Resource Elasticity Management using Fuzzy Controller Based on Threshold Changes in the Cloud Computing Environment

Document Type : Original Article

Authors

1 Department of computer engineering, Qom Branch, Islamic Azad University, Qom, Iran

2 Computer department, Faculty of Engineering, Islamic Azad University of Qom.

3 Information Security, Taali University

Abstract

Increasing the popularity and profitableness of cloud computing is dependent on providing the capabilities and features that the users desire. Elasticity is one of the strongest features that distinguish the cloud computing domain from other distributed system approaches. Cloud computing takes into account an unlimited capacity of the resources for the consumer, and the consumer can take the resources in demand based on competitive rates and increase or  decrease the number of resources. There have been many improvements to elasticity management by previous        researches. However, further researches are necessary to manage elasticity more efficiently. In this paper, an        approach for improving elasticity is presented using the fuzzy control system based on threshold changes for           high-performance computing applications in cloud computing. In the proposed approach, elasticity management is based on continuous monitoring and decision making. The results indicate that the proposed approach has a better performance in terms of response time, cost and service level agreement (SLA) violation, compared to previous      studies. In comparison with each of the two specified approaches, the response time of the proposed method has decreased by 6.5% and 9%, cost by 6% and 12%, and service level agreement (SLA) violation by 68% and 77.5%, respectively.
 

