Kiểm soát đầu vào để lập lịch cho các yêu cầu người dùng trên tính toán đám mây dựa vào ràng buộc QoS
Abstract
The problem of admission control to schedule for user requirements is NP-complete [1] in cloud computing environment. To solve this problem it is usually to put building heuristic algorithms to form a simple algorithm with complex polynomial. In this paper, we propose an algorithm of admission control and a scheduling algorithm for user requirements based on the use of ACO algorithm (Ant Colony Optimization) and take advantage of validity period between the requirements so that the total cost of the system is minimal but still satisfying QoS (Quality of Service) constraints for the requirements. Two algorithms are set up and run a complete test on CloudSim. The experimental results show the effectiveness and superiority of the proposed algorithm in comparing with sequential and EDF (Earliest Deadline First) algorithms.References
P. Brucker, Scheduling Algorithms, Fifth Edition, Springer Press, 2007.
http://www.microsoft.com/window
http://www.ibm.com/ibm/cloud/ibm_cloud/
Buyya, R., Ranjan, Modeling and Simulation of Scalable Cloud Computing Environments and the CloudSim Toolkit: Challenges and Opportunities, Proceedings of the 7th High Performance Computing and Simulation Conference, Leipzig, Germany, 2009.
Marco Dorigo and Thomas Stützle, Ant Colony Optimization, A Bradford Book, The MIT Press Cambridge, Massachusetts,London, England, 2004.
Thomas Stützle, Marco Dorigo: A Short Convergence Proof for a Class of Ant Colony Optimization Algorithms, IEEE transactions on evolutionary computation, Vol. 6, No. 4, August 2002.
Kun Li, Gaochao Xu, Guangyu Zhao, Yushuang Dong, Dan Wang, Cloud Task scheduling based on Load Balancing Ant Colony Optimization, Sixth Annual ChinaGrid Conference, 2011.
Jzau-Sheng Lin and Shou-Hung Wu, Fuzzy Artificial Bee Colony System with Cooling Schedule for the Segmentation of Medical Images by Using of Spatial Information, Research Journal of Applied Sciences, Engineering and Technology 4, 2973-2980, 2012
A. Burns, R.I. Davis, P. Wang and F. Zhang, Partitioned EDF Scheduling for Multiprocessors using a C=D Scheme. Department of Computer Science, University of York, UK.
Lukasz Kruk, John Lehoczky, Kavita Ramanan And Steven Shreve, Heavy traffic analysis for EDF queues with reneging, The Annals of Applied Probability. Vol. 21, No. 2, 484–545, 2011.
Jianguang Deng, Yuelong Zhao, Huaqiang Yuan, A Service Revenue-oriented Task Scheduling Model of Cloud Computing, Journal of Information & Computational Science, July 1, 2013.
Mao, Ming and Li, Jie, Cloud auto-scaling with deadline and budget constraints, Grid Computing, 11th IEEE/ACM International Conference , 2010.
K. H. Kim et al, Power-aware provisioning of cloud resources for real-time services. In International Workshop on Middleware for Grids, Clouds and e-Science , pages 1–6, 2009.
Ramkumar N, Nivethitha S, Efficient Resource Utilization Algorithm (ERUA) for Service Request Scheduling in Cloud, International Journal of Engineering and Technology (IJET), Vol 5 No 2 Apr-May 2013.
Swarupa Irugurala, Dr.K.Shahu Chatrapati, Various Scheduling Algorithms for Resource Allocation In Cloud Computing, The International Journal Of Engineering And Science (IJES), page 16-24, 2013.
Kousalya.K, Balasubramanie.P: An Enhanced Ant Algorithm for Grid Scheduling Problem, IJCSNS International Journal of Computer Science and Network Security, VOL.8 No.4, April 2008.