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Volume 7 Issue 5
Sep.  2020

IEEE/CAA Journal of Automatica Sinica

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Article Contents
Haitao Yuan, MengChu Zhou, Qing Liu and Abdullah Abusorrah, "Fine-Grained Resource Provisioning and Task Scheduling for Heterogeneous Applications in Distributed Green Clouds," IEEE/CAA J. Autom. Sinica, vol. 7, no. 5, pp. 1380-1393, Sept. 2020. doi: 10.1109/JAS.2020.1003177
Citation: Haitao Yuan, MengChu Zhou, Qing Liu and Abdullah Abusorrah, "Fine-Grained Resource Provisioning and Task Scheduling for Heterogeneous Applications in Distributed Green Clouds," IEEE/CAA J. Autom. Sinica, vol. 7, no. 5, pp. 1380-1393, Sept. 2020. doi: 10.1109/JAS.2020.1003177

Fine-Grained Resource Provisioning and Task Scheduling for Heterogeneous Applications in Distributed Green Clouds

doi: 10.1109/JAS.2020.1003177
Funds:  This work was supported in part by the National Natural Science Foundation of China (61802015, 61703011), the Major Science and Technology Program for Water Pollution Control and Treatment of China (2018ZX07111005), the National Defense Pre-Research Foundation of China (41401020401, 41401050102) and the Deanship of Scientific Research (DSR) at King Abdulaziz University, Jeddah (D-422-135-1441)
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  • An increasing number of enterprises have adopted cloud computing to manage their important business applications in distributed green cloud (DGC) systems for low response time and high cost-effectiveness in recent years. Task scheduling and resource allocation in DGCs have gained more attention in both academia and industry as they are costly to manage because of high energy consumption. Many factors in DGCs, e.g., prices of power grid, and the amount of green energy express strong spatial variations. The dramatic increase of arriving tasks brings a big challenge to minimize the energy cost of a DGC provider in a market where above factors all possess spatial variations. This work adopts a G/G/1 queuing system to analyze the performance of servers in DGCs. Based on it, a single-objective constrained optimization problem is formulated and solved by a proposed simulated-annealing-based bees algorithm (SBA) to find SBA can minimize the energy cost of a DGC provider by optimally allocating tasks of heterogeneous applications among multiple DGCs, and specifying the running speed of each server and the number of powered-on servers in each GC while strictly meeting response time limits of tasks of all applications. Realistic data-based experimental results prove that SBA achieves lower energy cost than several benchmark scheduling methods do.

     

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  • [1]
    A. Botta, W. De Donato, V. Persico, and A. Pescapé, “Integration of cloud computing and internet of things: A survey,” Future Generat. Comput. Syst., vol. 56, pp. 684–700, Mar. 2016. doi: 10.1016/j.future.2015.09.021
    [2]
    M. H. Ghahramani, M. C. Zhou, and C. T. Hon, “Toward cloud computing QoS architecture: Analysis of cloud systems and cloud services,” IEEE/CAA J. Autom. Sinica, vol. 4, no. 1, pp. 6–18, Jan. 2017. doi: 10.1109/JAS.2017.7510313
    [3]
    K. K. Gai, M. K. Qiu, H. Zhao, and X. T. Sun, “Resource management in sustainable cyber-physical systems using heterogeneous cloud computing,” IEEE Trans. Sust. Comput., vol. 3, no. 2, pp. 60–72, Apr.–Jun. 2018. doi: 10.1109/TSUSC.2017.2723954
    [4]
    M. Al-Ayyoub, M. Al-Quraan, Y. Jararweh, E. Benkhelifa, and S. Hariri, “Resilient service provisioning in cloud based data centers,” Future Generat. Comput. Syst., vol. 86, pp. 765–774, Sep. 2018. doi: 10.1016/j.future.2017.07.005
    [5]
    P. Pierleoni, R. Concetti, A. Belli, and L. Palma, “Amazon, Google and Microsoft solutions for IoT: Architectures and a performance comparison,” IEEE Access, vol. 8, pp. 5455–5470, Dec. 2019.
