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Wireless Personal Communications
Erschienen in: Wireless Personal Communications 2/2020

29.07.2020

Optimize Task Allocation in Cloud Environment Based on Big-Bang Big-Crunch

verfasst von: Pradeep Singh Rawat, Priti Dimri, Soumen Kanrar, Gyanendra Pal Saroha

Erschienen in: Wireless Personal Communications | Ausgabe 2/2020

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Abstract

Efficient resource allocation is indispensable in the current scenario of a service-oriented computing paradigm. Instance allocation to the host and the task allocation to the instance depends on the efficiency of scheduling technique. In this work, we exhibit the provisioning of tasks or cloudlets on a virtual machine. The Big-Bang Big-Crunch-cost model is proposed for efficient resource allocation. The proposed technique supports the principle of optimization method and performance is measured using makespan and resource cost. Our proposed cost-aware Big-Bang- Big-Crunch model, provides an optimal solution using the IaaS (Infrastructure as a service) model. It supports dynamic and independent task allocation on virtual machines. The proposed technique proclaims an evolution scheme that measures an objective function depends on performance metrics cost and time respectively. The input dataset defines the number of host nodes and datacenter configuration. The learning, evolution-based on BB-BC cost-aware method provides a globally optimal solution in a dynamic resource provisioning environment. Our approach effectively finds optimal simulation results than existing static, dynamic, and bio-inspired evolutionary provisioning techniques. Simulation results are exhibited that the cost-aware Big-Bang Big-Crunch method illustrates an adequate schedule of tasks on respective virtual machines. Reliability is measured using the operational cost of the resources in execution duration. Efficient resource utilization and the global optimum solution depends on the fitness function. The simulation results illustrate that our cost-aware astrology based soft computing methodology provides better results than time aware and cost-aware scheduling approaches. From simulation results, it is observed that Big-Bang Big-Crunch Cost aware proposed methodology improves average finish time by 15.23% with user requests 300, and average finish time improves by 19.18% with population size 400. The performance metric average resource cost enhancement by 30.46% with population size 400. The infrastructure cloud is considered for the performance measurement of the proposed cost-aware model which is constituted using static, dynamic, and meta-heuristic bio-inspired resource allocation techniques.
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Literatur
1.
Ang, T. F., Por, L. Y., & Liew, C. S. (2017). Dynamic pricing scheme for resource allocation in multi-cloud environment. Malaysian Journal of Computer Science, 30(1), 1–17.CrossRef
2.
Radojević, B., & Žagar, M. (2011). Analysis of issues with load balancing algorithms in hosted (cloud) environments. In 2011 Proceedings of the 34th international convention MIPRO (pp. 416–420). IEEE.
3.
Lu, X., & Gu, Z. (2011). A load-adapative cloud resource scheduling model based on ant colony algorithm. In 2011 IEEE international conference on cloud computing and intelligence systems (pp. 296–300). IEEE.
4.
Chang, X., Xia, R., Muppala, J. K., Trivedi, K. S., & Liu, J. (2016). Effective modeling approach for IaaS data center performance analysis under heterogeneous workload. IEEE Transactions on Cloud Computing, 6(4), 991–1003.CrossRef
5.
Singh, S., & Chana, I. (2016). A survey on resource scheduling in cloud computing: Issues and challenges. Journal of Grid Computing, 14(2), 217–264.CrossRef
