Review of the quality of service scheduling mechanisms in cloud

  • Authors

    • k R RemeshBabu Government Engineering College Idukki, Kerala, India
    • Philip Samuel CUSAT
    2018-08-10
    https://doi.org/10.14419/ijet.v7i3.13016
  • Cloud Computing, Load Balancing, QoS, Resource Allocation, Task Scheduling, Service Level Agreement.

  • Cloud computing provides on demand access to a large pool of heterogeneous computational and storage resources to users over the internet. Optimal scheduling mechanisms are needed for the efficient management of these heterogeneous resources. The optimal scheduler can improve the Quality of Services (QoS) as well as maintaining efficiency and fairness among these tasks. In large scale distributed systems, the performance of these scheduling algorithms is crucial for better efficiency. Now the cloud customers are charged based upon the amount of resources they are consumed or held in reserve. Comparing these scheduling algorithms from different perspectives is needed for further improvement. This paper provides a comparative study about different resource allocation, load balancing and virtual machine consolidation algorithms in cloud computing. These algorithms have been evaluated in terms of their ability to provide QoS for the tasks and Service Level Agreement (SLA) guarantee amongst the jobs served. This study identifies current and future research directions in this area for QoS enabled cloud scheduling.

     

     

  • References

    1. [1] Yi Yao, Jiayin Wang, Bo Sheng, Chiu C. Tan, and Ningfang Mi, “Self-Adjusting Slot Configurations for Homogeneous and Heterogeneous Hadoop Clustersâ€, IEEE Transactions on Cloud Computing, Vol. 5, No. 2, pp.344-357, April-June 2017.https://doi.org/10.1109/TCC.2015.2415802.

      [2] Yadaiah Balagoni, Rajeswara Rao, “A Cost-effective SLA-Aware Scheduling for Hybrid Cloud Environmentâ€, IEEE International Conference on Computational Intelligence and Computing Research,15-17, Dec. 2016, Chennai.https://doi.org/10.1109/ICCIC.2016.7919621.

      [3] Kwang Mong Sim, “Agent-based Approaches for Intelligent Intercloud Resource Allocationâ€, IEEE Transactions on Cloud Computing, Volume: PP, Issue: 99.

      [4] A Mukherjee, Debashis Deand DG Roy, “A Power and Latency Aware Cloudlet Selection Strategy for Multi-Cloudlet Environmentâ€, Volume: PP, Issue: 99, IEEE Transactions on Cloud Computing.

      [5] XQiu, Y Dai, Y Xiang, and L Xing, “Correlation Modeling and Resource Optimization for Cloud Service with Fault Recoveryâ€, Volume: PP, Issue: 99, IEEE Transactions on Cloud Computing.

      [6] Xiaolin Chang, Ruofan Xia, Jogesh K. Muppala, Kishor S. Trivedi, Jiqiang Liu, “Effective Modeling Approach for IaaS Data Center Performance Analysis under Heterogeneous Workloadâ€, Volume: PP, Issue: 99, IEEE Transactions on Cloud Computing.

      [7] Haiying Shen, “RIAL: Resource Intensity Aware Load Balancing in Cloudsâ€, Volume: PP, Issue: 99, IEEE Transactions on Cloud Computing.

      [8] Binglai Niu, Yong Zhou, Hamed Shah-Mansouri, and Vincent W. S. Wong, “A Dynamic Resource Sharing Mechanism for Cloud Radio Access Networksâ€, IEEE Transactions on Wireless Communications, Vol. 15, No. 12, pp. 8325 – 8338, December 2016.

      [9] Ravi Akella, SaptarshiDebroy, Prasad Calyam, Alex Berryman, Kunpeng Zhu, Mukundan Sridharan, “Security Middle ground for Resource Protection in Measurement Infrastructure-as-a-Serviceâ€, Volume: PP, Issue: 99, IEEE Transactions on Services Computing.

      [10] Cong Wang, Kui Ren, and Jia Wang, “Secure and Practical Outsourcing of Linear Programming in Cloud Computingâ€, INFOCOM, 2011 Proceedings IEEE, 10-15 April 2011, Shanghai, China.

      [11] Ali Pahlevan, Xiaoyu Qu, Marina Zapater, David Atienza, “Integrating Heuristic and Machine-Learning Methods for Efficient Virtual Machine Allocation in Data Centersâ€, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.

      [12] Cong Wang, Kui Ren, and Jia Wang, “Secure Optimization Computation Outsourcing in Cloud Computing: A Case Study of Linear Programmingâ€, IEEE Transactions on Computers, Volume 65, Issue: 1, pp.216 – 229, Jan. 1 2016. https://doi.org/10.1109/TC.2015.2417542.

