative Model for B2B Cloud Service Adoption in Korea - Focusing on IaaS Adoption , pp.155-164 http://dx.doi.org/10.14257/ijsh.2013.7.5.16 An Explorative Model for B2B Cloud Service Adoption in Korea - Focusing on IaaS Adoption Kwang-Kyu Seo Department of Management Engineering, Sangmyung

More information

Improved PSO-based Task Scheduling Algorithm in Cloud Computing

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

More information

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

PROCESS OF LOAD BALANCING IN CLOUD COMPUTING USING GENETIC ALGORITHM

PROCESS OF LOAD BALANCING IN CLOUD COMPUTING USING GENETIC ALGORITHM PROCESS OF LOAD BALANCING IN CLOUD COMPUTING USING GENETIC ALGORITHM Md. Shahjahan Kabir 1, Kh. Mohaimenul Kabir 2 and Dr. Rabiul Islam 3 1 Dept. of CSE, Dhaka International University, Dhaka, Bangladesh

More information

Comparison of Probabilistic Optimization Algorithms for Resource Scheduling in Cloud Computing Environment

Comparison of Probabilistic Optimization Algorithms for Resource Scheduling in Cloud Computing Environment Comparison of Probabilistic Optimization Algorithms for Resource Scheduling in Cloud Computing Environment Mayank Singh Rana *1, Sendhil Kumar KS *2, Jaisankar N *3 * School of Computing Science and Engineering,

More information

A cost efficient two-level market model for task scheduling problem in grid environment

A cost efficient two-level market model for task scheduling problem in grid environment A cost efficient two-level market model for task scheduling problem in grid environment Sara Kardani-Moghaddam, Reza Entezari-Maleki **, and Ali Movaghar Department of Computer Engineering, Sharif University

More information

Self-organized Multi-agent System for Service Management in the Next Generation Networks

Self-organized Multi-agent System for Service Management in the Next Generation Networks PROCEEDINGS OF THE WORKSHOP ON APPLICATIONS OF SOFTWARE AGENTS ISBN 978-86-7031-188-6, pp. 18-24, 2011 Self-organized Multi-agent System for Service Management in the Next Generation Networks Mario Kusek

More information

International Journal of Scientific Research Engineering & Technology (IJSRET)

International Journal of Scientific Research Engineering & Technology (IJSRET) CHROME: IMPROVING THE TRANSMISSION RELIABILITY BY BANDWIDTH OPTIMIZATION USING HYBRID ALGORITHM 1 Ajeeth Kumar J, 2 C.P Maheswaran, Noorul Islam University Abstract - An approach to improve the transmission

More information

Trust-Based Scheduling Strategy for Cloud Workflow Applications

Trust-Based Scheduling Strategy for Cloud Workflow Applications INFORMATICA, 2015, Vol. 26, No. 1, 159 180 159 2015 Vilnius University DOI: http://dx.doi.org/10.15388/informatica.2015.43 Trust-Based Scheduling Strategy for Cloud Workflow Applications Y.L. YANG 1,2,

More information

Participatory Cloud Computing and the Privacy and Security of Medical Information Applied to A Wireless Smart Board Network

Participatory Cloud Computing and the Privacy and Security of Medical Information Applied to A Wireless Smart Board Network Participatory Cloud Computing and the Privacy and Security of Medical Information Applied to A Wireless Smart Board Network Lutando Ngqakaza ngqlut003@myuct.ac.za UCT Department of Computer Science Abstract:

More information

Méta-heuristiques pour l optimisation

Méta-heuristiques pour l optimisation Méta-heuristiques pour l optimisation Differential Evolution (DE) Particle Swarm Optimization (PSO) Alain Dutech Equipe MAIA - LORIA - INRIA Nancy, France Web : http://maia.loria.fr Mail : Alain.Dutech@loria.fr

More information

Evaluation of Different Task Scheduling Policies in Multi-Core Systems with Reconfigurable Hardware

Evaluation of Different Task Scheduling Policies in Multi-Core Systems with Reconfigurable Hardware Evaluation of Different Task Scheduling Policies in Multi-Core Systems with Reconfigurable Hardware Mahyar Shahsavari, Zaid Al-Ars, Koen Bertels,1, Computer Engineering Group, Software & Computer Technology