    [6]
    F. Farahnakian, T. Pahikkala, P. Liljeberg, J. Plosila, N. T. Hieu, and H. Tenhunen, “Energy-aware VM consolidation in cloud data centers using utilization prediction model,” IEEE Trans. Cloud Comput., vol. 7, no. 2, pp. 524–536, Apr.–Jun. 2019. doi: 10.1109/TCC.2016.2617374
    [7]
    E. Baccarelli, N. Cordeschi, A. Mei, M. Panella, M. Shojafar, and J. Stefa, “Energy-efficient dynamic traffic offloading and reconfiguration of networked data centers for big data stream mobile computing: Review, challenges, and a case study,” IEEE Netw., vol. 30, no. 2, pp. 54–61, Mar.–Apr. 2016. doi: 10.1109/MNET.2016.7437025
    [8]
    Z. Zhou, J. Abawajy, M. Chowdhury, Z. G. Hu, K. Q. Li, H. B. Cheng, A. A. Alelaiwi, and F. M. Li, “Minimizing SLA violation and power consumption in cloud data centers using adaptive energy-aware algorithms,” Future Generat. Comput. Syst., vol. 86, pp. 836–850, Sep. 2018. doi: 10.1016/j.future.2017.07.048
    [9]
    E. Cortez, A. Bonde, A. Muzio, M. Russinovich, M. Fontoura, and R. Bianchini, “Resource central: Understanding and predicting workloads for improved resource management in large cloud platforms,” in Proc. 26th Symp. on Operating Systems Principles, Shanghai, China, 2017, pp. 153–167.
    [10]
    S. Mubeen, S. A. Asadollah, A. V. Papadopoulos, M. Ashjaei, H. Pei-Breivold, and M. Behnam, “Management of service level agreements for cloud services in IoT: A systematic mapping study,” IEEE Access, vol. 6, pp. 30184–30207, Aug. 2018. doi: 10.1109/ACCESS.2017.2744677
    [11]
    A. Kiani and N. Ansari, “A fundamental tradeoff between total and brown power consumption in geographically dispersed data centers,” IEEE Commun. Lett., vol. 20, no. 10, pp. 1955–1958, Oct. 2016. doi: 10.1109/LCOMM.2016.2598535
    [12]
    L. Yu, T. Jiang, and Y. L. Zou, “Distributed real-time energy management in data center Microgrids,” IEEE Trans. Smart Grid, vol. 9, no. 4, pp. 3748–3762, Jul. 2018. doi: 10.1109/TSG.2016.2640453
    [13]
    Q. Fang, J. Wang, Q. Gong, and M. X. Song, “Thermal-aware energy management of an HPC data center via two-time-scale control,” IEEE Trans. Ind. Informat., vol. 13, no. 5, pp. 2260–2269, Oct. 2017. doi: 10.1109/TII.2017.2698603
    [14]
    H. G. Rong, H. M. Zhang, S. Xiao, C. B. Li, and C. H. Hu, “Optimizing energy consumption for data centers,” Renew. Sust. Energy Rev., vol. 58, pp. 674–691, May 2016. doi: 10.1016/j.rser.2015.12.283
    [15]
    M. Vasudevan, Y. C. Tian, M. L. Tang, E. Kozan, and X. Y. Zhang, “Energy-efficient application assignment in profile-based data center management through a repairing genetic algorithm,” Appl. Soft Comput., vol. 67, pp. 399–408, Jun. 2018. doi: 10.1016/j.asoc.2018.03.016
