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Workload Scheduling in Fog and Cloud Environments: Emerging Concepts and Research Directions

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Advances in Computing, Informatics, Networking and Cybersecurity

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 289))

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Abstract

In recent years, we have been witnessing the growing adoption of infrastructure virtualization technologies and cloud computing. A wide range of applications has been migrated from traditional computing environments to the cloud. On the other hand, organizations with existing on-premises infrastructure investments are making the shift to hybrid cloud, in order to leverage the security provided by the private cloud and the virtually unlimited resources of the public cloud. With the rapid expansion of the Internet of Things, fog computing emerged as a new paradigm, extending the cloud to the network edge, closer to where the data are generated. The workloads on such platforms tend to be complex, featuring various degrees of parallelism. Consequently, one of the major challenges involved with fog and cloud computing, is the effective and efficient scheduling of the workload. In this chapter, we provide the necessary background in this field and present an overview of the emerging concepts and techniques, exploring future research directions.

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References

  1. Aburukba, R.O., AliKarrar, M., Landolsi, T., El-Fakih, K.: Scheduling Internet of Things requests to minimize latency in hybrid fog-cloud computing. Future Gener. Comput. Syst. 111, 539–551 (2020). https://doi.org/10.1016/j.future.2019.09.039

    Article  Google Scholar 

  2. Ali, I.M., Sallam, K.M., Moustafa, N., Chakraborty, R., Ryan, M.J., Choo, K.K.R.: An automated task scheduling model using non-dominated sorting genetic algorithm II for fog-cloud systems. IEEE Trans. Cloud Comput. 1–15 (2020). https://doi.org/10.1109/TCC.2020.3032386

  3. Amiri, M.J., Maiyya, S., Agrawal, D., Abbadi, A.E.: SeeMoRe: a fault-tolerant protocol for hybrid cloud environments. In: Proceedings of the IEEE 36th International Conference on Data Engineering (ICDE’20), pp. 1345–1356 (2020). https://doi.org/10.1109/ICDE48307.2020.00120

  4. Anand, A., Chaudhary, A., Arvindhan, M.: The need for virtualization: when and why virtualization took over physical servers. In: Proceedings of the First International Conference on Advanced Communication & Computational Technology (ICACCT’19), pp. 1351–1359 (2019). https://doi.org/10.1007/978-981-15-5341-7_102

  5. Auluck, N., Rana, O., Nepal, S., Jones, A., Singh, A.: Scheduling real time security aware tasks in fog networks. IEEE Trans. Serv. Comput. 1–14 (2019). https://doi.org/10.1109/TSC.2019.2914649

  6. Beloglazov, A., Abawajy, J., Buyya, R.: Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Future Gener. Comput. Syst. 28(5), 755–768 (2012). https://doi.org/10.1016/j.future.2011.04.017

    Article  Google Scholar 

  7. Bittencourt, L.F., Goldman, A., Madeira, E.R.M., da Fonseca, N.L.S., Sakellariou, R.: Scheduling in distributed systems: a cloud computing perspective. Comput. Sci. Rev. 30, 31–54 (2018). https://doi.org/10.1016/j.cosrev.2018.08.002

    Article  Google Scholar 

  8. Bittencourt, L.F., Madeira, E.R.M.: HCOC: a cost optimization algorithm for workflow scheduling in hybrid clouds. J. Internet Serv. Appl. 2, 207–227 (2011). https://doi.org/10.1007/s13174-011-0032-0

    Article  Google Scholar 

  9. Bittencourt, L.F., Madeira, E.R.M., da Fonseca, N.L.S.: Scheduling in hybrid clouds. IEEE Commun. Mag. 50(9), 42–47 (2012). https://doi.org/10.1109/MCOM.2012.6295710

    Article  Google Scholar 

  10. Bonomi, F., Milito, R., Zhu, J., Addepalli, S.: Fog computing and its role in the Internet of Things. In: Proceedings of the First Edition of the MCC Workshop on Mobile Cloud Computing (MCC’12), pp. 13–16 (2012). https://doi.org/10.1145/2342509.2342513