      [13] Sonia Yassa, Rachid Chelouah, Hubert Kadima and Bertrand Granado, “Multi-Objective Approach for Energy-Aware Workflow Scheduling in Cloud Computing Environmentsâ€, The ScientificWorld Journal Volume 2013, Article ID 350934, 13 pages, https://doi.org/10.1155/2013/350934.

      [14] R.K.Jena, "Multi Objective Task Scheduling in Cloud Environment Using Nested PSO Framework", Procedia Computer Science, Vol. 57, 2015, pp. 1219-1227.https://doi.org/10.1016/j.procs.2015.07.419.

      [15] Orachun Udomkasemsub, Li Xiaorong, Tiranee Achalakul, “A Multiple-Objective Workflow Scheduling Framework for Cloud Data Analyticsâ€, 2012 Ninth International Joint Conference on Computer Science and Software Engineering (JCSSE) 978-1-4673-1921-8/12.

      [16] Danlami Gabi, Abdul Samad Ismail, Anazida Zainal, Zalmiyah Zakaria, “Scalability-aware Scheduling Optimization Algorithm for Multi-Objective Cloud Task Scheduling Problemâ€, 2017 6th ICT International Student Project Conference (ICT-ISPC), https://doi.org/10.1109/ICT-ISPC.2017.8075304.

      [17] K. Muralitharan, R.Sakthivel, Y.Shi, “Multiobjective optimization technique for demand side management with load balancing approach in smart gridâ€, Journal of Neurocomputing 177(2016) 110–119. https://doi.org/10.1016/j.neucom.2015.11.015.

      [18] Heyang Xu, Bo Yang, Weiwei Qi and Emmanuel Ahene, “A Multi-objective Optimization Approach to Workflow Scheduling in Clouds Considering Fault Recoveryâ€, KSII Transactions on Internet and Information Systems Vol. 10, No. 3, pp. 976-995, Mar. 2016.

      [19] Bahman Keshanchi and Nima Jafari Navimipour, “Priority-Based Task scheduling in the Cloud Systems Using a Memetic Algorithmâ€, Journal of Circuits, Systems, and Computers Vol. 25, No. 10 (2016), World Scientific Publishing Company https://doi.org/10.1142/S021812661650119X.

      [20] P. K. Suri, Sunita Rani, “Simulator for Priority based Scheduling of Resources in Cloud Computingâ€, International Journal of Computer Applications,Volume 146 – No.14, pp.10-15, July 2016.

      [21] D. I. George Amalarethinam, S Kavitha, “Priority based Performance Improved Algorithm for Meta-task Scheduling in Cloud environment, IEEE 2nd Second International Conference on Computing and Communications Technologies (ICCCT’17) 2017.

      [22] Hao Wu, Xiayu Hua, Zheng Li, and Shangping Ren, “Resource and Instance Hour Minimization for Deadline Constrained DAG Applications Using Computer Cloudsâ€, IEEE Transactions on Parallel and Distributed Systems, Vol. 27, No. 3, pp. 885–899, March 2016.https://doi.org/10.1109/TPDS.2015.2411257.

      [23] Kanchana Viriyapant, Sucha Smanchat, “A Deadline-constrained Scheduling for Dynamic Multi-instances Parameter Sweep Workflowâ€, 15th International Conference on Computer and Information Science (ICIS), IEEE/ACIS, June 26-29, 2016, Okayama, Japan.

      [24] Xiaoping Li, Lihua Qian, and Rub´en Ruiz, “Cloud workflow scheduling with deadlines and time slot availabilityâ€, IEEE Transactions on Services Computing, Volume: PP, Issue: 99.

      [25] Mohamed Mohamed, Mourad Amziani, Djamel Belaïd, Samir Tata, Tarek Melliti, “An autonomic approach to manage elasticity of business processes in the Cloudâ€, Future Generation Computer Systems 50 (2015) 49–61.https://doi.org/10.1016/j.future.2014.10.017.

      [26] Jiali You, Nannan Qiao, Jinlin Wang, Guoqiang Zhang,Yiqiang Sheng, Haojiang Deng, Xue Liu, “An On-Site Elastic Autonomous Service Network with Efficient Task Assignmentâ€, 2016 IEEE 41st Conference on Local Computer Networks Workshops.

      [27] Xiaomin Zhu, Ji Wang, Hui Guo, Dakai Zhu, Laurence T. Yang and Ling Liu, “Fault Tolerant Scheduling for Real-Time Scientific Workflows with Elastic Resource Provisioning in Virtualized Cloudsâ€, IEEE Transactions on Parallel and Distributed Systems, Vol. 27, No. 12, December 2016, pp.3501-3517.https://doi.org/10.1109/TPDS.2016.2543731.