    [16]
    X. Lyu, H. Tian, C. Sengul, and P. Zhang, “Multiuser joint task offloading and resource optimization in proximate clouds,” IEEE Trans. Veh. Technol., vol. 66, no. 4, pp. 3435–3447, Apr. 2017. doi: 10.1109/TVT.2016.2593486
    [17]
    D. F. Li, P. L. Hong, K. P. Xue, and J. N. Pei, “Virtual network function placement considering resource optimization and SFC requests in cloud datacenter,” IEEE Trans. Parallel Distributed Syst., vol. 29, no. 7, pp. 1664–1677, Jul. 2018. doi: 10.1109/TPDS.2018.2802518
    [18]
    J. Li, M. G. Peng, Y. L. Yu, and Z. G. Ding, “Energy-efficient joint congestion control and resource optimization in heterogeneous cloud radio access networks,” IEEE Trans. Veh. Technol., vol. 65, no. 12, pp. 9873–9887, Dec. 2016. doi: 10.1109/TVT.2016.2531184
    [19]
    X. W. Qiu, Y. S. Dai, Y. P. Xiang, and L. D. Xing, “Correlation modeling and resource optimization for cloud service with fault recovery,” IEEE Trans. Cloud Comput., vol. 7, no. 3, pp. 693–704, Jul.–Sep. 2019. doi: 10.1109/TCC.2017.2691323
    [20]
    J. Bi, H. T. Yuan, W. Tan, M. C. Zhou, Y. S. Fan, J. Zhang, and J. Q. Li, “Application-aware dynamic fine-grained resource provisioning in a virtualized cloud data center,” IEEE Trans. Autom. Sci. Eng., vol. 14, no. 2, pp. 1172–1184, Apr. 2017. doi: 10.1109/TASE.2015.2503325
    [21]
    S. El Kafhali and K. Salah, “Stochastic Modelling and analysis of cloud computing data center,” in Proc. 20th Conf. on Innovations in Clouds, Internet and Networks, Paris, France, 2017, pp. 122–126.
    [22]
    A. Satpathy, S. K. Addya, A. K. Turuk, B. Majhi, and G. Sahoo, “Crow search based virtual machine placement strategy in cloud data centers with live migration,” Comput. Electr. Eng., vol. 69, pp. 334–350, Jul. 2018. doi: 10.1016/j.compeleceng.2017.12.032
    [23]
    A. Ponraj, “Optimistic virtual machine placement in cloud data centers using queuing approach,” Future Generat. Comput. Syst., vol. 93, pp. 338–344, Apr. 2019. doi: 10.1016/j.future.2018.10.022
    [24]
    H. T. Yuan, J. Bi, W. Tan, and B. H. Li, “CAWSAC: Cost-aware workload scheduling and admission control for distributed cloud data centers,” IEEE Trans. Autom. Sci. Eng., vol. 13, no. 2, pp. 976–985, Apr. 2016. doi: 10.1109/TASE.2015.2427234
    [25]
    B. Zhai, D. Blaauw, D. Sylvester, and K. Flautner, “Theoretical and practical limits of dynamic voltage scaling,” in Proc. 41st Design Autom. Conf., San Diego, CA, USA, 2004, pp. 868–873.
