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


Heterogeneous distributed systems are such systems where heterogeneous processors are distributed in different locations and are inter-connected by networks. Heterogeneous distributed embedded systems and heterogeneous distributed cloud systems are typical scenarios of heterogeneous distributed systems. As advanced heterogeneous distributed systems, cyber-physical systems (CPS) further enhance the existing embedded and cloud systems. Specifically, Automotive CPS (ACPS) and cyber-physical cloud systems (CPCS) are the CPS applied to embedded and cloud areas, respectively. There are a large amount of parallel applications with precedence constraints in those heterogeneous distributed systems which can be described by a directed acyclic graph (DAG) at a high level. To make full use of multiprocessors on heterogeneous distributed systems, how to efficiently schedule DAG applications is an important research direction and worth studying on ACPS and CPCS. Moreover, there are many scheduling policies, such as energy-efficient scheduling, reliability-aware scheduling, and high-performance real-time scheduling.


  1. 1.
  2. 2.
  3. 5.
    Ab Rahman, N.H., Glisson, W.B., Yang, Y., Choo, K.K.R.: Forensic-by-design framework for cyber-physical cloud systems. IEEE Cloud Comput. 3(1), 50–59 (2016)CrossRefGoogle Scholar
  4. 7.
    Abrishami, S., Naghibzadeh, M., Epema, D.H.: Deadline-constrained workflow scheduling algorithms for infrastructure as a service clouds. Futur. Gener. Comput. Syst. 29(1), 158–169 (2013)CrossRefGoogle Scholar
  5. 9.
    Arabnejad, H., Barbosa, J.: Fairness resource sharing for dynamic workflow scheduling on heterogeneous systems. In: 2012 IEEE 10th International Symposium on Parallel and Distributed Processing with Applications, pp. 633–639. IEEE (2012)Google Scholar
  6. 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
  7. 19.
    Baruah, S., Li, H., Stougie, L.: Towards the design of certifiable mixed-criticality systems. In: Proceedings of the 2010 16th IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS), pp. 13–22. IEEE (2010)Google Scholar
  8. 24.
    Benoit, A., Hakem, M.: Optimizing the latency of streaming applications under throughput and reliability constraints. In: Proceedings of the International Conference on Parallel Processing, pp. 325–332. IEEE (2009)Google Scholar
  9. 25.
    Benoit, A., Hakem, M., Robert, Y.: Fault tolerant scheduling of precedence task graphs on heterogeneous platforms. In: Proceedings of the 22th IEEE International on Parallel and Distributed Processing, pp. 1–8. IEEE (2008)Google Scholar
  10. 33.
    Burns, A., Davis, R.: Mixed criticality systems-a review. Technical Report, Department of Computer Science, University of York, pp. 1–64 (2016).
  11. 34.
    Cai, Z., Li, X., Gupta, J.N.: Heuristics for provisioning services to workflows in XaaS clouds. IEEE Trans. Serv. Comput. 9(2), 250–263 (2016)CrossRefGoogle Scholar
  12. 35.
    Cai, Z., Li, X., Gupta, J.N.D.: Heuristics for provisioning services to workflows in XaaS clouds. IEEE Trans. Serv. Comput. 9(2), 250–263 (2016)CrossRefGoogle Scholar
  13. 36.
    Chakraborty, S., Faruque, M.A.A., Chang, W., Goswami, D.: Automotive cyber-physical systems: a tutorial introduction. IEEE Des. Test 33(4), 92–108 (2016)CrossRefGoogle Scholar
  14. 40.
    Chen, W., da Silva, R.F., Deelman, E., Fahringer, T.: Dynamic and fault-tolerant clustering for scientific workflows. IEEE Trans. Cloud Comput. 4(1), 49–62 (2016)CrossRefGoogle Scholar
  15. 42.
    Chen, X., Feng, J., Hiller, M., Lauer, V.: Application of software watchdog as a dependability software service for automotive safety relevant systems. In: Proceedings of the 37th IEEE/IFIP International Conference on Dependable Systems and Networks, pp. 618–624. IEEE (2007)Google Scholar
  16. 44.
