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Cloud Computing Virtualization of Resources Allocation for Distributed Systems

Abstract

Cloud computing is a new technology which managed by a third party “cloud provider” to provide the clients with services anywhere, at any time, and under various circumstances. In order to provide clients with cloud resources and satisfy their needs, cloud computing employs virtualization and resource provisioning techniques.  The process of providing clients with shared virtualized resources (hardware, software, and platform) is a big challenge for the cloud provider because of over-provision and under-provision problems. Therefore, this paper highlighted some proposed approaches and scheduling algorithms applied for resource allocation within cloud computing through virtualization in the datacenter. The paper also aims to explore the role of virtualization in providing resources effectively based on clients’ requirements. The results of these approaches showed that each proposed approach and scheduling algorithm has an obvious role in utilizing the shared resources of the cloud data center. The paper also explored that virtualization technique has a significant impact on enhancing the network performance, save the cost by reducing the number of Physical Machines (PM) in the datacenter, balance the load, conserve the server’s energy, and allocate resources actively thus satisfying the clients’ requirements. Based on our review, the availability of Virtual Machine (VM) resource and execution time of requests are the key factors to be considered in any optimal resource allocation algorithm. As a results of our analyzing for the proposed approaches is that the requests execution time and VM availability are main issues and should in consideration in any allocating resource approach.