      [28] Yunliang Chen, Lizhe Wang, Xiaodao Chen, Rajiv Ranjan, Albert Y, Zomaya, Yuchen Zhou and Shiyan Hu, “Stochastic Workload Scheduling for Uncoordinated Datacenter Clouds with Multiple QoS Constraintsâ€, IEEE Transactions on Cloud Computing, Volume: PP, Issue: 99.

      [29] Nikos Tziritas, Samee U. Khan, Thanasis Loukopoulos, Spyros Lalis, Cheng-Zhong Xu, Keqin Li, Albert Y. Zomaya, “Online Inter-Datacenter Service Migrationsâ€, IEEE Transactions on Cloud Computing, Volume: PP, Issue: 99.

      [30] Fahimeh Ramezani, Jie Lu, Farookh Khadeer Hussain, “Task-Based System Load Balancing in Cloud Computing Using Particle Swarm Optimizationâ€, International Journal of Parallel Programming, Volume 42 Issue 5, October 2014, pp. 739-754.https://doi.org/10.1007/s10766-013-0275-4.

      [31] Hsu-Yang Kung, Ting-HuanKuo, Chi-Hua Chen, Yu-Lun Hsu, "Two-stage cloud service optimisation model for cloud service middleware platform", The Journal of Engineering, Vol. 2018, Iss. 3, pp. 155–161.

      [32] C Saravanakumar, C.Arun, “Efficient Idle Virtual Machine Management for Heterogeneous Cloud using Common Deployment Modelâ€, KSII Transactions on Internet and Information Systems Vol. 10, No. 4, Apr. 2016.

      [33] Aissan Dalvandi, Mohan Gurusamy and Kee Chaing Chua, “Application Scheduling, Placement, and Routing for Power Efficiency in Cloud Data Centersâ€, IEEE Transactions on Parallel and Distributed Systems, Volume: 28, Issue: 4, April 1, 2017, https://doi.org/10.1109/TPDS.2016.2607743.

      [34] Shangguang Wang, Zhipiao Liu, Zibin Zheng, Qibo Sun, Fangchun Yang, “Particle Swarm Optimization for Energy-Aware Virtual Machine Placement Optimization in Virtualized Data Centersâ€, 19th IEEE International Conference on Parallel and Distributed Systems, 2013. https://doi.org/10.1109/ICPADS.2013.26.

      [35] Konstantinos Tsakalozos, Vasilis Verroios, Mema Roussopoulos, and Alex Delis, “Live VM Migration under Time-Constraints in Share-Nothing IaaS-Cloudsâ€, IEEE Transactions on Parallel and Distributed Systems, Vol. 28, No. 8, August 2017.

      [36] Weiwei Kong, Yang Lei, Jing Ma, “Virtual machine resource scheduling algorithm for cloud computing based on auction mechanismâ€, International Journal Optik 127 (2016) 5099–5104, Elsevier. https://doi.org/10.1016/j.ijleo.2016.02.061.

      [37] K R Remesh Babu, Philip Samuel, “Virtual Machine Placement for Improved Quality in IaaS Cloudâ€, 2014 IEEE Fourth International Conference on Advances in Computing and Communications, pp.190-194.

      [38] Zhifeng Zhong, Kun Chen, Xiaojun Zhai, and Shuange Zhou, “Virtual Machine-Based Task Scheduling Algorithm in a Cloud Computing Environmentâ€, Tsinghua Science and Technology ISSN, pp.660-667, Volume 21, Number 6, December 2016.https://doi.org/10.1109/TST.2016.7787008.

      [39] Seyed Ebrahim Dashti, Amir Masoud Rahmani, "Dynamic VMs placement for energy efficiency by PSO in cloud computing", Journal of Experimental & Theoretical Artificial Intelligence, Volume 28, 2016 - Issue 1-2: Advances and Applications of Swarm Intelligence, pp.97-112.

      [40] Shaobin Zhan, Hongying Huo, "Improved PSO-based Task Scheduling Algorithm in Cloud Computing", Journal of Information & Computational Science 9: 13 (2012) 3821–3829.

      [41] Jianen Yan, Hongli Zhang, Haiyan Xu, Zhaoxin Zhang, "Discrete PSO-based workload optimization in virtual machine placement", PersUbiquitComput (2018) 22: 589. https://doi.org/10.1007/s00779-018-1111-z.

      [42] Bin Xiang, Bibo Zhang, and Lin Zhang, “Greedy-Ant: Ant Colony System-Inspired Workflow Scheduling for Heterogeneous Computingâ€, IEEE Access, Volume. 5, pp.11404-11412.

