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Group Based Resource Management and Pricing Model in Cloud Computing


Affiliations
1 Institute of Information Technology, Jahangirnagar University, Dhaka, Bangladesh
 

Cloud computing utilizes large scale computing infrastructure that has been radically changing the IT landscape enabling remote access to computing resources with low service cost, high scalability , availability and accessibility. Serving tasks from multiple users where the tasks are of different characteristics with variation in the requirement of computing power may cause under or over utilization of resources. Therefore maintaining such mega-scale datacenter requires efficient resource management procedure to increase resource utilization. However, while maintaining efficiency in service provisioning it is necessary to ensure the maximization of profit for the cloud providers. Most of the current research works aims at how providers can offer efficient service provisioning to the user and improving system performance. There are comparatively fewer specific works regarding resource management which also deals with the economic section that considers profit maximization for the provider. In this paper we represent a model that deals with both efficient resource utilization and pricing of the resources. The joint resource management model combines the work of user assignment, task scheduling and load balancing on the fact of CPU power endorsement. We propose four algorithms respectively for user assignment, task scheduling, load balancing and pricing that works on group based resources offering reduction in task execution time(56.3%), activated physical machines(41.44%),provisioning cost(23%). The cost is calculated over a time interval involving the number of served customer at this time and the amount of resources used within this time.

Keywords

Resource Management, Resource Pricing, Task Execution, Load Balancing, Task Scheduling.
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  • R. Buyya, C. S. Yeo, S. Venugopal, J. Broberg, I. Brandic, Cloud computing and emergingit platforms: Vision, hype, and reality for delivering computing as the 5th utility, FutureGeneration computer systems 25 (2009) 599–616..

  • M. Whaiduzzaman, M. N. Haque, M. RejaulKarimChowdhury, A. Gani, A study on strategicprovisioning of cloud computing services, The Scientific World Journal 2014 (2014)

  • M .Alba, IoT Devices to Outnumber Humans in 2017, https://www.engineering.com/IOT/ArticleID/15594/IoT-Devices-to-Outnumber-Humans-in2017.aspx,2017. [Online; ac-cessed 8-May-2018].

  • S. Akter, M. Whaiduzzaman, Dynamic service level agreement verification in cloud computing, IJCSIS (2017).

  • N. C. Luong, P. Wang, D. Niyato, Y. Wen, Z. Han, Resource management in cloud networking using economic analysis and pricing models: A survey, IEEE Communications Surveys & Tutorials 19 (2017) 954–1001.

  • R. weber, Cost Based Pricing,https://onlinelibrary.wiley.com/doi/abs/10.1002/ 0470867175.ch7, 2003. [Online; accessed 9-May-2018].

  • K. H. Prasad, T. A. Faruquie, L. V. Subramaniam, M. Mohania, G. Venkatachaliah, Resource allocation and sla determination for large data processing services over cloud, in: Services Computing (SCC), 2010 IEEE International Conference on, IEEE, pp. 522–529.

  • D. Di Spaltro, A. Polvi, L. Welliver, Methods and systems for cloud computing management, 2016.

  • US Patent 9,501,329.

  • M. Shojafar, N. Cordeschi, E. Baccarelli, Energy-efficient adaptive resource management for realtime vehicular cloud services, IEEE Transactions on Cloud computing (2016)

  • E. Oppong, S. Khaddaj, H. E. Elasriss, Cloud computing: resource management and serviceallocation, in: Distributed Computing and Applications to Business, Engineering & Science (DCABES), 2013 12th International Symposium on, IEEE, pp. 142–145.

  • D. Ajmire, M. Atique, Grouping based load balancing in cloud computing, International Journal of Innovative Research and Development 5 (2016).

  • S. Abrishami, M. Naghibzadeh, D. H. Epema, Deadline-constrained workflow scheduling algorithms for infrastructure as a service clouds, Future Generation Computer Systems 29 (2013) 158–169.

  • Z. Tang, L. Qi, Z. Cheng, K. Li, S. U. Khan, K. Li, An energy-efficient task scheduling algorithm in dvfs-enabled cloud environment, Journal of Grid Computing 14 (2016) 55–74.

  • J. M. Galloway, K. L. Smith, S. S. Vrbsky, Power aware load balancing for cloud computing, in: Proceedings of the World Congress on Engineering and Computer Science, volume 1, pp. 19–21.

  • E. Ibrahim, N. A. El-Bahnasawy, F. A. Omara, Task scheduling algorithm in cloud com-puting environment based on cloud pricing models, in: Computer Applications & Research (WSCAR), 2016 World Symposium on, IEEE, pp. 65–71.

