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Intelligent scheduling with deep fusion of hardware-software energy-saving principles for greening stochastic nonlinear heterogeneous super-systems

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Abstract

Green computing of stochastic nonlinear heterogeneous super-systems, represented by the cloud, is a new demand for sustainable human developments. However, the scheduling middleware is now in urgent need of a series of theoretical breakthroughs from homogeneity to heterogeneity, linearity to non-linearity, and even fuzzy decision-making to scientific decision-making based on mathematical model. Focusing on deep fusion of hardware-software energy-saving principles, an energy-aware intelligent scheduling model and algorithm are proposed in this paper; throughout the stages of model preparation, composition and algorithm designs, three features and innovations are included, which are formalizing hardware energy-saving principles via nonlinear regression quantization, a comprehensive evaluation model of adaptive green scheduling for stochastic nonlinear heterogeneous super-systems, and a scheduling algorithm with distributed evolutionary intelligence. Extensive simulator and simulation experiments highlight obvious superiorities in the proposed scheduler such as higher efficacy and better scalability, which fully considers nonlinear diversities of heterogeneous super-systems whether for data or computing intensive stochastic tasks.

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References

  1. Ax J, Sievers G, Daberkow J (2018) Coreva-mpsoc: a many-core architecture with tightly coupled shared and local data memories. IEEE Trans Parallel Distrib Syst 29(5):1030–1043

    Article  Google Scholar 

  2. Baymani S, Alexopoulos K, Valat S (2017) Exploring rapidio technology within a daq system event building network. IEEE Trans Nucl Sci 64(9):2598–2605

    Article  Google Scholar 

  3. Belkhir L, Elmeligi A (2018) Assessing ict global emissions footprint: trends to 2040 & recommendations. J Clean Prod 117(3):448–463

    Article  Google Scholar 

  4. Branke J, Nguyen S, Pickardt CW, Zhang M (2016) Automated design of production scheduling heuristics: a review. IEEE Trans Evol Comput 20(1):110–124

    Article  Google Scholar 

  5. Buchaca-Prats D, Lluis-Berral J, Carrera D (2018) Automatic generation of workload profiles using unsupervised learning pipelines. IEEE Trans Netw Serv Manag 15(1):142–155

    Article  Google Scholar 

  6. Bui DM, Yoon Y, Huh EN, Jun S, Lee S (2017) Energy efficiency for cloud computing system based on predictive optimization. J Parallel Distrib Comput 102(4):103–114

    Article  Google Scholar 

  7. Chakraborty S, Rosen MA, MacDonald BD (2017) Analysis and feasibility of an evaporative cooling system with diffusion-based sessile droplet evaporation for cooling microprocessors. Appl Therm Eng 125(10):104–110

    Article  Google Scholar 

  8. Chase J, Niyato D (2017) Joint optimization of resource provisioning in cloud computing. IEEE Trans Serv Comput 10(3):396–409

    Article  Google Scholar 

  9. Chen F, Dou R, Li M, Wu H (2016) A flexible qos-aware web service composition method by multi-objective optimization in cloud manufacturing. Comput Ind Eng 99(9):423–431

    Article  Google Scholar 

  10. Cheng D, Rao J, Guo Y, Jiang C, Zhou X (2017) Improving performance of heterogeneous mapreduce clusters with adaptive task tuning. IEEE Trans Parallel Distrib Syst 28(3):774–786

    Article  Google Scholar 

  11. Fyrbiak M, Rokicki S, Bissantz N (2018) Hybrid obfuscation to protect against disclosure attacks on embedded microprocessors. IEEE Trans Comput 67(3):307–321

    Article  MathSciNet  MATH  Google Scholar 

  12. Hayyolalam V, Kazem AAP (2018) A systematic literature review on qos-aware service composition and selection in cloud environment. J Netw Comput Appl 110(5):52–74

    Article  Google Scholar 

  13. Kiani A, Ansari N (2018) Profit maximization for geographically dispersed green data centers. IEEE Trans Smart Grid 9(2):703–711

    Article  Google Scholar 

  14. Liu H, Zhang P, Hu B, Moore P (2015) A novel approach to task assignment in a cooperative multi-agent design system. Appl Intell 43(1):162–175

    Article  Google Scholar 

  15. Liu ZZ, Chu DH, Song C, Xue X, Lu BY (2016) Social learning optimization (slo) algorithm paradigm and its application in qos-aware cloud service composition. Inf Sci 326:315– 333

    Article  Google Scholar 

  16. Lu C, Gao L, Li X, Zeng B, Zhou F (2018) A hybrid multi-objective evolutionary algorithm with feedback mechanism. Appl Intell 48(11):4149–4173

