ABSTRACT
Cloud computing enables cloud providers to offer computing infrastructure as a service (IaaS) in the form of virtual machines (VMs). Cloud management platforms automate the allocation of VMs to physical machines (PMs). An adaptive VM allocation policy is required to handle changes in the cloud environment and utilize the PMs efficiently In the literature, adaptive VM allocation is typically performed using either reservation-based or demand-based allocation. In this work, we have developed a parameter-based VM consolidation solution that aims to mitigate the issues with the reservation-based and demand-based solutions. This parameter-based VM consolidation exploits the range between demand-based and reservation-based finding VM to PM allocations that strike a delicate balance according to cloud providers' goals. Experiments conducted using CloudSim show how the proposed parameter-based solution gives a cloud provider the flexibility to manage the trade-off between utilization and other requirements.
- Luiz André Barroso and Urs Hölzle. 2007. The case for energy-proportional computing. (2007).Google Scholar
- L. A. Barroso and U. Hölzle. 2007. The Case for Energy-Proportional Computing. Computer 40, 12 (2007), 33--37. Google ScholarDigital Library
- A Beloglazov, J Abawajy, and R Buyya. 2012. Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Future Generation Computer Systems (2012). http://www.sciencedirect.com/science/article/pii/S0167739X11000689 Google ScholarDigital Library
- Anton Beloglazov and Rajkumar Buyya. 2012. Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in cloud data centers. Concurrency and Computation: Practice and Experience 24, 13 (2012), 1397--1420. Google ScholarDigital Library
- Damien Borgetto and Patricia Stolf. 2014. An energy efficient approach to virtual machines management in cloud computing. In Cloud Networking (CloudNet), 2014 IEEE 3rd International Conference on. IEEE, 229--235.Google ScholarCross Ref
- Nicolò Maria Calcavecchia, Ofer Biran, Erez Hadad, and Yosef Moatti. 2012. VM placement strategies for cloud scenarios. Proceedings - 2012 IEEE 5th International Conference on Cloud Computing, CLOUD 2012 (2012), 852--859. Google ScholarDigital Library
- Mohammed Rashid Chowdhury, Mohammad Raihan Mahmud, and Rashedur M Rahman. 2015. Implementation and performance analysis of various VM placement strategies in CloudSim. Journal of Cloud Computing 4, 1 (2015), 1. Google ScholarDigital Library
- Christopher Clark, Keir Fraser, Steven Hand, Jacob Gorm Hansen, Eric Jul, Christian Limpach, Ian Pratt, and Andrew Warfield. 2005. Live migration of virtual machines. In Proceedings of the 2nd conference on Symposium on Networked Systems Design & Implementation-Volume 2. USENIX Association, 273--286. Google ScholarDigital Library
- Waltenegus Dargie. 2014. Estimation of the cost of vm migration. In Computer Communication and Networks (ICCCN), 2014 23rd International Conference on. IEEE, 1--8.Google ScholarCross Ref
- Marcos Dias De Assuncao, Jean-Patrick Gelas, Laurent Lefevre, and Anne-Cecile Orgerie. 2012. The Green Gridfi5000: Instrumenting and using a Grid with energy sensors. In Remote Instrumentation for eScience and Related Aspects. Springer, 25--42.Google Scholar
- Xiaobo Fan, Wolf-Dietrich Weber, and Luiz Andre Barroso. 2007. Power provisioning for a warehouse-sized computer. In ACM SIGARCH Computer Architecture News, Vol. 35. ACM, 13--23. Google ScholarDigital Library
- Fahimeh Farahnakian, Adnan Ashraf, Tapio Pahikkala, Pasi Liljeberg, Juha Plosila, Ivan Porres, and Hannu Tenhunen. 2015. Using ant colony system to consolidate vms for green cloud computing. IEEE Transactions on Services Computing 8, 2 (2015), 187--198.Google ScholarCross Ref
- Md Hasanul Ferdaus, M Manzur Murshed, Rodrigo N Calheiros, and Rajkumar Buyya. 2014. Virtual Machine Consolidation in Cloud Data Centers Using ACO Metaheuristic. In Euro-Par. 306--317.Google Scholar
- Anshul Gandhi, Yuan Chen, Daniel Gmach, Martin Arlitt, and Manish Marwah. 2012. Hybrid resource provisioning for minimizing data center SLA violations and power consumption. Sustainable Computing: Informatics and Systems 2, 2 (2012), 91--104.Google ScholarCross Ref