      [43] Anton Beloglazov, Jemal Abawajy, Rajkumar Buyya, “Energy-aware resource allocation heuristics for efficient management of data centers for Cloud computingâ€, Future Generation Computer Systems 28 (2012) 755–768. https://doi.org/10.1016/j.future.2011.04.017.

      [44] Ali Al Buhussain, Robson E. De Grande, Azzedine Boukerche, “Elasticity Based Scheduling Heuristic Algorithm for Cloud Environmentsâ€, 2016 IEEE/ACM 20th International Symposium on Distributed Simulation and Real Time Applications. https://doi.org/10.1109/DS-RT.2016.34.

      [45] Yacine Kessaci, Nouredine Melab, El-Ghazali Talbi, "A multi-start local search heuristic for an energy efficient VMs assignment on top of the OpenNebula cloud manager", Future Generation Computer Systems, Volume 36, July 2014, pp, 237-256.

      [46] Jia Zhao, Kun Yang, Xiaohui Wei, Yan Ding, Liang Hu, and Gaochao Xu, “A Heuristic Clustering-Based Task Deployment Approach for Load Balancing Using Bayes Theorem in Cloud Environmentâ€, IEEE Transactions on Parallel and Distributed Systems, Vol. 27, No. 2, February 2016.https://doi.org/10.1109/TPDS.2015.2402655.

      [47] Shengjun Xue, Wenling Shi, Xiaolong Xu, "A Heuristic Scheduling Algorithm based on PSO in the Cloud Computing Environment", International Journal of u- and e- Service, Science and Technology, Vol.9, No. 1 (2016), pp.349-362.

      [48] Syed Hamid Hussain Madni, Muhammad ShafieAbd Latiff, Mohammed Abdullahi, Shafi'i Muhammad Abdulhamid, Mohammed Joda Usman, “Performance comparison of heuristic algorithms for task scheduling in IaaS cloud computing environmentâ€, PLOS ONE | https://doi.org/10.1371/journal.pone.0176321.

      [49] Zhicheng Cai, Xiaoping Li, and Jatinder N.D. Gupta, “Heuristics for Provisioning Services to Workflows in XaaS Cloudsâ€, IEEE Transactions on Services Computing, Vol. 9, No. 2, March/April 2016.

      [50] Mohammad Masdari, Farbod Salehi1, Marzie Jalali1, Moazam Bidaki, “A Survey of PSO-Based Scheduling Algorithms in Cloud Computingâ€, J NetwSyst Manage, Springer 2016, https://doi.org/10.1007/s10922-016-9385-9.

      [51] Mala Kalra, Sarbjeet Singh, “A review of metaheuristic scheduling techniques in cloud computingâ€, Egyptian Informatics Journal (2015) 16, 275–295.https://doi.org/10.1016/j.eij.2015.07.001

      [52] Chun-Wei Tsai, Wei-Cheng Huang, Meng-Hsiu Chiang, Ming-Chao Chiang, and Chu-Sing Yang, “A Hyper-Heuristic Scheduling Algorithm for Cloudâ€, IEEE Transactions on Cloud Computing, Vol. 2, No. 2, April-June 2014.

      [53] Asmae Benali, Bouchra El Asri and Houda Kriouile, “A Pareto-based Artificial Bee Colony and Product Line for Optimizing Scheduling of VM on Cloud Computingâ€, 2015 International Conference on Cloud Technologies and Applications (CloudTech).

      [54] Kriti Agrawal, Priyanka Tripathi, “Power aware Artificial Bee Colony Virtual Machine Allocation for Private Cloud Systemsâ€, 2015 International Conference on Computational Intelligence and Communication Networks.

      [55] Elaheh Hallaj, Seyyed Reza Kamel Tabbakh, “Study and Analysis of Task Scheduling Algorithms in Clouds Based on Artificial Bee Colonyâ€, Second International Congress on Technology, Communication and Knowledge (ICTCK 2015) November, 11-12, 2015 - Mashhad Branch, Islamic Azad University, Mashhad, Iran.https://doi.org/10.1109/ICTCK.2015.7582644.

      [56] Warangkhana Kimpan, Boonhatai Kruekaew, “Heuristic Task Scheduling with Artificial Bee Colony Algorithm for Virtual Machinesâ€, Joint 8th International Conference on Soft Computing and Intelligent Systems and 17th International Symposium on Advanced Intelligent Systems, 2016.https://doi.org/10.1109/SCIS-ISIS.2016.0067.