  • H. K. Ala’a Al-Shaikh, A. Sharieh, A. Sleit, Resource utilization in cloud computing as an optimization problem, Resource 7 (2016).

  • M. Whaiduzzaman, A. Naveed, A. Gani, Mobicore: Mobile device based cloudlet resource enhancement for optimal task response, IEEE Transactions on Services Computing (2016).

  • Whaiduzzaman, Md. "Performance enhancement framework for cloudlet in mobile cloud computing."

  • PhD diss., FakultiSainsKomputerdanTeknologiMaklumat, Universiti Malaya, 2016.

  • Whaiduzzaman, Md, Abdullah Gani, and AnjumNaveed. "Pefc: performance enhancement framework for cloudlet in mobile cloud computing." In Robotics and Manufacturing Automation (ROMA), 2014 IEEE International Symposium on, pp. 224-229. IEEE, 2014.

  • N. Zhang, H. Hämmäinen, Cost efficiency of sdn in lte-based mobile networks: Case finland,in: Networked Systems (NetSys), 2015 International Conference and Workshops on, IEEE, pp. 1–5

  • Z. Cao, J. Lin, C. Wan, Y. Song, Y. Zhang, X. Wang, Optimal cloud computing resource allocation for demand side management in smart grid, IEEE Transactions on Smart Grid 8 (2017) 1943–1955.

  • K. Tsakalozos, H. Kllapi, E. Sitaridi, M. Roussopoulos, D. Paparas, A. Delis, Flexible use of cloud resources through profit maximization and price discrimination, in: Data Engineering (ICDE), 2011 IEEE 27th International Conference on, IEEE, pp. 75–86.

  • J. Cao, K. Hwang, K. Li, A. Y. Zomaya, Optimal multiserver configuration for profit maxi-mization in cloud computing, ieee transactions on parallel and distributed systems 24 (2013) 1087–1096.

  • D. M. Divakaran, M. Gurusamy, M. Sellamuthu, Bandwidth allocation with differential pricing for flexible demands in data center networks, Computer Networks 73 (2014) 84–97.

  • J. Zhao, H. Li, C. Wu, Z. Li, Z. Zhang, F. C. Lau, Dynamic pricing and profit maximization for the cloud with geo-distributed data centers, in: INFOCOM, 2014 Proceedings IEEE, IEEE, pp. 118–126.

  • H. Shen, Rial: Resource intensity aware load balancing in clouds, IEEE Transactions on Cloud Computing (2017).

  • W. Voorsluys, J. Broberg, S. Venugopal, R. Buyya, Cost of virtual machine live migration in clouds: A performance evaluation, in: IEEE International Conference on Cloud Computing, Springer, pp.

  • –265.

  • Whaiduzzaman, Md. "Performance enhancement framework for cloudlet in mobile cloud computing."

  • PhD diss., FakultiSainsKomputerdanTeknologiMaklumat, Universiti Malaya, 2016.


Abstract Views: 388

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  • Group Based Resource Management and Pricing Model in Cloud Computing

Abstract Views: 388  |  PDF Views: 159

Authors

Shelia Rahman
Institute of Information Technology, Jahangirnagar University, Dhaka, Bangladesh
Afroza Sultana
Institute of Information Technology, Jahangirnagar University, Dhaka, Bangladesh
Afsana Islam
Institute of Information Technology, Jahangirnagar University, Dhaka, Bangladesh
Md. Whaiduzzaman
Institute of Information Technology, Jahangirnagar University, Dhaka, Bangladesh

Abstract


Cloud computing utilizes large scale computing infrastructure that has been radically changing the IT landscape enabling remote access to computing resources with low service cost, high scalability , availability and accessibility. Serving tasks from multiple users where the tasks are of different characteristics with variation in the requirement of computing power may cause under or over utilization of resources. Therefore maintaining such mega-scale datacenter requires efficient resource management procedure to increase resource utilization. However, while maintaining efficiency in service provisioning it is necessary to ensure the maximization of profit for the cloud providers. Most of the current research works aims at how providers can offer efficient service provisioning to the user and improving system performance. There are comparatively fewer specific works regarding resource management which also deals with the economic section that considers profit maximization for the provider. In this paper we represent a model that deals with both efficient resource utilization and pricing of the resources. The joint resource management model combines the work of user assignment, task scheduling and load balancing on the fact of CPU power endorsement. We propose four algorithms respectively for user assignment, task scheduling, load balancing and pricing that works on group based resources offering reduction in task execution time(56.3%), activated physical machines(41.44%),provisioning cost(23%). The cost is calculated over a time interval involving the number of served customer at this time and the amount of resources used within this time.

Keywords


Resource Management, Resource Pricing, Task Execution, Load Balancing, Task Scheduling.

References