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Dynamic forecast scheduling algorithm for virtual machine placement in cloud computing environment

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Abstract

In most cloud computing platforms, the virtual machine quotas are seldom changed once initialized, although the current allocated resources are not efficiently utilized. The average utilization of cloud servers in most datacenters can be improved through virtual machine placement optimization. How to dynamically forecast the resource usage becomes a key problem. This paper proposes a scheduling algorithm called virtual machine dynamic forecast scheduling (VM-DFS) to deploy virtual machines in a cloud computing environment. In this algorithm, through analysis of historical memory consumption, the most suitable physical machine can be selected to place a virtual machine according to future consumption forecast. This paper formalizes the virtual machine placement problem as a bin-packing problem, which can be solved by the first-fit decreasing scheme. Through this method, for specific virtual machine requirements of applications, we can minimize the number of physical machines. The VM-DFS algorithm is verified through the CloudSim simulator. Our experiments are carried out on different numbers of virtual machine requests. Through analysis of the experimental results, we find that VM-DFS can save 17.08 % physical machines on the average, which outperforms most of the state-of-the-art systems.

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References

  1. Armbrust M, Fox A, Griffith R, Joseph AD et al (2010) A view of cloud computing. Commun ACM 53(4):50–58

    Article  Google Scholar 

  2. Barham P, Dragovic B, Fraser K et al (2003) Xen and the art of virtualization. ACM SIGOPS Oper Syst Rev 37(5):164–177

    Article  Google Scholar 

  3. Zhang F, Sakr MF (2014) Performance variations in resource scaling for mapreduce applications on private and public clouds. In: Proceedings of The 7th IEEE international conference on cloud computing (cloud), Alaska, June 2014

  4. Wen X, Gu G, Li Q, Gao Y, Zhang X (2012) Comparison of open-source cloud management platforms: OpenStack and OpenNebula. In: Proceeding of IEEE 9th International Conference on Fuzzy Systems and Knowledge Discovery, pp 2457–2461

  5. Padala P, Zhu X, Wang Z, Singhal S, Shin KG et al (2007) Performance evaluation of virtualization technologies for server consolidation. HP Labs Tec. Report, Palo Alto

    Google Scholar 

  6. Clark C, Fraser K, Hand S et al (2005) Live migration of virtual machines. In: Proceedings of the 2nd conference on Symposium on Networked Systems Design and Implementation vol 2, pp 273–286

  7. Liu H, Jin H, Liao X, Hu L, Yu C (2009) Live migration of virtual machine based on full system trace and replay. In: Proceedings of the 18th ACM international symposium on High performance distributed computing, pp 101–110

  8. Pearce O, Gamblin T, de Supinski BR, Schulz M, Amato NM (2012) Quantifying the effectiveness of load balance algorithms. Proceedings of the 26th ACM international conference on Supercomputing, pp 185–194

  9. Singh A, Korupolu M, Mohapatra D (2008) Server-storage virtualization: integration and load balancing in data centers. In: Proceedings of the 2008 ACM/IEEE conference on Supercomputing, pp 53

  10. Carrera D, Steinder M, Whalley I, Torres J, Ayguadé E (2008) Managing SLAs of heterogeneous workloads using dynamic application placement. In: Proceedings of the 17th international symposium on High performance distributed computing, pp 217–218

  11. Verma A, Ahuja P, Neogi A (2008) Power-aware dynamic placement of HPC applications. In: Proceedings of the 22nd annual international conference on Supercomputing, pp 175–184

  12. Lucas Simarro JL, Moreno-Vozmediano R, Montero RS, Llorente IM (2011) Dynamic placement of virtual machines for cost optimization in multi-cloud environments. 2011 International Conference on High Performance Computing and Simulation (HPCS), pp 1–7

  13. Chen G, He W, Liu J, Nath S, Rigas L, Xiao L, Zhao F (2008) Energy-aware server provisioning and load dispatching for connection-intensive internet services. NSDI, pp 337–350