    [26]
    L. Gu, D. Z. Zeng, A. Barnawi, S. Guo, and I. Stojmenovic, “Optimal task placement with QoS constraints in geo-distributed data centers using DVFS,” IEEE Trans. Comput., vol. 64, no. 7, pp. 2049–2059, Jul. 2015. doi: 10.1109/TC.2014.2349510
    [27]
    W. C. Dou, X. L. Xu, S. M. Meng, X. Y. Zhang, C. H. Hu, S. Yu, and J. Yang, “An energy-aware virtual machine scheduling method for service QoS enhancement in clouds over big data,” Concurr. Comput.:Pract. Exp., vol. 29, no. 14, pp. e3909, Jul. 2017. doi: 10.1002/cpe.3909
    [28]
    M. Ghamkhari and H. Mohsenian-Rad, “Energy and performance management of green data centers: A profit maximization approach,” IEEE Trans. Smart Grid, vol. 4, no. 2, pp. 1017–1025, Jun. 2013. doi: 10.1109/TSG.2013.2237929
    [29]
    H. T. Yuan, J. Bi, and M. C. Zhou, “Spatiotemporal task scheduling for heterogeneous delay-tolerant applications in distributed green data centers,” IEEE Trans. Autom. Sci. Eng., vol. 16, no. 4, pp. 1686–1697, Oct. 2019. doi: 10.1109/TASE.2019.2892480
    [30]
    M. N. Hjelmeland, J. K. Zou, A. Helseth, and S. Ahmed, “Nonconvex medium-term hydropower scheduling by stochastic dual dynamic integer programming,” IEEE Trans. Sust. Energy, vol. 10, no. 1, pp. 481–490, Jan. 2019. doi: 10.1109/TSTE.2018.2805164
    [31]
    F. Zhang and D. R. Bull, “Rate-distortion optimization using adaptive lagrange multipliers,” IEEE Trans. Circ. Syst. Video Technol., vol. 29, no. 10, pp. 3121–3131, Oct. 2019. doi: 10.1109/TCSVT.2018.2873837
    [32]
    A. A. Augusto, M. B. Do Coutto Filho, J. C. S. de Souza, and M. A. R. Guimaraens, “Branch-and-bound guided search for critical elements in state estimation,” IEEE Trans. Power Syst., vol. 34, no. 3, pp. 2292–2301, May 2019. doi: 10.1109/TPWRS.2018.2881421
    [33]
    F. Bistaffa, N. Bombieri, and A. Farinelli, “An efficient approach for accelerating bucket elimination on GPUs,” IEEE Trans. Cybernet., vol. 47, no. 11, pp. 3967–3979, Nov. 2017. doi: 10.1109/TCYB.2016.2593773
    [34]
    J. Bi, H. T. Yuan, W. Tan, and B. H. Li., “TRS: Temporal request scheduling with bounded delay assurance in a green cloud data center,” Inf. Sci., vol. 360, pp. 57–72, Sep. 2016. doi: 10.1016/j.ins.2016.04.024
    [35]
    S. Pare, A. Kumar, V. Bajaj, and G. K. Singh, “A context sensitive multilevel thresholding using swarm based algorithms,” IEEE/CAA J. Autom. Sinica, vol. 6, no. 6, pp. 1471–1486, Nov. 2019.
    [36]
    Y. L. Cao, H. Zhang, W. F. Li, M. C. Zhou, Y. Zhang, and W. A. Chaovalitwongse, “Comprehensive learning particle swarm optimization algorithm with local search for multimodal functions,” IEEE Trans. Evolut. Comput., vol. 23, no. 4, pp. 718–731, Aug. 2019. doi: 10.1109/TEVC.2018.2885075
    [37]
    J. Q. Zhang, X. X. Zhu, Y. H. Wang, and M. C. Zhou, “Dual-environmental particle swarm optimizer in noisy and noise-free environments,” IEEE Trans. Cybernet., vol. 49, no. 6, pp. 2011–2021, Jun. 2019. doi: 10.1109/TCYB.2018.2817020
    [38]
    E. Yigit and H. Duysak, “Determination of optimal layer sequence and thickness for broadband multilayer absorber design using double-stage artificial bee colony algorithm,” IEEE Trans. Micro. Theory Tech., vol. 67, no. 8, pp. 3306–3317, Aug. 2019. doi: 10.1109/TMTT.2019.2919574