More information

QoS Guaranteed Intelligent Routing Using Hybrid PSO-GA in Wireless Mesh Networks

QoS Guaranteed Intelligent Routing Using Hybrid PSO-GA in Wireless Mesh Networks BULGARIAN ACADEMY OF SCIENCES CYBERNETICS AND INFORMATION TECHNOLOGIES Volume 15, No 1 Sofia 2015 Print ISSN: 1311-9702; Online ISSN: 1314-4081 DOI: 10.1515/cait-2015-0007 QoS Guaranteed Intelligent Routing

More information

A Framework to Improve Communication and Reliability Between Cloud Consumer and Provider in the Cloud

A Framework to Improve Communication and Reliability Between Cloud Consumer and Provider in the Cloud A Framework to Improve Communication and Reliability Between Cloud Consumer and Provider in the Cloud Vivek Sridhar Rational Software Group (India Software Labs) IBM India Bangalore, India Abstract Cloud

More information

Biogeography Based Optimization (BBO) Approach for Sensor Selection in Aircraft Engine

Biogeography Based Optimization (BBO) Approach for Sensor Selection in Aircraft Engine Biogeography Based Optimization (BBO) Approach for Sensor Selection in Aircraft Engine V.Hymavathi, B.Abdul Rahim, Fahimuddin.Shaik P.G Scholar, (M.Tech), Department of Electronics and Communication Engineering,

More information

DEVELOPMENT OF A COMPUTATIONAL INTELLIGENCE COURSE FOR UNDERGRADUATE AND GRADUATE STUDENTS

DEVELOPMENT OF A COMPUTATIONAL INTELLIGENCE COURSE FOR UNDERGRADUATE AND GRADUATE STUDENTS DEELOPMENT OF A COMPUTATIONAL INTELLIGENCE COURSE FOR UNDERGRADUATE AND GRADUATE STUDENTS Ganesh K. enayagamoorthy Department of Electrical and Computer Engineering University of Missouri Rolla, MO 65409,

More information

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

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

More information

LOAD BALANCING IN CLOUD USING ACO AND GENETIC ALGORITHM

LOAD BALANCING IN CLOUD USING ACO AND GENETIC ALGORITHM 724 LOAD BALANCING IN CLOUD USING ACO AND GENETIC ALGORITHM *Parveen Kumar Research Scholar Guru Kashi University, Talwandi Sabo ** Er.Mandeep Kaur Assistant Professor Guru Kashi University, Talwandi Sabo

More information

An Ontology-Based Approach for Optimal Resource Allocation in Vehicular Cloud Computing

An Ontology-Based Approach for Optimal Resource Allocation in Vehicular 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. 4, Issue. 2, February 2015,

More information

An Overview on Important Aspects of Cloud Computing

An Overview on Important Aspects of Cloud Computing An Overview on Important Aspects of Cloud Computing 1 Masthan Patnaik, 2 Ruksana Begum 1 Asst. Professor, 2 Final M Tech Student 1,2 Dept of Computer Science and Engineering 1,2 Laxminarayan Institute

More information

A Hybrid Load Balancing Policy underlying Cloud Computing Environment

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

More information

A Cloud-Based Retail Management System

A Cloud-Based Retail Management System A Cloud-Based Retail Management System Adewole Adewumi 1, Stanley Ogbuchi 1, and Sanjay MIsra 1 1 Department of Computer and Information Sciences, Covenant University, Ota, Nigeria {wole.adewumi, stanley.ogbuchi,

More information

A New Approach in Software Cost Estimation with Hybrid of Bee Colony and Chaos Optimizations Algorithms

A New Approach in Software Cost Estimation with Hybrid of Bee Colony and Chaos Optimizations Algorithms A New Approach in Software Cost Estimation with Hybrid of Bee Colony and Chaos Optimizations Algorithms Farhad Soleimanian Gharehchopogh 1 and Zahra Asheghi Dizaji 2 1 Department of Computer Engineering,