Keywords

Cloud Computing, Virtualization, Resource Allocation, Distributed Systems

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References

  1. A. T. Saraswathi, Y. R. A. Kalaashri, and S. Padmavathi, “Dynamic Resource Allocation Scheme in Cloud Computing,” Procedia Comput. Sci., vol. 47, pp. 30–36, Jan. 2015, doi: 10.1016/j.procs.2015.03.180.
  2. P. Priyadarshinee, R. D. Raut, M. K. Jha, and B. B. Gardas, “Understanding and predicting the determinants of cloud computing adoption: A two staged hybrid SEM - Neural networks approach,” Comput. Hum. Behav., vol. 76, pp. 341–362, Nov. 2017, doi: 10.1016/j.chb.2017.07.027.
  3. S. Vakilinia, M. M. Ali, and D. Qiu, “Modeling of the resource allocation in cloud computing centers,” Comput. Netw., vol. 91, no. Supplement C, pp. 453–470, Nov. 2015, doi: 10.1016/j.comnet.2015.08.030.
  4. O. Alzakholi, L. Haji, H. Shukur, R. Zebari, S. Abas, and M. Sadeeq, “Comparison Among Cloud Technologies and Cloud Performance,” J. Appl. Sci. Technol. Trends, vol. 1, no. 2, pp. 40–47, Apr. 2020, doi: 10.38094/jastt1219.
  5. D. Kesavaraja and A. Shenbagavalli, “QoE enhancement in cloud virtual machine allocation using Eagle strategy of hybrid krill herd optimization,” J. Parallel Distrib. Comput., Sep. 2017, doi: 10.1016/j.jpdc.2017.08.015.
  6. R. R. Zebari, S. R. Zeebaree, and K. Jacksi, “Impact Analysis of HTTP and SYN Flood DDoS Attacks on Apache 2 and IIS 10.0 Web Servers,” in 2018 International Conference on Advanced Science and Engineering (ICOASE), 2018, pp. 156–161.
  7. R. R. Zebari, S. R. Zeebaree, K. Jacksi, and H. M. Shukur, “E-Business Requirements For Flexibility And Implementation Enterprise System: A Review.” International Journal of Scientific & Technology Research (IJSTR), vol. 8, no. 11, pp. 655-660, 2019.
  8. S. R. Zeebaree, K. Jacksi, and R. R. Zebari, “Impact analysis of SYN flood DDoS attack on HAProxy and NLB cluster-based web servers,” Indones. J. Electr. Eng. Comput. Sci., vol. 19, no. 1, pp. 510–517, 2020.
  9. S. R. Zeebaree, R. R. Zebari, and K. Jacksi, “Performance analysis of IIS10. 0 and Apache2 Cluster-based Web Servers under SYN DDoS Attack,” TEST Engineering & Management, vol. 83, no. March - April 2020, 5854 - 5863, 2020.
  10. S. R. Zeebaree, R. R. Zebari, K. Jacksi, and D. A. Hasan, “Security Approaches For Integrated Enterprise Systems Performance: A Review.” International Journal of Scientific & Technology Research (IJSTR) vol. 8, no. 12, pp. 2485-2489, 2019.
  11. R. Zebari, A. Abdulazeez, D. Zeebaree, D. Zebari, and J. Saeed, “A Comprehensive Review of Dimensionality Reduction Techniques for Feature Selection and Feature Extraction,” J. Appl. Sci. Technol. Trends, vol. 1, no. 2, pp. 56–70, May 2020, doi: 10.38094/jastt1224.
  12. A. A. S. Farrag, S. A. Mahmoud, and E. S. M. El-Horbaty, “Intelligent cloud algorithms for load balancing problems: A survey,” presented at the 2015 IEEE Seventh International Conference on Intelligent Computing and Information Systems (ICICIS), Dec. 2015, pp. 210–216, doi: 10.1109/IntelCIS.2015.7397223.
  13. R. Rengasamy and M. Chidambaram, “A Novel Predictive Resource Allocation Framework for Cloud Computing,” in 2019 5th International Conference on Advanced Computing Communication Systems (ICACCS), Mar. 2019, pp. 118–122, doi: 10.1109/ICACCS.2019.8728526.
  14. T. Deepika and A. N. Rao, “Active resource provision in cloud computing through virtualization,” in 2014 IEEE International Conference on Computational Intelligence and Computing Research, Dec. 2014, pp. 1–4, doi: 10.1109/ICCIC.2014.7238373.
  15. O. M. Ahmed and W. M. Abduallah, “A Review on Recent Steganography Techniques in Cloud Computing,” Acad. J. Nawroz Univ., vol. 6, no. 3, pp. 106–111, 2017.
  16. O. M. Ahmed and A. B. Sallow, “Android security: a review,” Acad. J. Nawroz Univ., vol. 6, no. 3, pp. 135–140, 2017.
  17. K. Zaki and H. Saad, “Adoption of Cloud Human Resource Information System in Egyptian Hotels: An Experimental Design Research,” Int. J. Herit. Tour. Hosp., vol. 12, no. 1, pp. 233–245, Mar. 2018.