    [39]
    Q. Duan, J. Zeng, K. Chakrabarty, and G. Dispoto, “Real-time production scheduler for digital-print-service providers based on a dynamic incremental evolutionary algorithm,” IEEE Trans. Autom. Sci. Eng., vol. 12, no. 2, pp. 701–715, Apr. 2015. doi: 10.1109/TASE.2014.2304177
    [40]
    L. Zhu, F. R. Yu, Y. G. Wang, B. Ning, and T. Tang, “Big data analytics in intelligent transportation systems: A survey,” IEEE Trans. Intel. Transp. Syst., vol. 20, no. 1, pp. 383–398, Jan. 2019. doi: 10.1109/TITS.2018.2815678
    [41]
    B. Wang, H. X. Xie, X. D. Xia, and X. X. Zhang, “A NSGA-II algorithm hybridizing local simulated-annealing operators for a bi-criteria robust job-shop scheduling problem under scenarios,” IEEE Trans. Fuzzy Syst., vol. 27, no. 5, pp. 1075–1084, May 2019. doi: 10.1109/TFUZZ.2018.2879789
    [42]
    H. T. Yuan, J. Bi, W. Tan, M. C. Zhou, B. H. Li, and J. Q. Li, “TTSA: An effective scheduling approach for delay bounded tasks in hybrid clouds,” IEEE Trans. Cybernet., vol. 47, no. 11, pp. 3658–3668, Nov. 2017. doi: 10.1109/TCYB.2016.2574766
    [43]
    H. T. Yuan, J. Bi, W. Tan, and B. H. Li, “Temporal task scheduling with constrained service delay for profit maximization in hybrid clouds,” IEEE Trans. Autom. Sci. Eng., vol. 14, no. 1, pp. 337–348, Jan. 2017. doi: 10.1109/TASE.2016.2526781
    [44]
    M. Castellani, S. Otri, and D. T. Pham, “Printed circuit board assembly time minimisation using a novel bees algorithm,” Comput. Ind. Eng., vol. 133, pp. 186–194, Jul. 2019. doi: 10.1016/j.cie.2019.05.015
    [45]
    Z. J. Huang, Z. S. Wang, and H. G. Zhang, “Multilevel feature moving average ratio method for fault diagnosis of the microgrid inverter switch,” IEEE/CAA J. Autom. Sinica, vol. 4, no. 2, pp. 177–185, Apr. 2017. doi: 10.1109/JAS.2017.7510496
    [46]
    P. Y. Zhang, S. Shu, and M. C. Zhou, “An online fault detection model and strategies based on SVM-grid in cloud,” IEEE/CAA J. Autom. Sinica, vol. 5, no. 2, pp. 445–456, Mar. 2018. doi: 10.1109/JAS.2017.7510817
    [47]
    A. Kavousi, B. Vahidi, R. Salehi, M. K. Bakhshizadeh, N. Farokhnia, and S. H. Fathi, “Application of the bee algorithm for selective harmonic elimination strategy in multilevel inverters,” IEEE Trans. Power Electron., vol. 27, no. 4, pp. 1689–1696, Apr. 2012. doi: 10.1109/TPEL.2011.2166124
    [48]
    Y. J. Gong, J. J. Li, Y. C. Zhou, Y. Li, H. S. H. Chung, Y. H. Shi, and J. Zhang, “Genetic learning particle swarm optimization,” IEEE Trans. Cybernet., vol. 46, no. 10, pp. 2277–2290, Oct. 2016. doi: 10.1109/TCYB.2015.2475174
    [49]
    X. Deng, D. Wu, J. F. Shen, and J. He, “Eco-aware online power management and load scheduling for green cloud datacenters,” IEEE Syst. J., vol. 10, no. 1, pp. 78–87, Mar. 2016. doi: 10.1109/JSYST.2014.2344028
    [50]
    J. Bi, H. T. Yuan, W. Tan, and B. H. Li, “TRS: Temporal request scheduling with bounded delay assurance in a green cloud data center,” Inf. Sci., vol. 360, no. 1, pp. 57–72, Sep. 2016.