  14. Beloglazov A, Abawajy J, Buyya R (2012) Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Future Gener Comput Syst 28(5):755–768

    Article  Google Scholar 

  15. Stage A, Setzer T (2009) Network-aware migration control and scheduling of differentiated virtual machine workloads. In: Proceedings of the 2009 ICSE Workshop on Software Engineering Challenges of Cloud Computing, IEEE Computer Society, pp 9C–14

  16. Tang Z, Zhou J, Li K, Li R (2012) MTSD: a task scheduling algorithm for MapReduce base on deadline constraints. Parallel and Distributed Processing Symposium Workshops and PhD Forum (IPDPSW), 2012 IEEE 26th International, IEEE Computer Society

  17. Bobroff N, Kochut A, Beaty K (2007) Dynamic placement of virtual machines for managing sla violations. In: 10th IFIP/IEEE International Symposium on Integrated Network Management, pp 119C–128

  18. Coffman EG Jr, Garey MR, Johnson DS (1996) Approximation algorithms for bin packing: a survey. Approximation algorithms for NP-hard problems, pp 46–93

  19. Coffman EG Jr, Garey MR, Johnson DS (1991) A simple proof of the inequality \( FFD (L) \le 11/9 OPT (L)+ 1, L \) for the FFD bin-packing algorithm. Acta Math Appl Sinica 7(4):321–331

    Article  MathSciNet  Google Scholar 

  20. Box GE, Jenkins GM, Reinsel GC (2013) Time series analysis: forecasting and control. Wiley.com, New York

    Google Scholar 

  21. Shen D, Hellerstein JL (2000) Predictive models for proactive network management: application to a production web server. In: Proceeding of Network Operations and Management Symposium, pp 833–846

  22. Petrovic D, Schiper A (2012) Implementing virtual machine replication: a case study using Xen and KVM. In: Proceedings of 26th international conference on advanced information networking and applications(AINA), pp 73–80

  23. Li C-H (2012) Evaluating the effectiveness of memory overcommit techniques on kvm-based hosting platform. In: Proceedings of World Academy of Science, Engineering and Technology, no. 70, World Academy of Science, Engineering and Technology

  24. Dickey DA, Fuller WA (1979) Distribution of the estimators for autoregressive time series with a unit root. J Am Stat Assoc 74(336a):427–431

    MATH  MathSciNet  Google Scholar 

  25. Schneider T, Neumaier A (2001) Algorithm 808: ARfit Matlab package for the estimation of parameters and eigenmodes of multivariate autoregressive models. ACM Trans Math Softw 27(1):58–65

    Article  MATH  Google Scholar 

  26. Calheiros RN, Ranjan R, Beloglazov A, De Rose CA, Buyya R (2011) CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw Pract Exp 41(1):23–50

    Article  Google Scholar 

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Acknowledgments

The authors are grateful to the three anonymous reviewers for their criticism and comments which have helped to improve the presentation and quality of the paper. This work is supported by the Key Program of National Natural Science Foundation of China (Grant No. 61133005), National Natural Science Foundation of China (Grant Nos. 61103047 and 61370095), and Open Foundation of State Key Laboratory of Software Engineering (SKLSE20 I2-09-18).

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Authors and Affiliations

  1. College of Information Science and Engineering, Hunan University, Changsha, 410082, China

    Zhuo Tang, Yanqing Mo, Kenli Li & Keqin Li

  2. Department of Computer Science, State University of New York, New Paltz, NY, 12561, USA

    Keqin Li

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  1. Zhuo Tang
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  2. Yanqing Mo
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  3. Kenli Li
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Correspondence to Zhuo Tang.

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Tang, Z., Mo, Y., Li, K. et al. Dynamic forecast scheduling algorithm for virtual machine placement in cloud computing environment. J Supercomput 70, 1279–1296 (2014). https://doi.org/10.1007/s11227-014-1227-5

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