Keywords


[1]     M. Beltrán, “BECloud: A New Approach to Analyse Elasticity Enablers of Cloud Services,” Future Generation Computer Systems, vol. 64, pp. 39-49, 2016.##
[2]     Y. Tan, H. Nguyen, Z. Shen, X. Gu, C. Venkatramani, and D. Rajan, “Prepare: Predictive Performance Anomaly Prevention for Virtualized Cloud Systems,” In 2012 IEEE 32nd Int. Conf. on Distributed Computing Systems, pp. 285-294, 2012.##
[3]     E. Barrett, E. Howley, and J. Duggan, “Applying Reinforcement Learning Towards Automating Resource Allocation and Application Scalability in the Cloud,” Concurrency and Computation: Practice and Experience,vol. 25, pp. 1656-1674, 2013.##
[4]     L. R. Moore, K. Bean, and T. Ellahi, “Transforming reactive Auto-Scaling Into Proactive Auto-Scaling,” In Proc. of the 3rd Int. Workshop on Cloud Data and Platforms, pp. 7-12, 2013.##
[5]     P. D. Kaur and I. Chana, “A Resource Elasticity Framework for QoS-aware Execution of Cloud Applications,” Future Generation Computer Systems,vol. 37, pp. 14-25, 2014.##
[6]     E. B. Lakew, C. Klein, F. Hernandez-Rodriguez, and E. Elmroth, “Towards Faster Response Time Models for Vertical Elasticity,” In 2014 IEEE/ACM 7th Int. Con. on Utility and Cloud Computing, pp. 560-565, 2014.##
[7]     G. A. Moreno, J. Cámara, D. Garlan, and B. Schmerl, “Proactive Self-adaptation under Uncertainty: A Probabilistic Model Checking Approach,” In Proc. of the 2015 10th Joint Meeting on Foundations of Software Engineering, pp. 1-12, 2015.##
[8]     L. Baresi, S. Guinea, A. Leva, and G. Quattrocchi, “A Discrete-time Feedback Controller for Containerized Cloud Applications,” In Proc. of the 2016 24th ACM SIGSOFT Int. Symposium on Foundations of  Software Engineering, pp. 217-228, 2016.##
[9]     G. A. Moreno, J. Cámara, D. Garlan, and B. Schmerl, “Efficient Decision-making under Uncertainty for Proactive Self-adaptation,” In 2016 IEEE Int. Conf. on Autonomic Computing (ICAC), pp. 147-156, 2016.##
[10]  F. Paraiso, P. Merle, and L. Seinturier, “Socloud: A Service-Oriented Component-based PaaS for Managing Portability, Provisioning, Elasticity, and High Availability Across Multiple Clouds,” Computing,vol. 98, pp. 539-565, 2016.##
[11]  S. Lehrig, R. Sanders, G. Brataas, M. Cecowski, S. Ivanšek, and J. Polutnik, “CloudStore—towards Scalability, Elasticity, and Efficiency Benchmarking and Analysis in Cloud Computing,” Future Generation Computer Systems,vol. 78, pp. 115-126, 2018.##
[12]  A. Ashraf, B. Byholm, and I. Porres, “CRAMP: Cost-Efficient Resource Allocation for Multiple Web Applications with Proactive Scaling,” In 4th IEEE Int. Conf. on Cloud Computing Technology and Science Proc., pp. 581-586, 2012.##
[13]  A. Beloglazov and R. Buyya, “Adaptive Threshold-based Approach for Energy-efficient consolidation of Virtual Machines in Cloud Data Centers,” MGC@ Middleware,vol. 4, 2010.##
[14]  K. Li, “Quantitative Modeling and Analytical Calculation of Elasticity in Cloud Computing,” IEEE Transactions on Cloud Computing, 2017.##
[15]  M. C. Huebscher and J. A. McCann, “A Survey of Autonomic Computing—degrees, Models, and Applications,” ACM Computing Surveys (CSUR),vol. 40, p. 7, 2008.##
[16]  T. Lorido-Botran, J. Miguel-Alonso, and J. A. Lozano, “A Review of Auto-scaling Techniques for elastic Applications in Cloud Environments,” J. of Grid Computing, vol. 12, pp. 559-592, 2014.##
[17]  Y. Al-Dhuraibi, F. Paraiso, N. Djarallah, and P. Merle, “Elasticity in Cloud Computing: State of the Art and Research Challenges,” IEEE Transactions on Services Computing, vol. 11, pp. 430-447, 2017.##
[18]  S. Yi, D. Kondo, and A. Andrzejak, “Reducing Costs of Spot Instances via Checkpointing in the Amazon Elastic Compute Cloud,” In 2010 IEEE 3rd Int. Conf. on Cloud Computing, pp. 236-243, 2010.##
[19]  S. Wee, “Debunking Real-time Pricing in Cloud Computing,” In Proc. of the 2011 11th IEEE/ACM Int. Symposium on Cluster, Cloud and Grid Computing, pp. 585-590, 2011.##
[20]  A. Kivity, D. Laor, G. Costa, P. Enberg, N. Har’El, D. Marti, and V. Zolotarov, “OSv—optimizing the Operating System for Virtual Machines,” In 2014 {USENIX} Annual Technical Conference ({USENIX}{ATC} 14), pp. 61-72, 2014.##