    Dai, S., Koutsoukos, X.: Safety analysis of automotive control systems using multi-modal port-hamiltonian systems. In: Proceedings of the 19th International Conference on Hybrid Systems: Computation and Control, pp. 105–114. ACM (2016)Google Scholar
  17. 49.
    Fan, M., Quan, G.: Harmonic semi-partitioned scheduling for fixed-priority real-time tasks on multi-core platform. In: Proceedings of the Conference on Design, Automation and Test in Europe, pp. 503–508. EDA Consortium (2012)Google Scholar
  18. 51.
    Fu, Z., Huang, F., Sun, X., Vasilakos, A., Yang, C.N.: Enabling semantic search based on conceptual graphs over encrypted outsourced data. IEEE Trans. Serv. Comput. 1–1 (2016, in press).
  19. 52.
    Fürst, S.: Challenges in the design of automotive software. In: Proceedings of the Conference on Design, Automation and Test in Europe, pp. 256–258. European Design and Automation Association (2010)Google Scholar
  20. 57.
    Girault, A., Kalla, H.: A novel bicriteria scheduling heuristics providing a guaranteed global system failure rate. IEEE Trans. Dependable Secur. C. 6(4), 241–254 (2009)CrossRefGoogle Scholar
  21. 60.
    Goswami, D., Schneider, R., Masrur, A., Lukasiewycz, M., Chakraborty, S., Voit, H., Annaswamy, A.: Challenges in automotive cyber-physical systems design. In: 2012 International Conference on Embedded Computer Systems (SAMOS), pp. 346–354. IEEE (2012)Google Scholar
  22. 63.
    Guan, N., Stigge, M., Yi, W., Yu, G.: Fixed-priority multiprocessor scheduling with Liu and Layland’s utilization bound. In: Proceedings of the 2010 16th IEEE Real-Time and Embedded Technology and Applications Symposium, pp. 165–174. IEEE (2010)Google Scholar
  23. 67.
    Heinrich, P., Prehofer, C.: Network-wide energy optimization for adaptive embedded systems. ACM SIGBED Rev. 10(1), 33–36 (2013)CrossRefGoogle Scholar
  24. 69.
    Hsu, C.C., Huang, K.C., Wang, F.J.: Online scheduling of workflow applications in grid environments. Futur. Gener. Comput. Syst. 27(6), 860–870 (2011)CrossRefGoogle Scholar
  25. 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
  26. 72.
    ISO, I.: 26262–road vehicles-functional safety. ISO Standard (2011)Google Scholar
  27. 73.
    Karnouskos, S., Colombo, A.W., Bangemann, T.: Trends and challenges for cloud-based industrial cyber-physical systems. In: Industrial Cloud-Based Cyber-Physical Systems, pp. 231–240. Springer (2014)Google Scholar
  28. 75.
    Keahey, K., Raicu, I., Chard, K., Nicolae, B.: Guest editors introduction: special issue on scientific cloud computing. IEEE Trans. Cloud Comput. 4(1), 4–5 (2016)CrossRefGoogle Scholar
  29. 76.
    Khan, M.A.: Scheduling for heterogeneous systems using constrained critical paths. Parallel Comput. 38(4), 175–193 (2012)CrossRefGoogle Scholar
  30. 80.
    Kong, Y., Zhang, M., Ye, D.: A belief propagation-based method for task allocation in open and dynamic cloud environments. Knowl.-Based Syst. 115, 123–132 (2017). CrossRefGoogle Scholar
  31. 81.
    See Ref. [80]Google Scholar
  32. 82.
    Koslovski, G., Yeow, W.L., Westphal, C., Huu, T.T., Montagnat, J., Vicat-Blanc, P.: Reliability support in virtual infrastructures. In: Proceedings of the IEEE 2nd International Conference on Cloud Computing Technology and Science, pp. 49–58. IEEE (2010)Google Scholar
  33. 83.