      [57] Tawfeek MA, El-Sisi A, Keshk AE, Torkey FA, “Cloud task scheduling based on ant colony optimizationâ€, In: 8thintconf computing syst; 2013. p. 64–9. http://dx.doi.org/10.1109/ ICCES.2013.6707172.

      [58] Pacini E, Mateos C, Garcı´a C, “Balancing throughput and response time in online scientific clouds via ant colony optimizationâ€, AdvEng Software 2015; 84:31–47, Elsevier.https://doi.org/10.1016/j.advengsoft.2015.01.005.

      [59] Li K, Xu G, Zhao G, Dong Y, Wang D, “Cloud task scheduling based on load balancing ant colony optimizationâ€, Sixth AnnuChinagridConf 2011;2011:3–9. http://dx.doi.org/10.1109/ ChinaGrid.2011.17.

      [60] Liu X, Zhan Z, Du K, Chen W, “Energy aware virtual machine placement scheduling in cloud computing based on ant colony optimizationâ€, GECCO '14, Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation, pp. 41-48. https://doi.org/10.1145/2576768.2598265.

      [61] Ferdaus MH, Murshed M, Calheiros RN, Buyya R, “Virtual machine consolidation in cloud data centers using ACO metaheuristicâ€, In: Euro-Par 2014 parallel process. Springer; 2014. p. 306–17. https://doi.org/10.1007/978-3-319-09873-9.

      [62] Y. Gao, H. Guan, Z. Qi, Y. Hou, and L. Liu, “A multi-objective ant colony system algorithm for virtual machine placement in cloud computingâ€, J. Comput. Syst. Sci., vol. 79, no. 8, pp. 1230–1242, 2013.https://doi.org/10.1016/j.jcss.2013.02.004.

      [63] Quanwang Wu, Fuyuki Ishikawa, Qingsheng Zhu, Yunni Xia, Junhao Wen, “Deadline-constrained Cost Optimization Approaches for Workflow Scheduling in Cloudsâ€, IEEE Transactions on Parallel and Distributed Systems, Volume: PP, Issue: 99, 03 August 2017.

      [64] Ashish Gupta, Ritu Garg, “Load Balancing Based Task Scheduling with ACO in Cloud Computingâ€, 2017 IEEE International Conference on Computer an Applications (ICCA), https://doi.org/10.1109/COMAPP.2017.8079781.

      [65] Shengxiang Yang, and Sadaf Naseem Jat, “Genetic Algorithms With Guided and Local Search Strategies for University Course Timetablingâ€, IEEE Transactions on Systems, Man, and Cybernetics—Part C: Applications and Reviews, Vol. 41, No. 1, January 2011.https://doi.org/10.1109/TSMCC.2010.2049200.

      [66] Yonghua Xiong, Suzhen Huang, Min Wu, Jinhua She, and Keyuan Jiang, “A Johnson’s-Rule-Based Genetic Algorithm for Two-Stage-Task Scheduling Problem in Data-Centers of Cloud Computingâ€, IEEE Transactions on Cloud Computing.

      [67] An-ping Xiong and Chun-xiang Xu, “Energy Efficient Multiresource Allocation of Virtual Machine Based on PSO in Cloud Data Center,†Mathematical Problems in Engineering, vol. 2014, Article ID 816518, 8 pages, 2014.

      [68] Solmaz Abdi, Seyyed Ahmad Motamedi, and Saeed Sharifian, “Task Scheduling using Modified PSO Algorithm in Cloud Computing Environmentâ€, International Conference on Machine Learning, Electrical and Mechanical Engineering (ICMLEME'2014) Jan. 8-9, 2014 Dubai (UAE).

      [69] Entisar S. Alkayal, Nicholas R. Jennings, Maysoon F. Abulkhair, “Efficient Task Scheduling Multi-Objective Particle Swarm Optimization in Cloud Computingâ€, IEEE 41st Conference on Local Computer Networks Workshops, 2016.

      [70] Dinesh Kumar, Zahid Raza, “A PSO based VM Resource Scheduling Model for Cloud Computingâ€, IEEE International Conference on Computational Intelligence & Communication Technology, 2015.https://doi.org/10.1109/CICT.2015.35.

      [71] Liu Z, Wang X, “A PSO-based algorithm for load balancing in virtual machines of cloud computing environmentâ€, Lect Notes ComputSci (including SubserLect Notes ArtifIntellLect Notes Bioinformatics) 2012; 7331 LNCS: 142–7.https://doi.org/10.1007/978-3-642-30976-2_17.

      [72] Shahrzad Aslanzadeh, Zenon Chaczko, "Load balancing optimization in cloud computing: Applying Endocrine-particale swarm optimization", IEEE International Conference on Electro/Information Technology (EIT), Dekalb, IL, USA, 2015.

