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Dynamically Weighted Load Evaluation Method Based on Self-adaptive Threshold in Cloud Computing

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Abstract

Cloud resources and their loads possess dynamic characteristics. Current research methods have utilized certain physical indicators and fixed thresholds to evaluate cloud resources, which cannot meet the dynamic needs of cloud resources or accurately reflect their resource states. To address this challenge, this paper proposes a Self-adaptive threshold based Dynamically Weighted load evaluation Method (termed SDWM). It evaluates the load state of the resource through a dynamically weighted evaluation method. First, the work proposes some dynamic evaluation indicators in order to evaluate the resource state more accurately. Second, SDWM divided the resource load into three states, including O v e r l o a d, N o r m a l and I d l e using the self-adaptive threshold. It then migrated those overload resources to a balance load, and releases the idle resources whose idle times exceeded a threshold to save energy, which could effectively improve system utilization. Finally, SDWM leveraged an energy evaluation model to describe energy quantitatively using the migration amount of the resource request. The parameters of the energy model were obtained from a linear regression model according to the actual experimental environment. Experimental results showed that SDWM is superior to other methods in energy conservation, task response time, and resource utilization, and the improvements are 31.5 %, 50 %, 50.8 %, respectively. These results demonstrate the positive effect of the dynamic self-adaptive threshold. More specially, SDWM shows great adaptability when resources dynamically join or exit.

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References

  1. Dong M, Lit H, Ota K et al (2014) HVSTO: Efficient privacy preserving hybrid storage in cloud data center. In: in Proc. of 2014 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). IEEE, pp 529–534

  2. Dong M, Li H, Ota K et al (2014) Multicloud-Based Evacuation Services for Emergency Management. IEEE Cloud Comp 1(4):50–59

    Article  Google Scholar 

  3. Chang S. B., Jeun W. L., Youn C. H. (2011) Utility adaptive service brokering mechanism for personal cloud service. In: Proc. 2011 Military Communications Conference, Baltimore, MD, 7-10 Nov, pp 1622–1627

  4. Hikita J, Hirano A, Nakashima H (2008) Saving 200kw and 200 dollar k/year by power-aware job/machine scheduling. In: Proc. 2008 IEEE International Symposium on Parallel and Distributed Processing, Miami, FL, 14-18 Apr., 1-8

  5. Bucur AID, Epema DHJ (2007) Scheduling Policies for Processor Collocation in Multi cluster System. IEEE Trans Parallel Distrib Syst 18:958–962

    Article  Google Scholar 

  6. Dai YS, Levitin G, Trivedi KS (2007) Performance and reliability of tree-structured grid services considering data dependence and failure correlation. IEEE Trans Comput 56(7):925–936

    Article  MathSciNet  Google Scholar 

  7. Song F, Xu CZ (2007) Exploring event correlation for failure prediction in coalitions of clusters. In: Proc. of the 2007 ACM/IEEE Conference on Supercomputing, Reno, NV, USA, 10-16 Nov, pp 1–12

  8. Beloglazov A., Buyya R. (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):1397–1420

    Article  Google Scholar 

  9. Tang Z, Jiang L, Zhou J., Li K., Li K. (2015) A self-adaptive scheduling algorithm for reduce start time. Futur Gener Comput Syst 43(5):51–60

    Article  Google Scholar 

  10. Tripathy B., Dash S., Padhy S. K. (2015) Dynamic task scheduling using a directed neural network. Journal of Parallel and Distributed Computing 75(3):101–106

    Article  Google Scholar 

  11. Olivier B., Lionel E. D., Christopher T. C., Hejer R. (2013) Heterogeneous Resource Allocation under Degree Constraints. IEEE Trans Parallel Distrib Syst 24(5):926–937

    Article  Google Scholar 

  12. Islam S., Keung J., Lee K., Liu A. (2012) Empirical prediction models for adaptive resource provisioning in the cloud. Futur Gener Comput Syst 28(1):155–16

    Article  Google Scholar 

  13. Zhang Q., Zhani M. F., Boutaba R. (2014) Dynamic Heterogeneity-Aware Resource Provisioning in the Cloud. IEEE Transactions on Cloud Computing 2(1):14–28

    Article  Google Scholar 

  14. Bruneo D. (2014) A Stochastic Model to Investigate Data Center Performance and QoS in IaaS Cloud Computing Systems. IEEE Trans Parallel Distrib Syst 25(3):560–569

    Article  Google Scholar 

  15. Bera S., Misra S., Rodrigues J. J. P. C. (2015) Cloud Computing Applications for Smart Grid: A Survey. IEEE Trans Parallel Distrib Syst 26(5):1477–1494