  11. Bonomi, L.: Fog vs edge computing. Tech. Rep. 1.1.01, Nebbiolo Technologies Inc. (2019)

    Google Scholar 

  12. Buttazzo, G.C.: Hard Real-Time Computing Systems: Predictable Scheduling Algorithms and Applications, 3rd edn. Springer (2011). https://doi.org/10.1007/978-1-4614-0676-1

  13. Calheiros, R.N., Buyya, R.: Cost-effective provisioning and scheduling of deadline-constrained applications in hybrid clouds. In: Proceedings of the 13th International Conference on Web Information Systems Engineering (WISE’12), pp. 171–184 (2012). https://doi.org/10.1007/978-3-642-35063-4_13

  14. Chen, Y.: Service-Oriented Computing and System Integration: Software, IoT, Big Data, and AI as Services, 6th edn. Kendall Hunt Publishing (2018)

    Google Scholar 

  15. Chen, Y., Tsai, W.T.: Service-Oriented Computing and Web Software Integration: From Principles to Development, 5th edn. Kendall Hunt Publishing (2015)

    Google Scholar 

  16. Chunlin, L., Jianhang, T., Youlong, L.: Hybrid cloud adaptive scheduling strategy for heterogeneous workloads. J. Grid Comput. 17, 419–446 (2019). https://doi.org/10.1007/s10723-019-09481-3

    Article  Google Scholar 

  17. Cisco: Fog computing and the Internet of Things: extend the cloud to where the things are. Tech. Rep. C11-734435-00, Cisco Systems, Inc. (2015)

    Google Scholar 

  18. Dean, J., Ghemawat, S.: MapReduce: simplified data processing on large clusters. Commun. ACM 51(1), 107–113 (2008). https://doi.org/10.1145/1327452.1327492

  19. Deng, R., Lu, R., Lai, C., Luan, T.H., Liang, H.: Optimal workload allocation in fog-cloud computing toward balanced delay and power consumption. IEEE Internet Things J. 3(6), 1171–1181 (2016). https://doi.org/10.1109/JIOT.2016.2565516

    Article  Google Scholar 

  20. Drozdowski, M.: Scheduling for Parallel Processing, 1st edn. Springer (2009). https://doi.org/10.1007/978-1-84882-310-5

  21. Ekanayake, J., Fox, G.: High performance parallel computing with clouds and cloud technologies. In: Proceedings of the First International Conference on Cloud Computing (CloudComp’09), pp. 20–38 (2009)

    Google Scholar 

  22. El Kafhali, S., Salah, K.: Efficient and dynamic scaling of fog nodes for IoT devices. J. Supercomput. 73, 5261–5284 (2017). https://doi.org/10.1007/s11227-017-2083-x

    Article  Google Scholar 

  23. Enguehard, M., Carofiglio, G., Rossi, D.: A popularity-based approach for effective cloud offload in fog deployments. In: Proceedings of the 30th International Teletraffic Congress (ITC’18), pp. 55–63 (2018). https://doi.org/10.1109/ITC30.2018.00016

  24. Galambos, P.: Cloud, fog, and mist computing: advanced robot applications. IEEE Syst. Man Cybern. Mag. 6(1), 41–45 (2020). https://doi.org/10.1109/MSMC.2018.2881233

    Article  Google Scholar 

  25. Ghobaei-Arani, M., Souri, A., Rahmanian, A.A.: Resource management approaches in fog computing: a comprehensive review. J. Grid Comput. 18, 1–42 (2020). https://doi.org/10.1007/s10723-019-09491-1

    Article  Google Scholar 

  26. Ghose, M., Kaur, S., Sahu, A.: Scheduling real time tasks in an energy-efficient way using VMs with discrete compute capacities. Computing 102, 263–294 (2020). https://doi.org/10.1007/s00607-019-00738-z