      [73] Juan J, Durillo, Vlad Nae, Radu Prodan, “Multi-objective energy-efficient workflow scheduling using list-based heuristicsâ€, Future Generation Computer Systems, July 2014, Vol.36, pp. 221-236.https://doi.org/10.1016/j.future.2013.07.005.

      [74] Zhao, J., Hu, L., Ding, Y., Xu, G., & Hu, M., "A Heuristic Placement Selection of Live Virtual Machine Migration for Energy-Saving in Cloud Computing Environment", PLoS ONE, 9(9), e108275, 2014. https://doi.org/10.1371/journal.pone.0108275.

      [75] Haitao Yuan, Jing Bi, Wei Tan, Meng Chu Zhou, Bo Hu Li, and Jianqiang Li, “TTSA: An Effective Scheduling Approach for Delay Bounded Tasks in Hybrid Cloudsâ€, IEEE Transactions on Cybernetics, Vol. 47, No. 11, November 2017.https://doi.org/10.1109/TCYB.2016.2574766.

      [76] Gamal F. Elhady and Medhat A. Tawfeek, “A Comparative Study into Swarm Intelligence Algorithms for Dynamic Tasks Scheduling in Cloud Computingâ€, 2015 IEEE Seventh International Conference on Intelligent Computing and Information Systems (ICICIS'15).https://doi.org/10.1109/IntelCIS.2015.7397246.

      [77] Danlami Gabi, Abdul Samad Ismail, “Cloud Scalable Multi-Objective Task Scheduling Algorithm for Cloud Computing Using Cat Swarm Optimization and Simulated Annealingâ€, 2017 8th International Conference on Information Technology (ICIT).

      [78] Keng-Mao Cho, Pang-Wei Tsai, Chun-Wei Tsai, Chu-Sing Yang, “A hybrid meta-heuristic algorithm for VM scheduling with load balancing in cloud computingâ€, Neural Computing and Applications, Volume 26 Issue 6, August 2015, pp.1297-1309.https://doi.org/10.1007/s00521-014-1804-9.

      [79] Wen X, Huang M, Shi J, “Study on resources scheduling based on ACO algorithm and PSO algorithm in cloud computingâ€, 11th International Symposium on Distributed Computing and Applications to Business, Engineering & Science, 2012, pp. 219–222.

      [80] Kanwarpreet Kaur, Amardeep Kaur, “A hybrid approach of load balancing through VMs using ACO, Min Max and genetic algorithmâ€, IEEE International Conference on October 2016, https://doi.org/10.1109/NGCT.2016.7877486.

      [81] Sheng-Jun Xue, Wu Wu, “Scheduling Workflow in Cloud Computing Based on Hybrid Particle Swarm Algorithmâ€, TELKOMNIKA, Vol.10, No.7, November 2012, pp. 1560-1566.https://doi.org/10.11591/telkomnika.v10i7.1452.

      [82] Ali Al-maamari, Fatma A. Omara, “Task Scheduling using Hybrid Algorithm in Cloud Computing Environmentsâ€, IOSR Journal of Computer Engineering (IOSR-JCE), Volume 17, Issue 3, Ver. VI (May – Jun. 2015), pp. 96-106.

      [83] Huandong Wang, Yong Li, Ying Zhang, Depeng Jin, “Virtual Machine Migration Planning in Software-Defined Networksâ€, IEEE Transactions on Cloud Computing.

      [84] Walter Cerroni, and Flavio Esposito, “Optimizing Live Migration of Multiple Virtual Machinesâ€, IEEE Transactions on Cloud Computing.

      [85] Bhaskar Prasad Rimal, and Martin Maier, “Workflow Scheduling in Multi-Tenant Cloud Computing Environmentsâ€, IEEE Transactions on Parallel and Distributed Systems, Vol. 28, No. 1, January 2017.https://doi.org/10.1109/TPDS.2016.2556668.

      [86] Hamed Shah-Mansouri, Vincent W. S. Wong, and Robert Schober, “Joint Optimal Pricing and Task Scheduling in Mobile Cloud Computing Systemsâ€, IEEE Transactions on Wireless Communications, Vol. 16, No. 8, August 2017.https://doi.org/10.1109/TWC.2017.2707084.

      [87] Simon S. Woo, Jelena Mirkovic, “Optimal application allocation on multiple public cloudsâ€, International Journal Computer Networks 68 (2014) 138–148.

      [88] Haitao Yuan, Jing Bi, Wei Tan, and Bo Hu Li, “Temporal Task Scheduling With Constrained Service Delay for Profit Maximization in Hybrid Cloudsâ€, IEEE Transactions on Automation Science and Engineering, Vol. 14, No. 1, January 2017.https://doi.org/10.1109/TASE.2016.2526781.