    [51]
    J. Y. Luo, L. Rao, and X. Liu, “Spatio-temporal load balancing for energy cost optimization in distributed internet data centers,” IEEE Trans. Cloud Comput., vol. 3, no. 3, pp. 387–397, Jul.–Sep. 2015. doi: 10.1109/TCC.2015.2415798
    [52]
    H. T. Yuan, J. Bi, and M. C. Zhou, “Spatial task scheduling for cost minimization in distributed green cloud data centers,” IEEE Trans. Autom. Sci. Eng., vol. 16, no. 2, pp. 729–740, Apr. 2019. doi: 10.1109/TASE.2018.2857206
    [53]
    S. Shenoy, D. Gorinevsky, and N. Laptev, “Probabilistic modeling of computing demand for service level agreement,” IEEE Trans. Serv. Comput., vol. 12, no. 6, pp. 987–993, Nov.–Dec. 2019. doi: 10.1109/TSC.2016.2637929
    [54]
    S. Deng, Z. Xiang, P. Zhao, J. Taheri, H. Gao, J. Yin, and A. Zomaya, “Dynamical resource allocation in edge for trustable internet-of-things systems: A reinforcement learning method,” IEEE Trans. Industrial Informatics, vol. 16, no. 9, pp. 6103–6113, Sep. 2020.
    [55]
    W. Li, Y. Xia, M. Zhou, X. Sun, and Q. Zhu, “Fluctuation-aware and predictive workflow scheduling in cost-effective infrastructure-as-a-service clouds,” IEEE Access, vol. 6, pp. 61488–61502, Sep. 2020.
    [56]
    Q. Wu, F. Ishikawa, Q. Zhu, and Y. Xia, “Energy and migration cost-aware dynamic virtual machine consolidation in heterogeneous cloud datacenters,” IEEE Trans. Services Computing, vol. 12, no. 4, pp. 550–563, Oct. 2019.
    [57]
    P. Zhang and M. Zhou, “Dynamic cloud task scheduling based on a two-stage strategy,” IEEE Trans. Autom. Science and Engineering, vol. 15, no. 2, pp. 772–783, Oct. 2018.
    [58]
    S. Gao, M. Zhou, Y. Wang, J. Cheng, H. Yachi, and J. Wang, “Dendritic neuron model with effective learning algorithms for classification, approximation, and prediction,” IEEE Trans. Neural Networks and Learning Systems, vol. 30, no. 2, pp. 601–614, Feb. 2019.
    [59]
    J. J. Wang and T. Kumbasar, “Parameter optimization of interval type-2 fuzzy neural networks based on PSO and BBBC methods,” IEEE/CAA J. Autom. Sinica, vol. 6, no. 1, pp. 247–257, Jan. 2019.
    [60]
    J. Li, J. Zhang, C. Jiang, and M. Zhou, “Composite particle swarm optimizer with historical memory for function optimization,” IEEE Trans. Cybernetics, vol. 45, no. 10, pp. 2350–2363, Oct. 2015.
    [61]
    X. Zuo, C. Chen, W. Tan, and M. Zhou, “Vehicle scheduling of urban bus line via an improved multi-objective genetic algorithm,” IEEE Trans. Intelligent Transportation Systems, vol. 16, no. 2, pp. 1030–1041, Apr. 2015.
    [62]
    G. Tian, Y. Ren, and M. Zhou, “Dual-objective scheduling of rescue vehicles to distinguish forest fires via differential evolution and particle swarm optimization combined algorithm,” IEEE Trans. Intelligent Transportation Systems, vol. 17, no. 11, pp. 3009–3021, Nov. 2016.
    [63]
    W. Dong and M. Zhou, “A supervised learning and control method to improve particle swarm optimization algorithms,” IEEE Trans. Systems, Man and Cybernetics: Systems, vol. 47, no. 7, pp. 1149–1159, Jul. 2017.