    Kumar, P., Goswami, D., Chakraborty, S., Annaswamy, A., Lampka, K., Thiele, L.: A hybrid approach to cyber-physical systems verification. In: Proceedings of the 49th ACM/EDAC/IEEE Design Automation Conference, pp. 688–696. ACM (2012)Google Scholar
  34. 86.
    Lakshmanan, K., Kato, S., Rajkumar, R.: Scheduling parallel real-time tasks on multi-core processors. In: Real-Time Systems Symposium (RTSS), 2010 IEEE 31st, pp. 259–268. IEEE (2010)Google Scholar
  35. 87.
    Lakshmanan, K., Rajkumar, R.R., Lehoczky, J.P.: Partitioned fixed-priority preemptive scheduling for multi-core processors. In: Real-Time Systems, 2009. ECRTS’09. 21st Euromicro Conference on, pp. 239–248. IEEE (2009)Google Scholar
  36. 89.
    Lee, E.A., Seshia, S.A.: Introduction to embedded systems: a cyber-physical systems approach. Lee & Seshia, Lulu (2011)Google Scholar
  37. 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
  38. 92.
    Leu, J.S., Chen, C.F., Hsu, K.C.: Improving heterogeneous SOA-based IOT message stability by shortest processing time scheduling. IEEE Trans. Serv. Comput. 7(4), 575–585 (2014)CrossRefGoogle Scholar
  39. 94.
    Li, J., Ning, Z., Jedari, B., Xia, F., Lee, I., Tolba, A.: Geo-social distance-based data dissemination for socially aware networking. IEEE Access 4, 1444–1453 (2016)CrossRefGoogle Scholar
  40. 95.
    Li, J., Qiu, M., Ming, Z., Quan, G., Qin, X., Gu, Z.: Online optimization for scheduling preemptable tasks on IaaS cloud systems. J. Parallel Distrib. Comput. 72(5), 666–677 (2012)CrossRefGoogle Scholar
  41. 99.
    Li, K.: Power and performance management for parallel computations in clouds and data centers. J. Comput. Syst. Sci. 82(2), 174–190 (2016)MathSciNetzbMATHCrossRefGoogle Scholar
  42. 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
  43. 103.
    Liu, Q., Cai, W., Shen, J., Fu, Z., Liu, X., Linge, N.: A speculative approach to spatial-temporal efficiency with multi-objective optimization in a heterogeneous cloud environment. Secur. Commun. Netw. 9(17), 4002–4012 (2016). CrossRefGoogle Scholar
  44. 104.
    See Ref. [103]Google Scholar
  45. 108.
    Melani, A., Bertogna, M., Bonifaci, V., Marchetti-Spaccamela, A., Buttazzo, G.C.: Response-time analysis of conditional DAG tasks in multiprocessor systems. In: Real-Time Systems (ECRTS), 2015 27th Euromicro Conference on, pp. 211–221. IEEE (2015)Google Scholar
  46. 109.
    Mitchell, R., Chen, R.: Behavior rule specification-based intrusion detection for safety critical medical cyber physical systems. IEEE Trans. Dependable Secure Comput. 12(1), 16–30 (2015)CrossRefGoogle Scholar
  47. 111.
    Naghibzadeh, M.: Modeling and scheduling hybrid workflows of tasks and task interaction graphs on the cloud. Futur. Gener. Comput. Syst. 65, 33–45 (2016)CrossRefGoogle Scholar
  48. 112.
    Natale, M.D., Sangiovanni-Vincentelli, A.: Moving from federated to integrated architectures in automotive: the role of standards, methods and tools. Proc. IEEE 98(4), 603–620 (2010)CrossRefGoogle Scholar
  49. 114.
    Nilsson, J., Ödblom, A.C., Fredriksson, J.: Worst-case analysis of automotive collision avoidance systems. IEEE Trans. Veh. Technol. 65(4), 1899–1911 (2016)CrossRefGoogle Scholar
  50. 115.
    Ning, H., Liu, H., Ma, J., Yang, L.T., Huang, R.: Cybermatics: cyber–physical–social–thinking hyperspace based science and technology. Futur. Gener. Comput. Syst. 56, 504–522 (2016)CrossRefGoogle Scholar
  51. 117.