      [89] Jincy Joseph, K.R. Remesh Babu, “Scheduling to Minimize Context Switches for Reduced Power Consumption and Delay in the Cloudâ€, 2016 International Conference on Micro-Electronics and Telecommunication Engineering. https://doi.org/10.1109/ICMETE.2016.106.

      [90] Xianling Meng, Wei Wang, and Zhaoyang Zhang, “Delay-Constrained Hybrid Computation Offloading with Cloud and Fog Computingâ€, IEEE Access, Volume 5, pp.21355-21367, September 2017. https://doi.org/10.1109/ACCESS.2017.2748140.

      [91] Songyun Wang, Zhuzhong Qian, Jiabin Yuan, and Ilsun You, “A DVFS Based Energy-Efficient Tasks Scheduling in a Data Centerâ€, IEEE Access, Volume: 5, pp.13090 - 13102, July 2017.

      [92] Yibin Li, Min Chen, Wenyun Dai, and Meikang Qiu, “Energy Optimization With Dynamic Task Scheduling Mobile Cloud Computingâ€, IEEE Systems Journal, Vol. 11, No. 1, March 2017.https://doi.org/10.1109/JSYST.2015.2442994.

      [93] Hancong Duan, Chao Chen, Geyong Min, Yu Wu, “Energy-Aware Scheduling of Virtual Machines in Heterogeneous Cloud Computing Systemsâ€, Future Generation Computer Systems (2016), Volume 74, September 2017, pp. 142-150.

      [94] Li Shi, Zhemin Zhang, and Thomas Robertazzi, “Energy-Aware Scheduling of Embarrassingly Parallel Jobs and Resource Allocation in Cloudâ€, IEEE Transactions on Parallel and Distributed Systems, Vol. 28, No. 6, June 2017.https://doi.org/10.1109/TPDS.2016.2625254.

      [95] Weiwen Zhang and Yonggang Wen, “Energy-efficient Task Execution for Application as a General Topology in Mobile Cloud Computingâ€, IEEE Transactions on Cloud Computing.

      [96] Yaser Mansouri, Adel Nadjaran Toosi, and Rajkumar Buyya, “Cost Optimization for Dynamic Replication and Migration of Data in Cloud Data Centersâ€, IEEE Transactions on Cloud Computing.

      [97] Moussa Ehsan, Karthiek Chandrasekaran, Yao Chen, Radu Sion, “Cost-Efficient Tasks and Data Co-Scheduling with Afford Hadoopâ€, IEEE Transactions on Cloud Computing.

      [98] Keke Gai, Meikang Qiu, Hui Zhao, “Cost-Aware Multimedia Data Allocation for Heterogeneous Memory Using Genetic Algorithm in Cloud Computingâ€, IEEE Transactions on Cloud Computing.

      [99] Sowmya Karunakaran and Rangaraja P. Sundarraj, “Bidding Strategies for Spot Instances in Cloud Computing Marketsâ€, IEEE Internet Computing, Volume: 19, Issue: 3, May-June 2015, pp.32 - 40.https://doi.org/10.1109/MIC.2014.87.

      [100] Liang Zheng, Carlee Joe-Wong, Chee Wei Tan, Mung Chiang, Xinyu Wang, “How to Bid the Cloudâ€, SIGCOMM '15, Proceedings of the 2015 ACM Conference on Special Interest Group on Data Communication, pp.71-84.https://doi.org/10.1145/2785956.2787473.

      [101] Maristella Ribs, C.G.Furtado, José Neuman de Souza, Giovanni Cordeiro Barroso, Antão Moura, Alberto S Lima, Flávio R.C Sousa, “A Petri net-based decision-making framework for assessing cloud services adoption: The use of spot instances for cost reductionâ€, Journal of Network and Computer Applications 57 (2015)102–118.https://doi.org/10.1016/j.jnca.2015.07.002.

      [102] PeiYun Zhang, and Meng Chu Zhou, “Dynamic Cloud Task Scheduling Based on a Two-Stage Strategyâ€, IEEE Transactions on Automation Science and Engineering, Volume 15, Issue: 2, April 2018.

      [103] Lina Ni, Jinquan Zhang, Changjun Jiang, Chungang Yan, and Kan Yu, “Resource Allocation Strategy in Fog Computing Based on Priced Timed Petri Netsâ€, IEEE Internet of Things Journal, Vol. 4, No. 5, pp.772–783, October 2017. https://doi.org/10.1109/JIOT.2017.2709814.

