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Energy-Efficient Real-Time Scheduling

  • Guoqi Xie
  • Gang Zeng
  • Renfa Li
  • Keqin Li
Chapter

Abstract

For the heterogeneous distributed embedded systems, this chapter solves the problem of minimizing the energy consumption of a real-time parallel application by using the combined non-DVFS and global DVFS-enabled energy-efficient scheduling algorithms. The non-DVFS energy-efficient scheduling (NDES) algorithm is solved by introducing the concept of deadline slacks to reduce the energy consumption while satisfying the deadline constraint. The global DVFS-enabled energy-efficient scheduling (GDES) algorithm is presented by moving the tasks to the processor slacks that generate minimum dynamic energy consumptions. For heterogeneous distributed cloud systems, this chapter presents an energy-efficient processor merging (EPM) algorithm to turn off the most energy-consuming processor from the energy saving perspective, and a quick EPM (QEPM) algorithm to reduce the computation complexity of EPM. Finally, this chapter will give a large number of experiments to verify the validation and efficiency of proposed algorithms. For different heterogeneous distributed systems (heterogeneous distributed embedded systems and heterogeneous distributed cloud systems), this chapter presents different compared algorithms to evaluate the performance of proposed algorithms at different scales, parallelism, and heterogeneity degrees.

References

  1. 3.
  2. 11.
    Arabnejad, H., Barbosa, J.G.: List scheduling algorithm for heterogeneous systems by an optimistic cost table. IEEE Trans. Parallel Distrib. Syst. 25(3), 682–694 (2014)CrossRefGoogle Scholar
  3. 14.
    Bambagini, M., Marinoni, M., Aydin, H., Buttazzo, G.: Energy-aware scheduling for real-time systems: a survey. ACM Trans. Embed. Comput. Syst. 15(1), 303–307 (2016)CrossRefGoogle Scholar
  4. 15.
    Bansal, S., Kumar, P., Singh, K.: An improved duplication strategy for scheduling precedence constrained graphs in multiprocessor systems. IEEE Trans. Parallel Distrib. Syst. 14(6), 533–544 (2003)CrossRefGoogle Scholar
  5. 16.
    Barnett, J., et al.: Dynamic task-level voltage scheduling optimizations. IEEE Trans. Comput. 54(5), 508–520 (2005)CrossRefGoogle Scholar
  6. 20.
    Batalla, J.M., Kantor, M., Mavromoustakis, C.X., Skourletopoulos, G., Mastorakis, G.: A novel methodology for efficient throughput evaluation in virtualized routers. In: IEEE International Conference on Communications, pp. 6899–6905. IEEE (2015)Google Scholar
  7. 26.
    Bernat, G., Colin, A., Petters, S.M.: WCET analysis of probabilistic hard real-time systems. In: Proceedings of the 23rd IEEE Real-Time Systems Symposium, pp. 279–288. IEEE (2002)Google Scholar
  8. 32.
    Bunde, D.P.: Power-aware scheduling for makespan and flow. In: Proceedings of the 18th Annual ACM Symposium Parallelism in Algorithms and Architectures, pp. 190–196. ACM (2006)Google Scholar
  9. 39.
    Chen, S., Li, Z., Yang, B., Rudolph, G.: Quantum-inspired hyper-heuristics for energy-aware scheduling on heterogeneous computing systems. IEEE Trans. Parallel Distrib. Syst. 27(6), 1796–1810 (2016)CrossRefGoogle Scholar
  10. 43.
    Convolbo, M.W., Chou, J.: Cost-aware DAG scheduling algorithms for minimizing execution cost on cloud resources. J. Supercomput. 72(3), 985–1012 (2016)CrossRefGoogle Scholar
  11. 45.
    Davis, R.I., Burns, A.: A survey of hard real-time scheduling for multiprocessor systems. ACM Comput. Surv. (CSUR) 43(4), 35 (2011)CrossRefGoogle Scholar
  12. 50.
    Ferrandi, F., Lanzi, P.L., Pilato, C., Sciuto, D., Tumeo, A.: Ant colony heuristic for mapping and scheduling tasks and communications on heterogeneous embedded systems. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 29(6), 911–924 (2010)CrossRefGoogle Scholar
  13. 71.