    Article  MathSciNet  MATH  Google Scholar 

  27. Gouda, O.M., Hejji, D.J., Obaidat, M.S.: Privacy assessment of fitness tracker devices. In: Proceedings of the 2020 International Conference on Computer, Information and Telecommunication Systems (CITS’20), pp. 1–8 (2020). https://doi.org/10.1109/CITS49457.2020.9232503

  28. Grama, A., Gupta, A., Karypis, G., Kumar, V.: Introduction to Parallel Computing, 2nd edn. Addison-Wesley (2003)

    Google Scholar 

  29. Iorga, M., Feldman, L., Barton, R., Martin, M.J., Goren, N., Mahmoudi, C.: Fog computing conceptual model. Tech. Rep. 500-325, National Institute of Standards and Technology, U.S. Department of Commerce (2018). https://doi.org/10.6028/NIST.SP.500-325

  30. Jararweh, Y., Doulat, A., AlQudah, O., Ahmed, E., Al-Ayyoub, M., Benkhelifa, E.: The future of mobile cloud computing: integrating cloudlets and mobile edge computing. In: Proceedings of the 23rd International Conference on Telecommunications (ICT’16), pp. 1–5 (2016). https://doi.org/10.1109/ICT.2016.7500486

  31. Jiang, H.J., Huang, K.C., Chang, H.Y., Gu, D.S., Shih, P.J.: Scheduling concurrent workflows in HPC cloud through exploiting schedule gaps. In: Proceedings of the 11th International Conference on Algorithms and Architectures for Parallel Processing (ICA3PP’11), pp. 282–293 (2011). https://doi.org/10.1007/978-3-642-24650-0_24

  32. Khan, A.A., Zakarya, M., Khan, R., Rahman, I.U., Khan, M., Khan, A.U.R.: An energy, performance efficient resource consolidation scheme for heterogeneous cloud datacenters. J. Netw. Comput. Appl. 150, 102497 (2020). https://doi.org/10.1016/j.jnca.2019.102497

  33. Khayer, A., Talukder, M.S., Bao, Y., Hossain, M.N.: Cloud computing adoption and its impact on SMEs’ performance for cloud supported operations: a dual-stage analytical approach. Technol. Soc. 60, 101225 (2020). https://doi.org/10.1016/j.techsoc.2019.101225

  34. Kołodziej, J.: Evolutionary Hierarchical Multi-Criteria Metaheuristics for Scheduling in Large-Scale Grid Systems, 1st edn. Springer (2012). https://doi.org/10.1007/978-3-642-28971-2

  35. Kruatrachue, B., Lewis, T.G.: Duplication scheduling heuristic, a new precedence task scheduler for parallel systems. Tech. Rep. 87-60-3, Oregon State University (1987)

    Google Scholar 

  36. Li, G., Yan, J., Chen, L., Wu, J., Lin, Q., Zhang, Y.: Energy consumption optimization with a delay threshold in cloud-fog cooperation computing. IEEE Access 7, 159688–159697 (2019). https://doi.org/10.1109/ACCESS.2019.2950443

    Article  Google Scholar 

  37. Li, Y., Xia, Y.: Auto-scaling web applications in hybrid cloud based on docker. In: Proceedings of the 5th International Conference on Computer Science and Network Technology (ICCSNT’16), pp. 75–79 (2016). https://doi.org/10.1109/ICCSNT.2016.8070122

  38. Lin, K.J., Natarajan, S., Liu, J.W.S.: Imprecise results: utilizing partial computations in real-time systems. In: Proceedings of the 8th IEEE Real-Time Systems Symposium (RTSS’87), pp. 210–217 (1987)

    Google Scholar 

  39. Luo, J., Yin, L., Hu, J., Wang, C., Liu, X., Fan, X., Luo, H.: Container-based fog computing architecture and energy-balancing scheduling algorithm for energy IoT. Future Gener. Comput. Syst. 97, 50–60 (2019). https://doi.org/10.1016/j.future.2018.12.063