    Article  Google Scholar 

  17. Perez-Rodriguez R, Hernandez-Aguirre A (2018) A hybrid estimation of distribution algorithm for flexible job-shop scheduling problems with process plan flexibility. Appl Intell 48(10):3707–3734

    Article  Google Scholar 

  18. Shi L, Zhang Z, Robertazzi T (2017) Energy-aware scheduling of embarrassingly parallel jobs and resource allocation in cloud. IEEE Trans Parallel Distrib Syst 28(6):1607–1620

    Article  Google Scholar 

  19. Sotiriadis S, Bessis N, Buyya R (2018) Self managed virtual machine scheduling in cloud systems. Inf Sci 433:381–400

    Article  Google Scholar 

  20. Tagliavini G, Rossi D, Marongiu A (2018) Synergistic hw/sw approximation techniques for ultralow-power parallel computing. IEEE Trans Comput Aided Des Integr Circuits Syst 37(5):982–995

    Google Scholar 

  21. Wang J, Gong B, Liu H, Li S (2015) Multidisciplinary approaches to artificial swarm intelligence for heterogeneous computing and cloud scheduling. Appl Intell 43(3):662–675

    Article  Google Scholar 

  22. Ye Z, Mistry S, Bouguettaya A, Dong H (2016) Long-term qos-aware cloud service composition using multivariate time series analysis. IEEE Trans Serv Comput 9(3):382–393

    Article  Google Scholar 

  23. Zhang P, Liu H, Ding Y (2014) Dynamic bee colony algorithm based on multi-species co-evolution. Appl Intell 40(3):427–440

    Article  Google Scholar 

  24. Zhang Z, Lang M, Pakin S, Fu S (2016) Tracsim: simulating and scheduling trapped power capacity to maximize machine room throughput. Parallel Comput 57(9):108–124

    Article  Google Scholar 

  25. Zhang Z, Hu F, Zhang N (2018) Ant colony algorithm for satellite control resource scheduling problem. Appl Intell 48(10):3295–3305

    Article  Google Scholar 

  26. Zhao H, Wang J, Liu F (2018) Power-aware and performance-guaranteed virtual machine placement in the cloud. IEEE Trans Parallel Distrib Syst 29(6):1385–1400

    Article  Google Scholar 

  27. Zheng W, Ma K, Wang X (2017) Hybrid energy storage with supercapacitor for cost-efficient data center power shaving and capping. IEEE Trans Parallel Distrib Syst 28(4):1105–1118

    Article  Google Scholar 

  28. Zhong W, Zhuang Y, Sun J, Gu J (2018) A load prediction model for cloud computing using pso-based weighted wavelet support vector machine. Appl Intell 48(11):4072–4083

    Article  Google Scholar 

  29. Zhou J, Yao X (2017) A hybrid approach combining modified artificial bee colony and cuckoo search algorithms for multi-objective cloud manufacturing service composition. Int J Prod Res 55(16):4765–4784

    Article  Google Scholar 

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Funding

This study was funded by National High Technology Research and Development Program of China (863 Program) (No.2012AA01A306) , National Science Foundation for Young Scholars of China (No.61702248) and Talent Introduction Project of Ludong University (No.LB2016015).

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Authors and Affiliations

  1. School of Information and Electrical Engineering, Ludong University, Yantai, China

    Jinglian Wang

  2. School of Computer Science and Technology, Shandong University, Jinan, China

    Jinglian Wang & Bin Gong

  3. School of Information Science and Technology, Shandong Normal University, Jinan, China

    Hong Liu & Shaohui Li

Authors
  1. Jinglian Wang
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  2. Bin Gong
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  3. Hong Liu
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  4. Shaohui Li
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Corresponding author

Correspondence to Jinglian Wang.

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Conflict of interests

Jinglian Wang declares that she has no conflict of interest. Bin Gong declares that he has no conflict of interest. Hong Liu declares that she has no conflict of interest. Shaohui Li declares that he has no conflict of interest.

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National High Technology Research and Development Program of China(863 Program) (No.2006AA01A113 and No.2012AA01A306) and National Science Foundation for Young Scholars of China (No.61702248) and Talent Introduction Project of Ludong University (No.LB2016015)

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Wang, J., Gong, B., Liu, H. et al. Intelligent scheduling with deep fusion of hardware-software energy-saving principles for greening stochastic nonlinear heterogeneous super-systems. Appl Intell 49, 3159–3172 (2019). https://doi.org/10.1007/s10489-019-01424-5

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  • DOI: https://doi.org/10.1007/s10489-019-01424-5

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