    Huang, Q., Su, S., Li, J., Xu, P., Shuang, K., Huang, X.: Enhanced energy-efficient scheduling for parallel applications in cloud. In: Proceedings of the 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID 2012), pp. 781–786. IEEE Computer Society (2012)Google Scholar
  14. 74.
    Kashani, M.H., Jahanshahi, M.: Using simulated annealing for task scheduling in distributed systems. In: International Conference on Computational Intelligence, Modelling and Simulation, pp. 265–269. IEEE (2009)Google Scholar
  15. 84.
    Kuo, C.F., Lu, Y.F.: Task assignment with energy efficiency considerations for non-dvs heterogeneous multiprocessor systems. ACM Sigapp Appl. Comput. Rev. 14(4), 8–18 (2015)CrossRefGoogle Scholar
  16. 88.
    Langen, P.D., Juurlink, B.: Leakage-aware multiprocessor scheduling. J. Signal Process. Syst. 57(1), 73–88 (2009)CrossRefGoogle Scholar
  17. 90.
    Lee, Y.C., Zomaya, A.Y.: Energy conscious scheduling for distributed computing systems under different operating conditions. IEEE Trans. Parallel Distrib. Syst. 22(8), 1374–1381 (2011)CrossRefGoogle Scholar
  18. 97.
    Li, K.: Energy and time constrained task scheduling on multiprocessor computers with discrete speed levels. J. Parallel Distrib. Comput. 95, 15–28 (2016)CrossRefGoogle Scholar
  19. 98.
    Li, K.: Scheduling precedence constrained tasks with reduced processor energy on multiprocessor computers. IEEE Trans. Comput. 61(12), 1668–1681 (2012)MathSciNetCrossRefGoogle Scholar
  20. 102.
    Liu, J., Zhuge, Q., Gu, S., Hu, J., Zhu, G., Sha, E.H.M.: Minimizing system cost with efficient task assignment on heterogeneous multicore processors considering time constraint. IEEE Trans. Parallel Distrib. Syst. 25(8), 2101–2113 (2014)CrossRefGoogle Scholar
  21. 116.
    Niu, J., Liu, C., Gao, Y., Qiu, M.: Energy efficient task assignment with guaranteed probability satisfying timing constraints for embedded systems. IEEE Trans. Parallel Distrib. Syst. 25(8), 2043–2052 (2014)CrossRefGoogle Scholar
  22. 136.
    Singh, J., Betha, S., Mangipudi, B., Auluck, N.: Contention aware energy efficient scheduling on heterogeneous multiprocessors. IEEE Trans. Parallel Distrib. Syst. 26(5), 1251–1264 (2015)CrossRefGoogle Scholar
  23. 139.
    Swiecicka, A., Seredynski, F., Zomaya, A.Y.: Multiprocessor scheduling and rescheduling with use of cellular automata and artificial immune system support. IEEE Trans. Parallel Distrib. Syst. 17(3), 253–262 (2006)CrossRefGoogle Scholar
  24. 142.
    Tămaş-Selicean, D., Pop, P.: Design optimization of mixed-criticality real-time embedded systems. ACM Trans. Embed. Comput. Syst. 14(3), 50 (2015)CrossRefGoogle Scholar
  25. 148.
    Tang, Z., Qi, L., Cheng, Z., Li, K., Khan, S.U., Li, K.: An energy-efficient task scheduling algorithm in DVFS-enabled cloud environment. J. Grid Comput. 14(1), 55–74 (2016)CrossRefGoogle Scholar
  26. 149.
    Tarplee, K.M., Friese, R., Maciejewski, A.A., Siegel, H.J., Chong, E.K.: Energy and makespan tradeoffs in heterogeneous computing systems using efficient linear programming techniques. IEEE Trans. Parallel Distrib. Syst. 27(6), 1633–1646 (2016)CrossRefGoogle Scholar
  27. 150.
    Thanavanich, T., Uthayopas, P.: Efficient energy aware task scheduling for parallel workflow tasks on hybrids cloud environment. In: International Computer Science Engineering Conference, pp. 37–42. IEEE (2013)Google Scholar
  28. 152.
    Topcuoglu, H., Hariri, S., Wu, M.Y.: Performance-effective and low-complexity task scheduling for heterogeneous computing. IEEE Trans. Parallel Distrib. Syst. 13(3), 260–274 (2002)CrossRefGoogle Scholar
  29. 159.
    Wu, A.S., Yu, H., Jin, S., Lin, K.C., Schiavone, G.: An incremental genetic algorithm approach to multiprocessor scheduling. IEEE Trans. Parallel Distrib. Syst. 15(9), 824–834 (2004)CrossRefGoogle Scholar
  30. 161.