    Article  Google Scholar 

  40. Maheswaran, M., Ali, S., Siegel, H.J., Hensgen, D., Freund, R.F.: Dynamic mapping of a class of independent tasks onto heterogeneous computing systems. J. Parallel Distrib. Comput. 59(2), 107–131 (1999). https://doi.org/10.1006/jpdc.1999.1581

    Article  Google Scholar 

  41. Mavridis, I., Karatza, H.: Combining containers and virtual machines to enhance isolation and extend functionality on cloud computing. Future Gener. Comput. Syst. 94, 674–696 (2019). https://doi.org/10.1016/j.future.2018.12.035

    Article  Google Scholar 

  42. Mell, P., Grance, T.: The NIST definition of cloud computing. Tech. Rep. 800-145, National Institute of Standards and Technology, U.S. Department of Commerce (2011). https://doi.org/10.6028/NIST.SP.800-145

  43. Mhedheb, Y., Jrad, F., Tao, J., Zhao, J., Kołodziej, J., Streit, A.: Load and thermal-aware VM scheduling on the cloud. In: Proceedings of the 13th International Conference on Algorithms and Architectures for Parallel Processing (ICA3PP’13), pp. 101–114 (2013). https://doi.org/10.1007/978-3-319-03859-9_8

  44. Naha, R.K., Garg, S., Chan, A., Battula, S.K.: Deadline-based dynamic resource allocation and provisioning algorithms in fog-cloud environment. Future Gener. Comput. Syst. 104, 131–141 (2020). https://doi.org/10.1016/j.future.2019.10.018

    Article  Google Scholar 

  45. Obaidat, M.S., Nicopolitidis, P.: Smart Cities and Homes: Key Enabling Technologies, 1st edn. Morgan Kaufmann Publishers Inc. (2016)

    Google Scholar 

  46. OpenFog: OpenFog Architecture Overview. Tech. Rep. OPFWP001.0216, OpenFog Consortium Architecture Working Group (2016)

    Google Scholar 

  47. Pham, X.Q., Huh, E.N.: Towards task scheduling in a cloud-fog computing system. In: Proceedings of the 18th Asia-Pacific Network Operations and Management Symposium (APNOMS’16), pp. 1–4 (2016). https://doi.org/10.1109/APNOMS.2016.7737240

  48. Pham, X.Q., Man, N.D., Tri, N.D.T., Thai, N.Q., Huh, E.N.: A cost- and performance-effective approach for task scheduling based on collaboration between cloud and fog computing. Int. J. Distrib. Sens. Netw. 13(11), 1–16 (2017). https://doi.org/10.1177/1550147717742073

    Article  Google Scholar 

  49. Puliafito, C., Mingozzi, E., Longo, F., Puliafito, A., Rana, O.: Fog computing for the Internet of Things: a survey. ACM Trans. Internet Technol. 19(2), 18:1–18:41 (2019). https://doi.org/10.1145/3301443

  50. Ramakrishnan, J., Shabbir, M.S., Kassim, N.M., Nguyen, P.T., Mavaluru, D.: A comprehensive and systematic review of the network virtualization techniques in the IoT. Int. J. Commun. Syst. 33(7), e4331 (2020). https://doi.org/10.1002/dac.4331

  51. Ramirez, Y.M., Podolskiy, V., Gerndt, M.: Capacity-driven scaling schedules derivation for coordinated elasticity of containers and virtual machines. In: Proceedings of the 2019 IEEE International Conference on Autonomic Computing (ICAC’19), pp. 177–186 (2019). https://doi.org/10.1109/ICAC.2019.00029

  52. Ren, J., Zhang, D., He, S., Zhang, Y., Li, T.: A survey on end-edge-cloud orchestrated network computing paradigms: transparent computing, mobile edge computing, fog computing, and cloudlet. ACM Comput. Surv. 52(6), 125:1–125:36 (2019). https://doi.org/10.1145/3362031