    [64]
    Q. Kang, S. W. Feng, M. Zhou, A. C. Ammari, and K. Sedraoui, “Optimal load scheduling of plug-in hybrid electric vehicles via weight-aggregation multi-objective evolutionary algorithms,” IEEE Trans. Intelligent Transportation Systems, vol. 18, no. 9, pp. 2557–2568, Sep. 2017.
    [65]
    Z. Cao, C. Lin, M. Zhou, and R. Huang, “Scheduling semiconductor testing facility by using cuckoo search algorithm with reinforcement learning and surrogate modeling,” IEEE Trans. Autom. Science and Engineering, vol. 16, no. 2, pp. 825–837, Apr. 2019.
    [66]
    K. Z. Gao, Z. G. Cao, L. Zhang, Z. H. Chen, Y. Y. Han, and Q. K. Pan, “A review on swarm intelligence and evolutionary algorithms for solving flexible job shop scheduling problems,” IEEE/CAA J. Autom. Sinica, vol. 6, no. 4, pp. 875–887, Jul. 2019.
    [67]
    Y. Fu, M. Zhou, X. Guo, and L. Qi, “Artificial-molecule-based chemical reaction optimization for flow shop scheduling problem with deteriorating and learning effects,” IEEE Access, vol. 7, pp. 53429–53440, 2019.
    [68]
    J. Q. Li, Q. K. Pan, P. Y. Duan, H. Y. Sang, and K. Z. Gao, “Solving multi-area environmental/economic dispatch by Pareto-based chemical-reaction optimization algorithm,” IEEE/CAA J. Autom. Sinica, vol. 6, no. 5, pp. 1240–1250, Sep. 2019.
    [69]
    C. Liu, J. Wang, and M. Zhou, “Reconfiguration of virtual cellular manufacturing systems via improved imperialist competitive approach,” IEEE Trans. Autom. Science and Engineering, vol. 16, no. 3, pp. 1301–1314, Jul. 2019.
    [70]
    Y. Yu, S. Gao, Y. Wang, and Y. Todo, “Global optimum-based search differential evolution,” IEEE/CAA J. Autom. Sinica, vol. 6, no. 2, pp. 379–394, Mar. 2019.
    [71]
    Y. Feng, M. Zhou, G. Tian, Z. Li, Z. Zhang, Q. Zhang, and J. Tan, “Target disassembly sequencing and scheme evaluation for cnc machine tools using improved multiobjective ant colony algorithm and fuzzy integral,” IEEE Trans. Systems, Man, and Cybernetics: Systems, vol. 49, no. 12, pp. 2438–2451, Dec. 2019.
    [72]
    L. Huang, M. Zhou, and K. Hao, “Non-dominated immune-endocrine short feedback algorithm for multi-robot maritime patrolling,” IEEE Trans. Intelligent Transportation Systems, vol. 21, no. 1, pp. 362–373, Jan. 2020.
    [73]
    P. Zhang, M. Zhou, and G. Fortino, “Security and trust issues in fog computing: A survey,” Future Generation Computer Systems, vol. 88, pp. 16–27, Nov. 2018.
    [74]
    T. Alfakih, M. M. Hassan, A. Gumaei, C. Savaglio, and G. Fortino, “Task offloading and resource allocation for mobile edge computing by deep reinforcement learning based on SARSA,” IEEE Access, vol. 8, pp. 54074–54084, May 2020.
    [75]
    Q. Fan and N. Ansari, “On cost aware cloudlet placement for mobile edge computing,” IEEE/CAA J. Autom. Sinica, vol. 6, no. 4, pp. 926–937, Jul. 2019.

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    Highlights

    • This work uses a G/G/1 queuing system to analyze performance of green clouds (GCs).
    • An energy cost problem is minimized while strictly meeting latency limits of tasks.
    • Simulated-annealing-based bees algorithm (SBA) is used to find a real-time solution.
    • SBA properly consumes energy by optimally allocating tasks of heterogeneous in GCs.
    • SBA achieves lower energy cost than its several benchmark scheduling peers can do.

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