    Xiao, X., Xie, G., Li, R., Li, K.: Minimizing schedule length of energy consumption constrained parallel applications on heterogeneous distributed systems. In: Proceedings of the 14th IEEE International Symposium on Parallel Distributed Processing with Applications, pp. 1471–1476. IEEE Computer Society (2016)Google Scholar
  31. 163.
    Xie, G., Li, R., Li, K.: Heterogeneity-driven end-to-end synchronized scheduling for precedence constrained tasks and messages on networked embedded systems. J. Parallel Distrib. Comput. 83, 1–12 (2015)CrossRefGoogle Scholar
  32. 164.
    Xie, G., Liu, L., Yang, L., Li, R.: Scheduling trade-off of dynamic multiple parallel workflows on heterogeneous distributed computing systems. Concurr. Comput. Pract. Exp. 29(8), 1–18 (2017).  https://doi.org/10.1002/cpe.3782 Google Scholar
  33. 165.
    Xie, G., Xiao, X., Li, R., Li, K.: Schedule length minimization of parallel applications with energy consumption constraints using heuristics on heterogeneous distributed systems. Concurr. Comput. Pract. Exp. 1–10 (2016).  https://doi.org/10.1002/cpe.4024 CrossRefGoogle Scholar
  34. 166.
    Xie, G., Zeng, G., Chen, Y., Bai, Y., Zhou, Z., Li, R., Li, K.: Minimizing redundancy to satisfy reliability requirement for a parallel application on heterogeneous service-oriented systems. IEEE Trans. Serv. Comput. 1–1 (2017).  https://doi.org/10.1109/TSC.2017.2665552
  35. 171.
    Xie, Y., Zeng, G., Chen, Y., Kurachi, R., Takada, H., Li, R.: Worst case response time analysis for messages in controller area network with gateway. IEICE Trans. Inf. Syst. 96(7), 1467–1477 (2013)CrossRefGoogle Scholar
  36. 173.
    Xu, Y., Li, K., He, L., Zhang, L., Li, K.: A hybrid chemical reaction optimization scheme for task scheduling on heterogeneous computing systems. IEEE Trans. Parallel Distrib. Syst. 26(12), 3208–3222 (2015)CrossRefGoogle Scholar
  37. 177.
    Zeng, G., Matsubara, Y., Tomiyama, H., Takada, H.: Energy-aware task migration for multiprocessor real-time systems. Futur. Gener. Comput. Syst. 56, 220–228 (2016)CrossRefGoogle Scholar
  38. 183.
    Zhao, B., Aydin, H., Zhu, D.: On maximizing reliability of real-time embedded applications under hard energy constraint. IEEE Trans. Ind. Inf. 6(3), 316–328 (2010)CrossRefGoogle Scholar
  39. 184.
    Zhao, B., Aydin, H., Zhu, D.: Shared recovery for energy efficiency and reliability enhancements in real-time applications with precedence constraints. ACM Trans. Des. Autom. Electron. Syst. (TODAES) 18(2), 23 (2013)CrossRefGoogle Scholar
  40. 186.
    Zhao, L., Ren, Y., Sakurai, K.: Reliable workflow scheduling with less resource redundancy. Parallel Comput. 39(10), 567–585 (2013)MathSciNetCrossRefGoogle Scholar
  41. 187.
    Zhao, L., Ren, Y., Xiang, Y., Sakurai, K.: Fault-tolerant scheduling with dynamic number of replicas in heterogeneous systems. In: Proceedings of the 12th IEEE International Conference on High Performance Computing and Communications, pp. 434–441. IEEE (2010)Google Scholar
  42. 193.
    Zhu, D., Aydin, H.: Reliability-aware energy management for periodic real-time tasks. IEEE Trans. Comput. 58(10), 1382–1397 (2009)MathSciNetCrossRefGoogle Scholar
  43. 195.
    Zhuravlev, S., Saez, J.C., Blagodurov, S., Fedorova, A., Prieto, M.: Survey of energy-cognizant scheduling techniques. IEEE Trans. Parallel Distrib. Syst. 24(7), 1447–1464 (2013)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Guoqi Xie
    • 1
  • Gang Zeng
    • 2
  • Renfa Li
    • 3
  • Keqin Li
    • 4
  1. 1.College of Computer Science and Electronic EngineeringHunan UniversityChangshaChina
  2. 2.Graduate School of EngineeringNagoya UniversityNagoyaJapan
  3. 3.Key Laboratory for Embedded and Cyber-Physical Systems of Hunan ProvinceHunan UniversityChangshaChina
  4. 4.Department of Computer ScienceState University of New YorkNew PaltzUSA

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