      [104] Yi-Li Zhang, Jin-Bai Zhang, “Schedule model in a cloud computing based on credit and costâ€, Computer Science, Technology and Application, https://doi.org/10.1142/9789813200449_0047.

      [105] Neethu B, K.R Remesh Babu, “Dynamic Resource Allocation in Market Oriented Cloud using Auction Methodâ€, 2016 International Conference on Micro-Electronics and Telecommunication Engineering, https://doi.org/10.1109/ICMETE.2016.137.

      [106] Mohammad Aazam, Eui-Nam Huh, Marc St-Hilaire, Chung-Horng Lung, and Ioannis Lambadaris, “Cloud Customer’s Historical Record Based Resource Pricingâ€, IEEE Transactions on Parallel and Distributed Systems, Vol. 27, No. 7, July 2016.https://doi.org/10.1109/TPDS.2015.2473850.

      [107] Salah-Eddine Benbrahim, Alejandro Quintero, and Martine Bellaiche, “Live Placement of Interdependent Virtual Machines to Optimize Cloud Service Profits and Penalties on SLAsâ€, IEEE Transactions on Cloud Computing, DOI 10.1109/TCC.2016.2603506,

      [108] Parvathy Babu, K.R Remesh Babu, “Cloud Revenue Maximization using Competition and Cooperationâ€, 2016 International Conference on Micro-Electronics and Telecommunication Engineering, https://doi.org/10.1109/ICMETE.2016.138.

      [109] Kaiyue Wu, Ping Lu, and Zuqing Zhu, “Distributed Online Scheduling and Routing of Multicast-Oriented Tasks for Profit-Driven Cloud Computingâ€, IEEE Communications Letters, Vol. 20, No. 4, April 2016.https://doi.org/10.1109/LCOMM.2016.2526001.

      [110] Xingquan Zuo, Guoxiang Zhang, and Wei Tan, “Self-Adaptive Learning PSO-Based Deadline Constrained Task Scheduling for Hybrid IaaS Cloudâ€, IEEE Transactions on Automation Science and Engineering, Vol. 11, No. 2, April 2014, pp. 564-573.https://doi.org/10.1109/TASE.2013.2272758.

      [111] Hua He, Guangquan Xu, Shanchen Pang, Zenghua Zhao, “AMTS: Adaptive multi-objective task scheduling strategy in cloud computingâ€, China Communications, Year: 2016, Volume: 13, Issue: 4, pp. 162 – 17.

      [112] Maria Alejandra Rodriguez, Rajkumar Buyya, “Deadline Based Resource Provisioning and Scheduling Algorithm for Scientific Workflows on Cloudsâ€, IEEE Transactions on Cloud Computing 2014, Volume 2, Issue 2, pp. 222 – 235.

      [113] Lizheng Guo, Shuguang Zhao, Shigen Shen, Changyuan Jiang, “Task Scheduling Optimization in Cloud Computing Based on Heuristic Algorithm, Journal of Networksâ€, Vol. 7, No. 3, March 2012, pp.547-553.https://doi.org/10.4304/jnw.7.3.547-553.

      [114] Pandey S, Wu L, Guru, Buyya R, “A particle swarm optimization-based heuristic for scheduling workflow applications in cloud computing environmentsâ€, 24th IEEE International Conference on Advanced Information Networking and Applications 2010.https://doi.org/10.1109/AINA.2010.31.

      [115] Zhangjun Wu, Ni Z, Gu L, Liu X, “A revised discrete particle swarm optimization for cloud workflow schedulingâ€, International Conference on Computational Intelligence and Security, IEEE, https://doi.org/10.1109/CIS.2010.46 .

      [116] Nazia Anwar and Huifang Deng, "A Hybrid Metaheuristic for Multi-Objective Scientific Workflow Scheduling in a Cloud Environment", Applied Sciences, 8 (2018), 538,.https://doi.org/10.3390/app8040538.

      [117] K.R.R. Babu, P. Samuel, Interference aware prediction mechanism for auto scaling in cloud, Computers and Electrical Engineering, Vol. 69(2018) pp. 351-363, https://doi.org/10.1016/j.compeleceng.2017.12.021.

      [118] Remesh Babu K.R., Samuel P, “Enhanced Bee Colony Algorithm for Efficient Load Balancing and Scheduling in Cloudâ€, Innovations in Bio-Inspired Computing and Applications. Advances in Intelligent Systems and Computing, vol 424. Springer, Cham.

  • Downloads

  • How to Cite

    R RemeshBabu, k, & Samuel, P. (2018). Review of the quality of service scheduling mechanisms in cloud. International Journal of Engineering & Technology, 7(3), 1677-1695. https://doi.org/10.14419/ijet.v7i3.13016