  53. Sahoo, J., Mohapatra, S., Lath, R.: Virtualization: a survey on concepts, taxonomy and associated security issues. In: Proceedings of the Second International Conference on Computer and Network Technology (ICCNT’10), pp. 222–226 (2010). https://doi.org/10.1109/ICCNT.2010.49

  54. Shah-Mansouri, H., Wong, V.W.S.: Hierarchical fog-cloud computing for IoT systems: a computation offloading game. IEEE Internet Things J. 5(4), 3246–3257 (2018). https://doi.org/10.1109/JIOT.2018.2838022

  55. Stamatakis, A., Ott, M.: Exploiting fine-grained parallelism in the phylogenetic likelihood function with MPI, Pthreads, and OpenMP: a performance study. In: Proceedings of the Third IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB’08), pp. 424–435 (2008). https://doi.org/10.1007/978-3-540-88436-1_3

  56. Stavrinides, G.L., Karatza, H.D.: Fault-tolerant gang scheduling in distributed real-time systems utilizing imprecise computations. Simul.: Trans. Soc. Model. Simul. Int. 85(8), 525–536 (2009). https://doi.org/10.1177/0037549709340729

  57. Stavrinides, G.L., Karatza, H.D.: The impact of input error on the scheduling of task graphs with imprecise computations in heterogeneous distributed real-time systems. In: Proceedings of the 18th International Conference on Analytical and Stochastic Modelling Techniques and Applications (ASMTA’11), pp. 273–287 (2011). https://doi.org/10.1007/978-3-642-21713-5_20

  58. Stavrinides, G.L., Karatza, H.D.: The impact of data locality on the performance of a SaaS cloud with real-time data-intensive applications. In: Proceedings of the 21st IEEE/ACM International Symposium on Distributed Simulation and Real Time Applications (DS-RT’17), pp. 1–8 (2017). https://doi.org/10.1109/DISTRA.2017.8167683

  59. Stavrinides, G.L., Karatza, H.D.: Energy-aware scheduling of real-time workflow applications in clouds utilizing DVFS and approximate computations. In: Proceedings of the IEEE 6th International Conference on Future Internet of Things and Cloud (FiCloud’18), pp. 33–40 (2018). https://doi.org/10.1109/FiCloud.2018.00013

  60. Stavrinides, G.L., Karatza, H.D.: The impact of checkpointing interval selection on the scheduling performance of real-time fine-grained parallel applications in SaaS clouds under various failure probabilities. Concurr. Comput. Pract. Exp. 30(12), e4288 (2018). https://doi.org/10.1002/cpe.4288

  61. Stavrinides, G.L., Karatza, H.D.: Scheduling data-intensive workloads in large-scale distributed systems: trends and challenges. Studies in Big Data, vol. 36, 1st edn., chap. 2, pp. 19–43. Springer (2018). https://doi.org/10.1007/978-3-319-73767-6_2

  62. Stavrinides, G.L., Karatza, H.D.: Cost-effective utilization of complementary cloud resources for the scheduling of real-time workflow applications in a fog environment. In: Proceedings of the 7th International Conference on Future Internet of Things and Cloud (FiCloud’19), pp. 1–8 (2019). https://doi.org/10.1109/FiCloud.2019.00009

  63. Stavrinides, G.L., Karatza, H.D.: An energy-efficient, QoS-aware and cost-effective scheduling approach for real-time workflow applications in cloud computing systems utilizing DVFS and approximate computations. Future Gener. Comput. Syst. 96, 216–226 (2019). https://doi.org/10.1016/j.future.2019.02.019

    Article  Google Scholar 

  64. Stavrinides, G.L., Karatza, H.D.: A hybrid approach to scheduling real-time IoT workflows in fog and cloud environments. Multimed. Tools Appl. 78(17), 24639–24655 (2019). https://doi.org/10.1007/s11042-018-7051-9