    Article  Google Scholar 

  16. Pagani S., Chen J. J., Li M. M. (2015) Energy Efficiency on Multi-core Architectures with Multiple Voltage Islands. IEEE Trans Parallel Distrib Syst 26(6):1608–1621

    Article  Google Scholar 

  17. Cardosa M., Singh A., Pucha H., Chandra A. (2012) Exploiting spatio-temporal tradeoffs for energy-aware MapReduce in the Cloud. IEEE Trans Comput 61(12):1737–1751

    Article  MathSciNet  Google Scholar 

  18. Ghamkhari M., Mohsenian-Rad H. (2013) Energy and Performance Management of Green Data Centers: A Profit Maximization Approach. IEEE Trans Smart Grid 4(2):1017–1025

    Article  Google Scholar 

  19. Mobius C., Dargie W., Schill A. (2013) Power Consumption Estimation Models for Processors, Virtual Machines, and Servers. IEEE Trans Parallel Distrib Syst 25(6):1045–9219

    Google Scholar 

  20. Pedram M. (2012) Energy-Efficient Datacenters. IEEE Trans Comput Aided Des Integr Circuits Syst 31 (10):1465–1484

    Article  Google Scholar 

  21. Guo Y., Fang Y. (2013) Electricity cost saving strategy in data centers by using energy storage. IEEE Trans Parallel Distrib Syst 24(6):1149–1160

    Article  Google Scholar 

  22. Farahnakian F., Liljeberg P., Plosila J. (2014) Energy-Efficient Virtual Machines Consolidation in Cloud Data Centers using Reinforcement Learning. In: Proc. of 2014 22nd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing, Torino, 12-14 Feb , pp 500–507

  23. Garraghan P., Moreno I. S., Townend P., Xu J. (2014) An Analysis of Failure-Related Energy Waste in a Large-Scale Cloud Environment. IEEE Transactions on Emerging Topics in Computing 2(2):166–180

    Article  Google Scholar 

  24. Hosseinimotlagh S., Khunjush F., Hosseinimotlagh S. (2014) A Cooperative Two-Tier Energy-Aware Scheduling for Real-Time Tasks in Computing Clouds. In: Proc. of 2014 22nd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing, Torino, 12-14 Feb, pp 178–182

  25. Zhu XM, Yang T, Chen HK, Wang J, Yin S, Liu XC (2014) Real-Time Tasks Oriented Energy-Aware Scheduling in Virtualized Clouds. IEEE Transactions on Cloud Computing, 2014 2(2):180–198

    Google Scholar 

  26. Luo J. Y., Rao L., Liu X. (2014) Temporal Load Balancing with Service Delay Guarantees for Data Center Energy Cost Optimization. IEEE Trans Parallel Distrib Syst 25(3):775–784

    Article  Google Scholar 

  27. Ye K. J., WU Z. H., Jiang X. H., He Q. M. (2012) Power management of virtualized cloud computing platform. Chin J Comput Phys 35(6):1262–1283

    Article  Google Scholar 

  28. Kansal A., Zhao F., Liu J., Kothari N., Bhattacharya A. A. (2010) Virtual machine power metering and provisioning. In: Proc. of the 1st ACM symposium on Cloud computing. Indianapolis, Indiana, USA, ACM. 10-11 June, pp 39–50

  29. Owusu F., Pattinson C. (2012) The current state of understanding of the energy efficiency of cloud computing. In: Proc. of 2012 IEEE 11th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), Liverpool, 25-27 June, pp 1948–1953

  30. Kliazovich D., Bouvry P., Khan S. U. (2012) GreenCloud: a packet-level simulator of energy-aware cloud computing data centers. J Supercomput 62(3):1263–1283

    Article  Google Scholar 

  31. Bessis N., Sotiriadis S., Pop F. et al (2013) Using a novel message-exchanging optimization (MEO) model to reduce energy consumption in distributed systems. Simul Model Pract Theory 39(3):104–120

    Article  Google Scholar 

  32. Nathuji R., Schwan K. (2007) VirtualPower: coordinated power management in virtualized enterprise systems. ACM SIGOPS Operating Systems Review 41(6):265–278

    Article  Google Scholar 

  33. Cao J. W., Li K. Q. (2014) Optimal Power Allocation and Load Distribution for Multiple Heterogeneous Multicore Server Processors across Clouds and Data Centers. IEEE Trans Comput 63(1):45–58

    Article  MathSciNet  Google Scholar 

  34. Verma A., Ahuja P., Neogi A (2008) pMapper: power and migration cost aware application placement in virtualized systems. Middleware 2008. Springer Berlin Heidelberg, pp 243–264

  35. Zhu X., Young D., Watson BJ et al (2008) 1000 islands: Integrated capacity and workload management for the next generation data center. In: Proc. of 2008 International Conference on Autonomic Computing, Chicago, IL, 2-6 June, pp 172–181