6.
Rawat, P. (2018). A survey and analysis with different resource provisioning strategies in cloud environment. Future Generation Computer Systems, 1, 339–345.
7.
Sheikh, H. F., Ahmad, I., & Fan, D. (2015). An evolutionary technique for performance-energy-temperature optimized scheduling of parallel tasks on multi-core processors. IEEE Transactions on Parallel and Distributed Systems, 27(3), 668–681.CrossRef
8.
Gupta, P., & Ghrera, S. P. (2016). Trust and deadline aware scheduling algorithm for cloud infrastructure using ant colony optimization. In 2016 International conference on innovation and challenges in cyber security (ICICCS-INBUSH) (pp. 187–191). IEEE.
9.
Vasudewa, K., & Gupta, P. (2016). A survey on elastic resource allocation algorithm for cloud infrastructure. In 2016 International conference on innovation and challenges in cyber security (ICICCS-INBUSH) (pp. 199–203). IEEE.
10.
Joseph, C. T., Chandrasekaran, K., & Cyriac, R. (2015). A novel family genetic approach for virtual machine allocation. Procedia Computer Science, 46, 558–565.CrossRef
11.
Aggarwal, R. (2018). Resource provisioning and resource allocation in cloud computing environment. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 3.
12.
Gao, R., & Wu, J. (2015). Dynamic load balancing strategy for cloud computing with ant colony optimization. Future Internet, 7(4), 465–483.CrossRef
13.
Goswami, N., Garala, K., & Maheta, P. (2015). Cloud load balancing based on ant colony optimization algorithm. IOSR Journal of Computer Engineering, 3, 11–18.
14.
Kanrar, S. (2012). Enhancement of job allocation in private Cloud by distributed processing. In: Proceedings of the second international conference on computational science, engineering and information technology CCSEIT ‘12 (pp. 94–98). ACM.
15.
Suresh, A., & Varatharajan, R. (2017). Competent resource provisioning and distribution techniques for cloud computing environment. Cluster Computing, pp. 1–8.
16.
Mansouri, N., & Javidi, M. M. (2019). Cost-based job scheduling strategy in cloud computing environments. Distributed and Parallel Databases, pp. 1–36.
17.
Lu, Y., & Sun, N. (2019). An effective task scheduling algorithm based on dynamic energy management and efficient resource utilization in green cloud computing environment. Cluster Computing, 22(1), 513–520.MathSciNetCrossRef
18.
Wei, L., Foh, C. H., He, B., & Cai, J. (2015). Towards efficient resource allocation for heterogeneous workloads in iaas clouds. IEEE Transactions on Cloud Computing, 6(1), 264–275.CrossRef
19.
Kruekaew, B., & Kimpan, W. (2014). Virtual machine scheduling management on cloud computing using artificial bee colony. In Proceedings of the International MultiConference of engineers and computer scientists (Vol. 1, pp. 12–14).
20.
Jha, R. S., & Gupta, P. (2015). Power aware resource virtual machine allocation policy for cloud infrastructure. In 2015 Third International Conference on Image Information Processing (ICIIP) (pp. 256–260). IEEE.
21.
Madni, S. H. H., Latiff, M. S. A., & Coulibaly, Y. (2017). Recent advancements in resource allocation techniques for cloud computing environment: A systematic review. Cluster Computing, 20(3), 2489–2533.CrossRef
22.
Chiang, C. W., Lee, Y. C., Lee, C. N., & Chou, T. Y. (2006). Ant colony optimisation for task matching and scheduling. IEE Proceedings-Computers and Digital Techniques, 153(6), 373–380.CrossRef
23.
Jaradat, G. M., & Ayob, M. (2010). Big bang-big crunch optimization algorithm to solve the course timetabling problem. In 2010 10th International conference on intelligent systems design and applications (pp. 1448–1452). IEEE.
24.
Delavar, A. G., & Aryan, Y. (2014). HSGA: a hybrid heuristic algorithm for workflow scheduling in cloud systems. Cluster Computing, 17(1), 129–137.CrossRef