    Article  Google Scholar 

  65. Stavrinides, G.L., Karatza, H.D.: Scheduling different types of gang jobs in distributed systems. In: Proceedings of the 2019 International Conference on Computer, Information and Telecommunication Systems (CITS’19), pp. 1–5 (2019). https://doi.org/10.1109/CITS.2019.8862091

  66. Stavrinides, G.L., Karatza, H.D.: Cost-aware cloud bursting in a fog-cloud environment with real-time workflow applications. Concurr. Comput. Pract. Exp. e5850 (2020). https://doi.org/10.1002/cpe.5850

  67. Stavrinides, G.L., Karatza, H.D.: Dynamic scheduling of bags-of-tasks with sensitive input data and end-to-end deadlines in a hybrid cloud. Multimed. Tools Appl. 1–23 (2020). https://doi.org/10.1007/s11042-020-08974-8

  68. Stavrinides, G.L., Karatza, H.D.: Orchestration of real-time workflows with varying input data locality in a heterogeneous fog environment. In: Proceedings of the Fifth International Conference on Fog and Mobile Edge Computing (FMEC’20), pp. 202–209 (2020). https://doi.org/10.1109/FMEC49853.2020.9144824

  69. Stavrinides, G.L., Karatza, H.D.: Scheduling real-time bag-of-tasks applications with approximate computations in SaaS clouds. Concurr. Comput. Pract. Exp. 32(1), e4208 (2020). https://doi.org/10.1002/cpe.4208

  70. Stavrinides, G.L., Karatza, H.D.: Weighted scheduling of mixed gang jobs on distributed resources. In: Proceedings of the 2020 International Conference on Communications, Computing, Cybersecurity and Informatics (CCCI’20), pp. 1–6 (2020). https://doi.org/10.1109/CCCI49893.2020.9256505

  71. Sun, L., Dong, H., Hussain, O.K., Hussain, F.K., Liu, A.X.: A framework of cloud service selection with criteria interactions. Future Gener. Comput. Syst. 94, 749–764 (2019). https://doi.org/10.1016/j.future.2018.12.005

    Article  Google Scholar 

  72. Surbiryala, J., Rong, C.: Cloud computing: history and overview. In: Proceedings of the 2019 IEEE Cloud Summit (CS’19), pp. 1–7 (2019). https://doi.org/10.1109/CloudSummit47114.2019.00007

  73. Tabak, E.K., Cambazoglu, B.B., Aykanat, C.: Improving the performance of independent task assignment heuristics MinMin, MaxMin and Sufferage. IEEE Trans. Parallel Distrib. Syst. 25(5), 1244–1256 (2014). https://doi.org/10.1109/TPDS.2013.107

    Article  Google Scholar 

  74. Talaat, M., Alsayyari, A.S., Alblawi, A., Hatata, A.Y.: Hybrid-cloud-based data processing for power system monitoring in smart grids. Sustain. Cities Soc. 55, 102049 (2020). https://doi.org/10.1016/j.scs.2020.102049

  75. 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). https://doi.org/10.1109/71.993206

    Article  Google Scholar 

  76. Van den Bossche, R., Vanmechelen, K., Broeckhove, J.: Cost-optimal scheduling in hybrid IaaS clouds for deadline constrained workloads. In: Proceedings of the IEEE 3rd International Conference on Cloud Computing (CLOUD’10), pp. 228–235 (2010). https://doi.org/10.1109/CLOUD.2010.58

  77. Varela, M., Skorin-Kapov, L., Ebrahimi, T.: Quality of service versus quality of experience, 1st edn., chap. 6. T-Labs Series in Telecommunication Services, pp. 85–96. Springer (2014). https://doi.org/10.1007/978-3-319-02681-7_6

  78. Voorsluys, W., Broberg, J., Buyya, R.: Introduction to cloud computing, 1st edn., chap. 1, pp. 1–41. Wiley (2011). https://doi.org/10.1002/9780470940105.ch1