    NSF: Cyber-physical systems (cps). Program solicitation nsf 16-549. Website, pp. 1–21. (2016)
  52. 126.
    Qiu, W., Zheng, Z., Wang, X., Yang, X., Lyu, M.R.: Reliability-based design optimization for cloud migration. IEEE Trans. Serv. Comput. 7(2), 223–236 (2014)CrossRefGoogle Scholar
  53. 127.
    Ranjan, R., Wang, L., Zomaya, A.Y., Georgakopoulos, D., Sun, X.H., Wang, G.: Recent advances in autonomic provisioning of big data applications on clouds. IEEE Trans. Cloud Comput. 3(2), 101–104 (2015)CrossRefGoogle Scholar
  54. 128.
    Rodriguez, M.A., Buyya, R.: Deadline based resource provisioningand scheduling algorithm for scientific workflows on clouds. IEEE Trans. Cloud Comput. 2(2), 222–235 (2014)CrossRefGoogle Scholar
  55. 130.
    Saifullah, A., Li, J., Agrawal, K., Lu, C., Gill, C.: Multi-core real-time scheduling for generalized parallel task models. Real-Time Syst. 49(4), 404–435 (2013)zbMATHCrossRefGoogle Scholar
  56. 135.
    Silic, M., Delac, G., Srbljic, S.: Prediction of atomic web services reliability for QoS-aware recommendation. IEEE Trans. Serv. Comput. 8(3), 425–438 (2015)CrossRefGoogle Scholar
  57. 145.
    Tanaka, M., Tatebe, O.: Workflow scheduling to minimize data movement using multi-constraint graph partitioning. In: Proceedings of the 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012), pp. 65–72. IEEE Computer Society (2012)Google Scholar
  58. 147.
    Tang, X., Li, K., Liao, G.: An effective reliability-driven technique of allocating tasks on heterogeneous cluster systems. Cluster Comput. 17(4), 1413–1425 (2014)CrossRefGoogle Scholar
  59. 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
  60. 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
  61. 153.
    Ullman, J.D.: Np-complete scheduling problems. J. Comput. Syst. Sci. 10(3), 384–393 (1975)MathSciNetzbMATHCrossRefGoogle Scholar
  62. 154.
    Vasile, M.A., Pop, F., Tutueanu, R.I., Cristea, V., ołodziej, J.: Resource-aware hybrid scheduling algorithm in heterogeneous distributed computing. Futur. Gener. Comput. Syst. 51, 61–71 (2015)CrossRefGoogle Scholar
  63. 157.
    Wang, W., Wu, Q., Tan, Y., Wu, F.: Maximize throughput scheduling and cost-fairness optimization for multiple dags with deadline constraint. In: International Conference on Algorithms and Architectures for Parallel Processing, pp. 621–634. Springer (2015)Google Scholar
  64. 158.
    Wasicek, A., Derler, P., Lee, E.A.: Aspect-oriented modeling of attacks in automotive cyber-physical systems. In: Proceedings of the 51st ACM/EDAC/IEEE Design Automation Confrence, pp. 1–6. ACM (2014)Google Scholar
  65. 160.
    Wu, C.Q., Lin, X., Yu, D., Xu, W., Li, L.: End-to-end delay minimization for scientific workflows in clouds under budget constraint. IEEE Trans. Cloud Comput. 3(2), 169–181 (2015)CrossRefGoogle Scholar
  66. 162.
    Xie, G., Chen, Y., Liu, Y., Wei, Y., Li, R., Li, K.: Resource consumption cost minimization of reliable parallel applications on heterogeneous embedded systems. IEEE Trans. Ind. Informat. 13(4), 1629–1640 (2017)CrossRefGoogle Scholar
  67. 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
  68. 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). Google Scholar
  69. 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). CrossRefGoogle Scholar
  70. 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).
  71. 168.
    Xie, G., Zeng, G., Li, Z., Li, R., Li, K.: Adaptive dynamic scheduling on multi-functional mixed-criticality automotive cyber-physical systems. IEEE Trans. Veh. Technol. 66(8), 6676–6692 (2017)CrossRefGoogle Scholar
  72. 169.