25.
Singh, P., Dutta, M., & Aggarwal, N. (2017). A review of task scheduling based on meta-heuristics approach in cloud computing. Knowledge and Information Systems, 52(1), 1–51.CrossRef
26.
Kalra, M., & Singh, S. (2015). A review of metaheuristic scheduling techniques in cloud computing. Egyptian Informatics Journal, 16(3), 275–295.CrossRef
27.
Erol, O. K., & Eksin, I. (2006). A new optimization method: Big bang–big crunch. Advances in Engineering Software, 37(2), 106–111.CrossRef
28.
Correia, S. D., Beko, M., da Silva Cruz, L. A., & Tomic, S. (2018). Elephant herding optimization for energy-based localization. Sensors, 18(9), 2849.
29.
Cho, K. M., Tsai, P. W., Tsai, C. W., & Yang, C. S. (2015). A hybrid meta-heuristic algorithm for VM scheduling with load balancing in cloud computing. Neural Computing and Applications, 26(6), 1297–1309.CrossRef
30.
Katyal, M., & Mishra, A. (2014). A comparative study of load balancing algorithms in cloud computing environment. arXiv:​1403.​6918.
31.
Mishra, S. K., Puthal, D., Sahoo, B., Jena, S. K., & Obaidat, M. S. (2018). An adaptive task allocation technique for green cloud computing. The Journal of Supercomputing, 74(1), 370–385.CrossRef
32.
Juarez, F., Ejarque, J., & Badia, R. M. (2018). Dynamic energy-aware scheduling for parallel task-based application in cloud computing. Future Generation Computer Systems, 78, 257–271.CrossRef
33.
Rawat, P. S., Saroha, G. P., & Barthwal, V. (2012). Performance evaluation of social networking application with different load balancing policy across virtual machine in a single data center using cloudanalyst. In 2012 2nd IEEE International conference on parallel, distributed and grid computing (pp. 469–473). IEEE.
34.
Jin, X., Zhang, F., Wang, L., Hu, S., Zhou, B., & Liu, Z. (2015). Joint optimization of operational cost and performance interference in cloud data centers. IEEE Transactions on Cloud Computing, 5(4), 697–711.CrossRef
35.
Liu, J., Zhang, Y., Zhou, Y., Zhang, D., & Liu, H. (2014). Aggressive resource provisioning for ensuring QoS in virtualized environments. IEEE Transactions on Cloud Computing, 3(2), 119–131.CrossRef
36.
Abrishami, S., Naghibzadeh, M., & Epema, D. H. (2013). Deadline-constrained workflow scheduling algorithms for infrastructure as a service clouds. Future Generation Computer Systems, 29(1), 158–169.CrossRef
37.
Zhang, F., Cao, J., Hwang, K., Li, K., & Khan, S. U. (2014). Adaptive workflow scheduling on cloud computing platforms with iterativeordinal optimization. IEEE Transactions on Cloud Computing, 3(2), 156–168.CrossRef
38.
Beheshti, Z., & Shamsuddin, S. M. H. (2013). A review of population-based meta-heuristic algorithms. International Journal of Advances in Soft Computing Application, 5(1), 1–35.
39.
Dam, S., Mandal, G., Dasgupta, K., & Dutta, P. (2014). An ant colony based load balancing strategy in cloud computing. In Advanced computing, networking and informatics (Vol. 2, pp. 403–413). Springer, Cham.
40.
Kousalya, K., & Balasubramanie, P. (2009). To improve ant algorithm’s grid scheduling using local search. International Journal of Cognitive Computing in Engineering, 7(4), 47–57.
41.
Tawfeek, M. A., El-Sisi, A., Keshk, A. E., & Torkey, F. A. (2013). Cloud task scheduling based on ant colony optimization. In 2013 8th international conference on computer engineering & systems (ICCES) (pp. 64–69). IEEE.
42.
Zheng, Z., Wang, R., Zhong, H., & Zhang, X. (2011). An approach for cloud resource scheduling based on Parallel Genetic Algorithm. In 2011 3rd International conference on computer research and development (Vol. 2, pp. 444–447). IEEE.
43.
Pooranian, Z., Shojafar, M., Abawajy, J. H., & Abraham, A. (2015). An efficient meta-heuristic algorithm for grid computing. Journal of Combinatorial Optimization, 30(3), 413–434.MathSciNetMATHCrossRef