[21]  Y. Al-Dhuraibi, F. Zalila, N. Djarallah, and P. Merle, “Coordinating Vertical Elasticity of both Containers and Virtual Machines,” 8th International Conference on Cloud Computing and Services Science, 2018.##
[22]  A. Shahidinejad, M. Ghobaei-Arani, and L. Esmaeili, “An Elastic Controller Using Colored Petri Nets in Cloud Computing Environment,” Cluster Computing,pp. 1-27, 2019.##
[23]  D. Villegas, N. Bobroff, I. Rodero, J. Delgado, Y. Liu, A. Devarakonda, L. Fong, M. sadjadi, and M. Parashar, “Cloud Federation in a Layered Service Model,” Journal of Computer and System Sciences,vol. 78, pp. 1330-1344, 2012.##
[24]  R. Khorsand, M. Ghobaei‐Arani, and M. Ramezanpour, “FAHP Approach for Autonomic Resource Provisioning of Multitier Applications in Cloud Computing Environments,” Software: Practice and Experience,vol. 48, pp. 2147-2173, 2018.##
[25]  R. da Rosa Righi, V. F. Rodrigues, C. A. Da Costa, G. Galante, L. C. E. De Bona, and T. Ferreto, “Autoelastic: Automatic Resource Elasticity for High Performance Applications in the Cloud,” IEEE Transactions on Cloud Computing,vol. 4, pp. 6-19, 2015.##
[26]  R. da Rosa Righi, V. F. Rodrigues, G. Rostirolla, C. A. da Costa, E. Roloff, and P. O. A. Navaux, “A Lightweight Plug-and-play Elasticity Service for Self-organizing Resource Provisioning on Parallel Applications,” Future Generation Computer Systems,vol. 78, pp. 176-190, 2018.##
[27]  V. F. Rodrigues, R. da Rosa Righi, G. Rostirolla, J. L. V. Barbosa, C. A. da Costa, A. M. Alberti, and V.Chang, “Towards Enabling Live Thresholding as Utility to Manage Elastic Master-Slave Applications in the Cloud,” Journal of Grid Computing,vol. 15, pp. 535-556, 2017.##
[28]  T. Bhardwaj and S. C. Sharma, “Fuzzy Logic-based Elasticity Controller for Autonomic Resource Provisioning in Parallel Scientific Applications: A Cloud Computing Perspective,” Computers & Electrical Engineering, vol. 70, pp. 1049-1073, 2018.##
[29]  V. Cardellini, T. G. Grbac, M. Nardelli, N. Tanković, and H.-L. Truong, “QoS-Based Elasticity for Service Chains in Distributed Edge Cloud Environments,” In Autonomous Control for a Reliable Internet of Services, ed: Springer, Cham, pp. 182-211, 2018.##
[30]  N. Chohan, C. Castillo, M. Spreitzer, M. Steinder, A. N. Tantawi, and C. Krintz, “See Spot Run: Using Spot Instances for Mapreduce Workflows,” HotCloud,vol. 10, pp. 7-7, 2010.##
[31]  M. Mattess, C. Vecchiola, and R. Buyya, “Managing Peak Loads by Leasing Cloud Infrastructure Services from a Spot Market,” In 2010 IEEE 12th Int. Conf. on High Performance Computing and Communications (HPCC), pp. 180-188, 2010.##
[32]  A. Andrzejak, D. Kondo, and S. Yi, “Decision Model for Cloud Computing under SLA Constraints,” In 2010 IEEE Int. Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems, pp. 257-266, 2010.##
[33]  E. Folkerts, A. Alexandrov, K. Sachs, A. Iosup, V. Markl, and C. Tosun, “Benchmarking in the Cloud: What it Should, Can, and Cannot Be,” In Technology Conf. on Performance Evaluation and Benchmarking, pp. 173-188, 2012.##
[34]  S. Islam, K. Lee, A. Fekete, and A. Liu, “How a Consumer Can Measure Elasticity for Cloud Platforms,” In Proceedings of the 3rd ACM/SPEC International Conference on Performance Engineering, pp. 85-96, 2012.##
[35]  A. A. Rahmanian, M. Ghobaei-Arani, and S. Tofighy, “A Learning Automata-based Ensemble Resource Usage Prediction Algorithm for Cloud Computing Environment,” Future Generation Computer Systems,vol. 79, pp. 54-71, 2018.##
[36]  H.-J. Zimmermann, “Fuzzy Set Theory—and itsApplications,” Springer Science & Business Media, 2011.##
[37]  R. Bouabdallah, S. Lajmi, and K. Ghedira, “Use of Reactive and Proactive Elasticity to Adjust Resources Provisioning in the Cloud Provider,” In 2016 IEEE 18th Int. Conf. on High Performance Computing and Communications; IEEE 14th Int. Conf. on Smart City; IEEE 2nd Int. Conf. on Data Science and Systems (HPCC/SmartCity/DSS), pp. 1155-1162, 2016.##
[38]  C. Reiss, J. Wilkes, and J. L. Hellerstein, “Google Cluster-usage Traces: Format+ Schema,” Google Inc., White Paper,pp. 1-14, 2011.##
Volume 8, Issue 3 - Serial Number 31
November 2020
Pages 63-81
  • Receive Date: 17 September 2019
  • Revise Date: 13 January 2020
  • Accept Date: 01 February 2020
  • Publish Date: 22 October 2020