    Xie, G., Zeng, G., Liu, L., Li, R., Li, K.: High performance real-time scheduling of multiple mixed-criticality functions in heterogeneous distributed embedded systems. J. Syst. Archit. 70, 3–14 (2016)CrossRefGoogle Scholar
  73. 170.
    Xie, G., Zeng, G., Liu, L., Li, R., Li, K.: Mixed real-time scheduling of multiple dags-based applications on heterogeneous multi-core processors. Microprocess. Microsyst. 47, 93–103 (2016)CrossRefGoogle Scholar
  74. 174.
    Yu, Z., Shi, W.: A planner-guided scheduling strategy for multiple workflow applications. In: 2008 International Conference on Parallel Processing-Workshops, pp. 1–8. IEEE (2008)Google Scholar
  75. 176.
    Zeller, M., Prehofer, C., Weiss, G., Eilers, D., Knorr, R.: Towards self-adaptation in real-time, networked systems: efficient solving of system constraints for automotive embedded systems. In: Proceedings of the 15th IEEE International Conference on Self-Adaptive and Self-Organizing Systems, pp. 79–88. IEEE (2011)Google Scholar
  76. 178.
    Zeng, H., Di Natale, M., Giusto, P., Sangiovanni-Vincentelli, A.: Stochastic analysis of can-based real-time automotive systems. IEEE Trans. Ind. Inf. 5(4), 388–401 (2009)CrossRefGoogle Scholar
  77. 179.
    Zeng, H., Natale, M.D., Ghosal, A., Sangiovanni-Vincentelli, A.: Schedule optimization of time-triggered systems communicating over the flexray static segment. IEEE Trans. Ind. Inform. 7(1), 1–17 (2011)CrossRefGoogle Scholar
  78. 180.
    Zeng, J., Yang, L.T., Lin, M., Ning, H., Ma, J.: A survey: cyber-physical-social systems and their system-level design methodology. Futur. Gener. Comput. Syst. (2016). Available online:
  79. 181.
    Zhang, F., Cao, J., Hwang, K., Li, K., Khan, S.U.: Adaptive workflow scheduling on cloud computing platforms with iterativeordinal optimization. IEEE Trans. Cloud Comput. 3(2), 156–168 (2015)CrossRefGoogle Scholar
  80. 182.
    Zhangjie, F., Xingming, S., Qi, L., Lu, Z., Jiangang, S.: Achieving efficient cloud search services: multi-keyword ranked search over encrypted cloud data supporting parallel computing. IEICE Trans. Commun. 98(1), 190–200 (2015)Google Scholar
  81. 185.
    Zhao, H., Sakellariou, R.: Scheduling multiple DAGs onto heterogeneous systems. In: Parallel and Distributed Processing Symposium, 2006. IPDPS 2006. 20th International, pp. 159–172. IEEE (2006)Google Scholar
  82. 186.
    Zhao, L., Ren, Y., Sakurai, K.: Reliable workflow scheduling with less resource redundancy. Parallel Comput. 39(10), 567–585 (2013)MathSciNetCrossRefGoogle Scholar
  83. 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
  84. 190.
    Zheng, Z., Zhou, T.C., Lyu, M., King, I.: Component ranking for fault-tolerant cloud applications. IEEE Trans. Serv. Comput. 5(4), 540–550 (2012)CrossRefGoogle Scholar
  85. 191.
    Zhou, A., Wang, S., Cheng, B., Zheng, Z., Yang, F., Chang, R., Lyu, M., Buyya, R.: Cloud service reliability enhancement via virtual machine placement optimization. IEEE Trans. Serv. Comput. 10(6), 902–913 (2016)CrossRefGoogle Scholar
  86. 192.
    Zhou, A.C., He, B., Liu, C.: Monetary cost optimizations for hosting workflow-as-a-service in IaaS clouds. IEEE Trans. Cloud Comput. 4(1), 34–48 (2016)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

Personalised recommendations