More information

APPLICATION OF ADVANCED SEARCH- METHODS FOR AUTOMOTIVE DATA-BUS SYSTEM SIGNAL INTEGRITY OPTIMIZATION

APPLICATION OF ADVANCED SEARCH- METHODS FOR AUTOMOTIVE DATA-BUS SYSTEM SIGNAL INTEGRITY OPTIMIZATION APPLICATION OF ADVANCED SEARCH- METHODS FOR AUTOMOTIVE DATA-BUS SYSTEM SIGNAL INTEGRITY OPTIMIZATION Harald Günther 1, Stephan Frei 1, Thomas Wenzel, Wolfgang Mickisch 1 Technische Universität Dortmund,

More information

Game Theory Based Iaas Services Composition in Cloud Computing

Game 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 information

COMPUTATIONIMPROVEMENTOFSTOCKMARKETDECISIONMAKING MODELTHROUGHTHEAPPLICATIONOFGRID. Jovita Nenortaitė

COMPUTATIONIMPROVEMENTOFSTOCKMARKETDECISIONMAKING MODELTHROUGHTHEAPPLICATIONOFGRID. Jovita Nenortaitė ISSN 1392 124X INFORMATION TECHNOLOGY AND CONTROL, 2005, Vol.34, No.3 COMPUTATIONIMPROVEMENTOFSTOCKMARKETDECISIONMAKING MODELTHROUGHTHEAPPLICATIONOFGRID Jovita Nenortaitė InformaticsDepartment,VilniusUniversityKaunasFacultyofHumanities

More information

Soft Computing Models for Cloud Service Optimization

Soft Computing Models for Cloud Service Optimization Soft Computing Models for Cloud Service Optimization G. Albeanu, Spiru Haret University & Fl. Popentiu-Vladicescu UNESCO Department, University of Oradea Abstract The cloud computing paradigm has already

More information

Wireless Sensor Networks Coverage Optimization based on Improved AFSA Algorithm

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

More information

Research Article Service Composition Optimization Using Differential Evolution and Opposition-based Learning

Research Article Service Composition Optimization Using Differential Evolution and Opposition-based Learning Research Journal of Applied Sciences, Engineering and Technology 11(2): 229-234, 2015 ISSN: 2040-7459; e-issn: 2040-7467 2015 Maxwell Scientific Publication Corp. Submitted: May 20, 2015 Accepted: June

More information

AS-PSO, Ant Supervised by PSO Meta-heuristic with Application to TSP.

AS-PSO, Ant Supervised by PSO Meta-heuristic with Application to TSP. AS-PSO, Ant Supervised by PSO Meta-heuristic with Application to TSP. Nizar Rokbani *1, Arsene L. Momasso *2, Adel.M Alimi *3 # REGIM-Lab, Research Groups on Intelligent Machine University of Sfax, Tunisia

More information

Hybrid Job scheduling Algorithm for Cloud computing Environment

Hybrid Job scheduling Algorithm for Cloud computing Environment Hybrid Job scheduling Algorithm for Cloud computing Environment Saeed Javanmardi 1, Mohammad Shojafar 2, Danilo Amendola 2, Nicola Cordeschi 2, Hongbo Liu 3, and Ajith Abraham 4,5 1 Department of Computer

More information

D A T A M I N I N G C L A S S I F I C A T I O N

D A T A M I N I N G C L A S S I F I C A T I O N D A T A M I N I N G C L A S S I F I C A T I O N FABRICIO VOZNIKA LEO NARDO VIA NA INTRODUCTION Nowadays there is huge amount of data being collected and stored in databases everywhere across the globe.

More information

Data Security Strategy Based on Artificial Immune Algorithm for Cloud Computing

Data 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 information

A Study on Service Oriented Network Virtualization convergence of Cloud Computing

A Study on Service Oriented Network Virtualization convergence of Cloud Computing A Study on Service Oriented Network Virtualization convergence of Cloud Computing 1 Kajjam Vinay Kumar, 2 SANTHOSH BODDUPALLI 1 Scholar(M.Tech),Department of Computer Science Engineering, Brilliant Institute

More information

TASK SCHEDULING IN CLOUD COMPUTING

TASK SCHEDULING IN CLOUD COMPUTING TASK SCHEDULING IN CLOUD COMPUTING SONIA SINDHU 1 Assistant professor, Govt.College for Women Jind, Haryana,India Abstract: Recently, there has been a dramatic increase in the popularity of cloud computing

More information