44.
Yu, J., & Buyya, R. (2006). Scheduling scientific workflow applications with deadline and budget constraints using genetic algorithms. Scientific Programming, 14(3–4), 217–230.CrossRef
45.
Gu, J., Hu, J., Zhao, T., & Sun, G. (2012). A new resource scheduling strategy based on genetic algorithm in cloud computing environment. Journal of Computers, 7(1), 42–52.CrossRef
46.
Sawant, S. (2011). A genetic algorithm scheduling approach for virtual machine resources in a cloud computing environment.
47.
Xhafa Xhafa, F., Carretero Casado, J. S., & Abraham, A. (2007). Genetic algorithm based schedulers for grid computing systems. International Journal of Innovative Computing Information and Control, 3(5), 1053–1071.
48.
Liang, J. J., Qin, A. K., Suganthan, P. N., & Baskar, S. (2006). Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Transactions on Evolutionary Computation, 10(3), 281–295.CrossRef
49.
Guo, L., Zhao, S., Shen, S., & Jiang, C. (2012). Task scheduling optimization in cloud computing based on heuristic algorithm. Journal of Networks, 7(3), 547.CrossRef
50.
Zhang, L., Chen, Y., Sun, R., Jing, S., & Yang, B. (2008). A task scheduling algorithm based on PSO for grid computing. International Journal of Computational Intelligence Research, 4(1), 37–43.CrossRef
51.
Pandey, S., Wu, L., Guru, S. M., & Buyya, R. (2010). A particle swarm optimization-based heuristic for scheduling workflow applications in cloud computing environments. In 2010 24th IEEE international conference on advanced information networking and applications (pp. 400–407). IEEE.
52.
Singh, P., Dimri, P., Saroha, G. P., & Barthwal, V. (2016). A load balancing analysis of cloud base application with different service broker policies. International Journal of Computer Applications, 975, 8887.
53.
Zhao, C., Zhang, S., Liu, Q., Xie, J., & Hu, J. (2009). Independent tasks scheduling based on genetic algorithm in cloud computing. In 2009 5th international conference on wireless communications, networking and mobile computing (pp. 1–4). IEEE.
54.
Domanal, S. G., & Reddy, G. R. M. (2018). An efficient cost optimized scheduling for spot instances in heterogeneous cloud environment. Future Generation Computer Systems, 84, 11–21.CrossRef
55.
Ge, Y., & Wei, G. (2010). GA-based task scheduler for the cloud computing systems. In 2010 International conference on web information systems and mining (Vol. 2, pp. 181–186). IEEE.
56.
Rawat, P. S., & Gupta, P. (2019). Efficient utilization of iaas cloud using adaptive evolution based technique. Indian Journal of Science and Technology, 12, 677–691.
57.
Prasad, G. V., Prasad, A. S., & Rao, S. (2016). A combinatorial auction mechanism for multiple resource procurement in cloud computing. IEEE Transactions on Cloud Computing, 6(4), 904–914.MathSciNetCrossRef
58.
Calheiros, R. N., Ranjan, R., Beloglazov, A., De Rose, C. A., & Buyya, R. (2011). CloudSim: A toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and Experience, 41(1), 23–50.
59.
Wickremasinghe, B., Calheiros, R. N., & Buyya, R. (2010). Cloudanalyst: A cloudsim-based visual modeller for analysing cloud computing environments and applications. In 2010 24th IEEE international conference on advanced information networking and applications (pp. 446–452). IEEE.
Metadaten
Titel
Optimize Task Allocation in Cloud Environment Based on Big-Bang Big-Crunch
verfasst von
Pradeep Singh Rawat
Priti Dimri
Soumen Kanrar
Gyanendra Pal Saroha
Publikationsdatum
29.07.2020
Verlag
Springer US
Erschienen in
Wireless Personal Communications / Ausgabe 2/2020
Print ISSN: 0929-6212
Elektronische ISSN: 1572-834X
DOI
https://doi.org/10.1007/s11277-020-07651-1

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