  18. N. Harki, A. Ahmed, and L. Haji, “CPU Scheduling Techniques: A Review on Novel Approaches Strategy and Performance Assessment,” J. Appl. Sci. Technol. Trends, vol. 1, no. 2, pp. 48–55, 2020.
  19. M. A. Sadeeq, S. R. Zeebaree, R. Qashi, S. H. Ahmed, and K. Jacksi, “Internet of Things security: a survey,” in 2018 International Conference on Advanced Science and Engineering (ICOASE), 2018, pp. 162–166.
  20. S. A. Mostafa, S. S. Gunasekaran, A. Mustapha, M. A. Mohammed, and W. M. Abduallah, “Modelling an Adjustable Autonomous Multi-agent Internet of Things System for Elderly Smart Home,” in Advances in Neuroergonomics and Cognitive Engineering, Cham, 2020, pp. 301–311, doi: 10.1007/978-3-030-20473-0_29.
  21. S. Atiewi, A. Abuhussein, and M. A. Saleh, “Impact of Virtualization on Cloud Computing Energy Consumption: Empirical Study,” in Proceedings of the 2nd International Symposium on Computer Science and Intelligent Control, Stockholm, Sweden, Sep. 2018, pp. 1–7, doi: 10.1145/3284557.3284738.
  22. B. Kruekaew and W. Kimpan, “Virtual Machine Scheduling Management on Cloud Computing Using Artificial Bee Colony,” Lect. Notes Eng. Comput. Sci., vol. 1, pp. 18–22, Mar. 2014.
  23. M. Ficco, C. Esposito, F. Palmieri, and A. Castiglione, “A coral-reefs and Game Theory-based approach for optimizing elastic cloud resource allocation,” Future Gener. Comput. Syst., vol. 78, no. Part 1, pp. 343–352, Jan. 2018, doi: 10.1016/j.future.2016.05.025.
  24. S. R. Zeebaree, L. M. Haji, I. Rashid, R. R. Zebari, O. M. Ahmed, K. Jacksi, & H. M. Shukur, “Multicomputer Multicore System Influence on Maximum Multi-Processes Execution Time,” TEST Engineering & Management, vol. 83, no. May/June, pp. 14921–14931, May 2020.
  25. O. H. Jader, S. R. Zeebaree, and R. R. Zebari, “A State Of Art Survey For Web Server Performance Measurement And Load Balancing Mechanisms.” International Journal of Scientific & Technology Research (IJSTR), vol. 8, no. 12, pp. 535-543, 2019.
  26. M. Singh, “Virtualization in Cloud Computing- a Study,” in 2018 International Conference on Advances in Computing, Communication Control and Networking (ICACCCN), Oct. 2018, pp. 64–67, doi: 10.1109/ICACCCN.2018.8748398.
  27. Z. N. Rashid, S. R. Zebari, K. H. Sharif, and K. Jacksi, “Distributed Cloud Computing and Distributed Parallel Computing: A Review,” presented at the 2018 International Conference on Advanced Science and Engineering (ICOASE), 2018, pp. 167–172.
  28. Z. N. Rashid, S. R. Zeebaree, and A. Shengul, “Design and Analysis of Proposed Remote Controlling Distributed Parallel Computing System Over the Cloud,” presented at the 2019 International Conference on Advanced Science and Engineering (ICOASE), 2019, pp. 118–123.
  29. S. R. Zebari and A. S. Yowakib, “Improved Approach for Unbalanced Load-Division Operations Implementation on Hybrid Parallel Processing Systems,” Sci. J. Univ. Zakho, vol. 1, no. 2, pp. 832–848, 2013.
  30. S. R. M. Zeebaree, H. M. Shukur, L. M. Haji, R. R. Zebari, K. Jacksi, and S. M.Abas, “Characteristics and Analysis of Hadoop Distributed Systems,” Technology Reports of Kansai University, vol. 62, no. 4, pp. 1555–1564, Apr. 2020.
  31. M. Duggan, J. Duggan, E. Howley, and E. Barrett, “A network aware approach for the scheduling of virtual machine migration during peak loads,” Clust. Comput., vol. 20, no. 3, pp. 2083–2094, Sep. 2017, doi: 10.1007/s10586-017-0948-7.
  32. S. R. Zeebaree, H. M. Shukur, and B. K. Hussan, “Human resource management systems for enterprise organizations: A review,” Period. Eng. Nat. Sci., vol. 7, no. 2, pp. 660–669, 2019.
  33. T. Bhardwaj, H. Upadhyay, and S. C. Sharma, “Autonomic Resource Allocation Mechanism for Service-based Cloud Applications,” in 2019 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS), Oct. 2019, pp. 183–187, doi: 10.1109/ICCCIS48478.2019.8974515.
  34. S. R. Zeebaree, A. B. Sallow, B. K. Hussan, and S. M. Ali, “Design and Simulation of High-Speed Parallel/Sequential Simplified DES Code Breaking Based on FPGA,” in 2019 International Conference on Advanced Science and Engineering (ICOASE), 2019, pp. 76–81.