  36. Gmach D., Rolia J., Cherkasova L. et al (2008) An integrated approach to resource pool management: Policies, efficiency and quality metrics. In: Proc. of IEEE International Conference on Dependable Systems and Networks With FTCS and DCC, Anchorage, AK, 24-27 June, pp 326–335

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

    Article  Google Scholar 

  38. Beloglazov A., Buyya R. (2013) Managing overloaded hosts for dynamic consolidation of virtual machines in cloud data centers under quality of service constraints. IEEE Trans Parallel Distrib Syst 24(7):1366–1379

    Article  Google Scholar 

  39. Xiao Z., Song W., Chen Q. (2013) Dynamic Resource Allocation Using Virtual Machines for Cloud Computing Environment. IEEE Trans Parallel Distrib Syst 24(6):1107–1116

    Article  Google Scholar 

  40. Liu H. K., He B. S. (2015) VMbuddies: Coordinating Live Migration of Multi-Tier Applications in Cloud Environments. IEEE Trans Parallel Distrib Syst 26(4):1192–1205

    Article  Google Scholar 

  41. Hu Z. J. (2010) The resource availability evaluation in service grid environment for QoS. Ph.D. dissertation

  42. Baliga J., Ayre R. W. A., Hinton K., Tucker RS (2011) Green cloud computing: Balancing energy in processing, storage, and transport. Proc IEEE 99(1):149–167

    Article  Google Scholar 

  43. Dong M, Li H, Ota K et al (2015) Rule caching in SDN-enabled mobile access networks. IEEE Netw 29 (4):40–45

    Article  Google Scholar 

  44. Zuo L., Dong S., Zhu C., Shu L., Han G. (2015) A Cloud Resource Evaluation Model Based on Entropy Optimization and Ant Colony Clustering. Comput J 58(6):1254–1266

    Article  Google Scholar 

  45. Zuo L., Shu L., Zhu C., Zhou Z. (2013) A Resource Evaluation Model Based on Entropy Optimization toward Green Cloud. In: Proc. of 2013 Semantics, Knowledge and Grids 2013, Beijing, Oct, pp 74–81

  46. Valls M G, Alonso A, Ruiz J et al (2003) An architecture of a quality of service resource manager middleware for flexible embedded multimedia systems. In: Software Engineering and Middleware, Springer Berlin Heidelberg, pp 36–55

  47. Palopoli L, Cucinotta T, Marzario L et al (2009) AQuoSAadaptive quality of service architecture. In: Software: Practice and Experience, vol. 39 no. 1, 1-31

  48. Calheiros R. N., Ranjan R., Beloglazov A., Cesar A. F., De R., Buyya R. (2011) CloudSim: A Toolkit for Modeling and Simulation of Cloud Computing Environments and Evaluation of Resource Provisioning Algorithms. Software: Practice and Experience, Wiley 41(1):23–50

    Google Scholar 

  49. He X., Sun X. (2003) QoS Guided Min-Min Heuristic for Grid Task Scheduling. J Comput Sci Technol 18 (4):442–451

    Article  MATH  Google Scholar 

Download references

Acknowledgments

This work is supported by the Natural Science Foundation of Guangdong Province, China Project No. 2014A030313729, 2013 Special Fund of Guangdong Higher School Talent Recruitment, 2013 top Level Talents Project in Sailing Plan of Guangdong Province, National Natural Science Foundation of China (Grant NO. 61401107), and 2014 Guangdong Province Outstanding Young Professor Project, Science and Technology Key Project of Guangdong No. 2014B010112006, Natural Science Fund of Guangdong No. 2015A030308017. Lei Shu and Shoubin Dong are the corresponding authors.

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Authors and Affiliations

  1. Guangdong Provincial Key Laboratory of Petrochemical Equipment Fault Diagnosis, Guangdong University of Petrochemical Technology, Maoming, China

    Liyun Zuo & Lei Shu

  2. School of Computer Science and Engineering, South China University of Technology, Guangzhou, China

    Liyun Zuo & Shoubin Dong

  3. Department of Electrical and Computer Engineering, The University of British Columbia, Vancouver, Canada

    Chunsheng Zhu

  4. China University of Geoscience, Beijing, China

    Zhangbing Zhou

  5. TELECOM SudParis, Paris, France

    Zhangbing Zhou

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  1. Liyun Zuo
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Correspondence to Liyun Zuo.

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Zuo, L., Shu, L., Dong, S. et al. Dynamically Weighted Load Evaluation Method Based on Self-adaptive Threshold in Cloud Computing. Mobile Netw Appl 22, 4–18 (2017). https://doi.org/10.1007/s11036-016-0679-7

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  • DOI: https://doi.org/10.1007/s11036-016-0679-7

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