- Abdul Hameed, Alireza Khoshkbarforoushha, Rajiv Ranjan, Prem Prakash Jayaraman, Joanna Kolodziej, Pavan Balaji, Sherali Zeadally, Qutaibah Marwan Malluhi, Nikos Tziritas, Abhinav Vishnu, and others. 2016. A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98, 7 (2016), 751--774. Google ScholarDigital Library
- A Hios and T Ulichnie. 2013. Top 10 Data Center Business Management Priorities for 2013 about the Uptime Institute Network. Technical Report. Technical Report, Uptime Institute.Google Scholar
- Craig A Lee and Alan F Sill. 2014. A design space for dynamic service level agreements in OpenStack. Journal of Cloud Computing 3, 1 (2014), 17.Google ScholarCross Ref
- Drazen Lucanin and Ivona Brandic. 2013. Take a break: cloud scheduling optimized for real-time electricity pricing. In Cloud and Green Computing (CGC), 2013 Third International Conference on. IEEE, 113--120. Google ScholarDigital Library
- Ching Chuen Teck Mark, Dusit Niyato, and Tham Chen-Khong. 2011. Evolutionary optimal virtual machine placement and demand forecaster for cloud computing. Proceedings - International Conference on Advanced Information Networking and Applications, AINA (2011), 348--355. Google ScholarDigital Library
- Michael Mitzenmacher. 2001. The power of two choices in randomized load balancing. IEEE Transactions on Parallel and Distributed Systems 12, 10 (2001), 1094--1104. Google ScholarDigital Library
- Mohammad Alaul Haque Monil and Rashedur M Rahman. 2016. VM consolidation approach based on heuristics fuzzy logic, and migration control. Journal of Cloud Computing 5, 1 (2016), 1--18. Google ScholarDigital Library
- Devwrat More, Sharad Mehta, Pooja Pathak, Lokesh Walase, and Jibi Abraham. 2014. Achieving Energy Efficiency by Optimal Resource Utilisation in Cloud Environment. In Cloud Computing in Emerging Markets (CCEM), 2014 IEEE International Conference on. IEE, 1--8.Google ScholarCross Ref
- Abdelkhalik Mosa and Norman W Paton. 2016. Optimizing virtual machine placement for energy and SLA in clouds using utility functions. Journal of Cloud Computing 5, 1 (2016), 17. Google ScholarDigital Library
- Nrdc.org. 2015. America's Data Centers Consuming and Wasting Growing Amounts of Energy. (2015). http://www.nrdc.org/energy/data-center-efficiency-assessment.aspGoogle Scholar
- Ilia Pietri and Rizos Sakellariou. 2016. Mapping virtual machines onto physical machines in cloud computing: A survey. ACM Computing Surveys (CSUR) 49, 3 (2016), 49. Google ScholarDigital Library
- Nguyen Quang-Hung, Pham Dac Nien, Nguyen Hoai Nam, Nguyen Huynh Tuong, and Nam Thoai. 2013. A genetic algorithm for power-aware virtual machine allocation in private cloud. In Information and Communication Technology-EurAsia Conference. Springer, 183--191. Google ScholarDigital Library
- Md Golam Rabbani, Rafael Pereira Esteves, Maxim Podlesny, Gael Simon, Lisandro Zambenedetti Granville, and Raouf Boutaba. 2013. On tackling virtual data center embedding problem. In Integrated Network Management (IM 2013), 2013 IFIP/IEEE International Symposium on. IEEE, 177--184.Google Scholar
- Lei Shi and Bernard Butler. 2013. Provisioning of requests for virtual machine sets with placement constraints in IaaS clouds. IFIP/IEEE International Symposium on Integrated Network Management (IM 2013) (2013), 499-505. http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6573024Google Scholar
- Fetahi Wuhib, Rerngvit Yanggratoke, and Rolf Stadler. 2013. Allocating Compute and Network Resources Under Management Objectives in Large-Scale Clouds. Journal of Network and Systems Management (2013), 1--26. Google ScholarDigital Library
- Zhen Xiao, Weijia Song, and Qi Chen. 2013. Dynamic resource allocation using virtual machines for cloud computing environment. IEEE transactions on parallel and distributed systems 24, 6 (2013), 1107--1117. Google ScholarDigital Library
Index Terms
Virtual machine consolidation for cloud data centers using parameter-based adaptive allocation
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