  79. Wang, B., Song, Y., Sun, Y., Liu, J.: Managing deadline-constrained bag-of-tasks jobs on hybrid clouds. In: Proceedings of the 24th High Performance Computing Symposium (HPC’16), pp. 1–8 (2016). https://doi.org/10.22360/SpringSim.2016.HPC.039

  80. Wang, W.J., Chang, Y.S., Lo, W.T., Lee, Y.K.: Adaptive scheduling for parallel tasks with QoS satisfaction for hybrid cloud environments. J. Supercomput. 66(2), 783–811 (2013). https://doi.org/10.1007/s11227-013-0890-2

    Article  Google Scholar 

  81. Weng, C., Lu, X.: Heuristic scheduling for bag-of-tasks applications in combination with QoS in the computational grid. Future Gener. Comput. Syst. 21(2), 271–280 (2005). https://doi.org/10.1016/j.future.2003.10.004

    Article  Google Scholar 

  82. Wu, H.Y., Lee, C.R.: Energy efficient scheduling for heterogeneous fog computing architectures. In: Proceedings of the 42nd IEEE Annual Computer Software and Applications Conference (COMPSAC’18), pp. 555–560 (2018). https://doi.org/10.1109/COMPSAC.2018.00085

  83. Xu, J., Hao, Z., Zhang, R., Sun, X.: A method based on the combination of laxity and ant colony system for cloud-fog task scheduling. IEEE Access 7, 116218–116226 (2019). https://doi.org/10.1109/ACCESS.2019.2936116

    Article  Google Scholar 

  84. Yang, T., Gerasoulis, A.: DSC: scheduling parallel tasks on an unbounded number of processors. IEEE Trans. Parallel Distrib. Syst. 5(9), 951–967 (1994). https://doi.org/10.1109/71.308533

    Article  Google Scholar 

  85. Zhang, Y., Zhou, J., Sun, J.: Scheduling bag-of-tasks applications on hybrid clouds under due date constraints. J. Syst. Archit. 101, 101654 (2019). https://doi.org/10.1016/j.sysarc.2019.101654

  86. Zhang, Y., Zhou, J., Sun, L., Mao, J., Sun, J.: A novel firefly algorithm for scheduling bag-of-tasks applications under budget constraints on hybrid clouds. IEEE Access 7, 151888–151901 (2019). https://doi.org/10.1109/ACCESS.2019.2948468

    Article  Google Scholar 

  87. Zhou, X., Zhang, G., Sun, J., Zhou, J., Wei, T., Hu, S.: Minimizing cost and makespan for workflow scheduling in cloud using fuzzy dominance sort based HEFT. Future Gener. Comput. Syst. 93, 278–289 (2019). https://doi.org/10.1016/j.future.2018.10.046

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Informatics, Aristotle University of Thessaloniki, 54124, Thessaloniki, Greece

    Georgios L. Stavrinides & Helen D. Karatza

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  1. Georgios L. Stavrinides
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  2. Helen D. Karatza
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Correspondence to Georgios L. Stavrinides .

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Editors and Affiliations

  1. Department of Informatics, Aristotle University of Thessaloniki, Thessaloniki, Greece

    Petros Nicopolitidis

  2. Department of Computer Science and Engineering, Indian Institute of Technology, Kharagpur, West Bengal, India

    Sudip Misra

  3. Department of Computer Science, St. Francis Xavier University, Antigonish, NS, Canada

    Laurence T. Yang

  4. Department of Electrical and Computer Engineering, University of Arizona, Tucson, AZ, USA

    Bernard Zeigler

  5. Dalian University of Technology, Dalian, China

    Zhaolng Ning

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Stavrinides, G.L., Karatza, H.D. (2022). Workload Scheduling in Fog and Cloud Environments: Emerging Concepts and Research Directions. In: Nicopolitidis, P., Misra, S., Yang, L.T., Zeigler, B., Ning, Z. (eds) Advances in Computing, Informatics, Networking and Cybersecurity. Lecture Notes in Networks and Systems, vol 289. Springer, Cham. https://doi.org/10.1007/978-3-030-87049-2_1

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