  35. K. Jacksi, N. Dimililer, and S. R. Zeebaree, “State of the art exploration systems for linked data: a review,” Int J Adv Comput Sci Appl IJACSA, vol. 7, no. 11, pp. 155–164, 2016.
  36. K. Jacksi, N. Dimililer, and S. R. Zeebaree, “A survey of exploratory search systems based on LOD resources,” 2015.
  37. K. Jacksi, S. Zeebaree, and N. Dimililer, “Design and Implementation of LOD Explorer: A LOD Exploration and Visualization Model,” J. Appl. Sci. Technol. Trends, vol. 1, no. 2, pp. 31–39, 2020.
  38. X. Yuan Min, Geyong ,Yang, Laurence T. ,Ding, Yi, Fang, Qing, “A game theory-based dynamic resource allocation strategy in Geo-distributed Datacenter Clouds,” Future Gener. Comput. Syst., vol. 76, no. Supplement C, pp. 63–72, Nov. 2017, doi: 10.1016/j.future.2017.04.046.
  39. N. Leontiou, D. Dechouniotis, S. Denazis, and S. Papavassiliou, “A hierarchical control framework of load balancing and resource allocation of cloud computing services,” Comput. Electr. Eng., vol. 67, pp. 235–251, 4, doi: 10.1016/j.compeleceng.2018.03.035.
  40. H. Zhou, S. Deng, H. Huang, and Y. Wu, “Resource allocation in cloud computing based on clustering method,” presented at the 2015 Annual IEEE Systems Conference (SysCon) Proceedings, Apr. 2015, pp. 489–494, doi: 10.1109/SYSCON.2015.7116799.
  41. B. R. Ibrahim, S. R. Zeebaree, and B. K. Hussan, “Performance Measurement for Distributed Systems using 2TA and 3TA based on OPNET Principles,” Sci. J. Univ. Zakho, vol. 7, no. 2, pp. 65–69, 2019.
  42. C.-F. Wang, W.-Y. Hung, and C.-S. Yang, “A prediction based energy conserving resources allocation scheme for cloud computing,” in 2014 IEEE International Conference on Granular Computing (GrC), Oct. 2014, pp. 320–324, doi: 10.1109/GRC.2014.6982857.
  43. P. Pradhan, P. K. Behera, and B. N. B. Ray, “Modified Round Robin Algorithm for Resource Allocation in Cloud Computing,” Procedia Comput. Sci., vol. 85, pp. 878–890, Jan. 2016, doi: 10.1016/j.procs.2016.05.278.
  44. M. Padmavathi and S. M. Basha, “Dynamic and elasticity ACO load balancing algorithm for cloud computing,” presented at the 2017 International Conference on Intelligent Computing and Control Systems (ICICCS), Jun. 2017, pp. 77–81, doi: 10.1109/ICCONS.2017.8250571.
  45. M. J. Usman, A. Samad, Ismail, H. Chizari, and A. Aliyu, “Energy-Efficient virtual machine allocation technique using interior search algorithm for cloud datacenter,” in 2017 6th ICT International Student Project Conference (ICT-ISPC), Johor, Malaysia, May 2017, pp. 1–4, doi: 10.1109/ICT-ISPC.2017.8075327.
  46. S. B. Akintoye and A. Bagula, “Optimization of virtual resources allocation in cloud computing environment,” in 2017 IEEE AFRICON, Sep. 2017, pp. 873–880, doi: 10.1109/AFRCON.2017.8095597.
  47. S. Yin, P. Ke, and L. Tao, “An improved genetic algorithm for task scheduling in cloud computing,” in 2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA), May 2018, pp. 526–530, doi: 10.1109/ICIEA.2018.8397773.
  48. J. Chen, “A Cloud Resource Allocation Method Supporting Sudden and Urgent Demands,” in 2018 Sixth International Conference on Advanced Cloud and Big Data (CBD), Aug. 2018, pp. 66–70, doi: 10.1109/CBD.2018.00021.
  49. A. Khodar, H. Fadhil, and I. Alkhayat, “New Scheduling Approach for Virtual Machine Resources in Cloud Computing based on Genetic Algorithm,” in 2019 International Russian Automation Conference (RusAutoCon), Sep. 2019, pp. 1–5, doi: 10.1109/RUSAUTOCON.2019.8867638.
  50. M. M. Than and T. Thein, “Energy-Saving Resource Allocation in Cloud Data Centers,” in 2020 IEEE Conference on Computer Applications(ICCA), Feb. 2020, pp. 1–6, doi: 10.1109/ICCA49400.2020.9022819.
  51. S. B. Akintoye and A. Bagula, “Optimization of virtual resources allocation in cloud computing environment,” in 2017 IEEE AFRICON, Sep. 2017, pp. 873–880, doi: 10.1109/AFRCON.2017.8095597.

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