Abstract
Based on the problem of task communication overhead being higher than the task execution time has a direct negative impact on the makespan of task scheduling in the current scheduling algorithms. In this paper, we propose a novel hybrid task scheduling algorithm based on task clustering (HTSTC). The algorithm uses task clustering technology to integrate tasks that meet the conditions into one cluster and uses task duplication method in the phase of processor selection. The algorithm effectively reduces the task communication overhead, and advances the start time of the successor tasks. In the layering and task priority calculation phase, HTSTC takes into account both the task communication overhead and task execution cost on different processors. The proposed algorithm effectively shortens the makespan of task scheduling. Experiments show that HTSTC has superior performance when compared to HEFT and CPOP, two of the currently leading algorithms.
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References
Ahmad I, Kwok Y.-K. (1998) On exploiting task duplication in parallel program scheduling. IEEE Trans. Parallel Distrib Syst 9(9):872–892
Al-Mouhamed MA (1990) Lower bound on the number of processors and time for scheduling precedence graphs with communication costs. IEEE Trans Software Eng 16(12):1390–1401
Andrew AM (1993) Systems: an introductory analysis with applications to biology, control, and artificial intelligence, by John H. Holland. Robotica 11(5):489
Arabnejad H, Barbosa JG (2014) List scheduling algorithm for heterogeneous systems by an optimistic cost table. IEEE Trans Parallel Distrib Syst 25(3):682–694
Bhatti MK, Oz I, Amin S, Mushtaq M, Farooq U, Popov K, Brorsson M (2018) Locality-aware task scheduling for homogeneous parallel computing systems. Computing 100(6):557–595
Bozdag D, Özgüner F, Çatalyürek ÜV (2009) Compaction of schedules and a two-stage approach for duplication-based DAG scheduling. IEEE Trans Parallel Distrib Syst 20(6):857–871
Correa RC, Ferreira A, Rebreyend P (1996) Integrating list heuristics into genetic algorithms for multiprocessor scheduling. In: IEEE Symposium on parallel and distributed processing, p 462
Canon L-C, Jeannot E, Sakellariou R, Zheng W (2008) Comparative evaluation of the robustness of DAG scheduling heuristics. In: Grid computing - achievements and prospects: coreGRID integration workshop 2008, Hersonissos, Crete, Greece, April 2-4, 2008, pp 73–84
Chaoqun Z, Yimin Z (2017) An improved duplication based heterogeneous multi-core scheduling algorithm. Electronic Science Technology
Cirou B, Jeannot E (2001) Triplet: a clustering scheduling algorithm for heterogeneous systems. In: 30th International workshops on parallel processing (ICPP 2001 workshops), 3-7 September 2001, Valencia, Spain, pp 231–236
Darbha S, Agrawal DP (1998) Optimal scheduling algorithm for distributed-memory machines. IEEE Trans Parallel Distrib Syst 9(1):87–96
Ding G, Wu Q, Zhang L, Lin Y, Tsiftsis TA, Yao Y-D (2018) An amateur drone surveillance system based on the cognitive internet of things. IEEE Commun Mag 56(1):29–35
Dogan A, Füsun Ö (2002) LDBS: a duplication based scheduling algorithm for heterogeneous computing systems. In: 31st International conference on parallel processing (ICPP 2002), 20-23 August 2002, Vancouver, BC, Canada, pp 352–359
Dou Z, Shi C, Lin Y, Li W (2017) Modeling of non-gaussian colored noise and application in CR multi-sensor networks. EURASIP J Wireless Comm and Networking 2017:192
Kun HE, Ling Y, Zhuming LI (2012) Distributed clustering and greedy scheduling algorithm based on task duplication. Journal of New Industrialization
Gerasoulis A, Yang T (1992) . A comparison of clustering heuristics for scheduling directed acyclic graphs on multiprocessors 16:276–291
Glover F (1994) Tabu search for nonlinear and parametric optimization (with links to genetic algorithms). Discret Appl Math 49(1–3):231–255
Singh H, Youssef A (1995) Mapping and scheduling heterogeneous task graphs using genetic algorithms. In: 5th IEEE heterogeneous computing workshop (HCW ’96)
Chen HB, Shirazi B, Kavi K, Hurson AR (1993) Static scheduling using linear clustering with task duplication, 285–290
Jiang Y-S, Chen W-M (2015) Task scheduling for grid computing systems using a genetic algorithm. J Supercomput 71(4):1357–1377
Jianguo S, Wenshan W, Liang K, Yun L, Liguo Z, Da Q, Lei C (2017) A data authentication scheme for uav ad hoc network communication. J Supercomput 8:1–16
Khan MA (2012) Scheduling for heterogeneous systems using constrained critical paths. Parallel Comput 38 (4–5):175–193
Sj K, Jc B (1988) . General approach to mapping of parallel computations upon multiprocessor architectures 3:1–8
Kruatrachue B, Lewis T (1988) Grain size determination for parallel processing. IEEE Softw 5(1):23–32
Lin Y, Li Y, Yin X, Dou Z (2018) Multisensor fault diagnosis modeling based on the evidence theory. IEEE Trans Reliab 67(2):513–521
Lin Y, Wang C, Ma C, Dou Z, Ma X (2016) A new combination method for multisensor conflict information. J Supercomput 72(7):2874–2890
Lin Y, Wang C, Wang J, Dou Z (2016) A novel dynamic spectrum access framework based on reinforcement learning for cognitive radio sensor networks. Sensors 16(10):1675
Liu G, Liu S, Muhammad K, Sangaiah AK, Doctor F (2018) Object tracking in vary lighting conditions for fog based intelligent surveillance of public spaces. IEEE Access 6:29283–29296
Liu S, Bai W, Liu G, Li W, Srivastava HM (2018) Parallel fractal compression method for big video data. Complexity 2018:2016976:1–2016976:16
Liu T, Guan Y, Lin Y (2017) Research on modulation recognition with ensemble learning. EURASIP J Wireless Comm Network 2017:179
Dorigo M, Maniezzo V, Colorni A (1991) Ant system: an autocatalytic optimizing process technical report 91-016. Clustering 3(12):340
Ma X, Wang T, Lin Y, Jin S (2018) Parallel iterative inter-carrier interference cancellation in underwater acoustic orthogonal frequency division multiplexing. Wireless Personal Commun 5:1–14
Olukotun K, Nayfeh BA, Hammond L, Wilson KG, Chang K (1996) The case for a single-chip multiprocessor. In: ASPLOS-VII proceedings - seventh international conference on architectural support for programming languages and operating systems, Cambridge, Massachusetts, USA, October 1-5, 1996, pp 2–11
Shroff P, Watson DW, Flann NF, Freund RF (1996) Genetic simulated annealing for scheduling data-dependent tasks in heterogeneous environments. In: Heterogeneous computing workshop
Palis MA, Liou J-C, Wei DSL (1996) Task clustering and scheduling for distributed memory parallel architectures. IEEE Trans Parallel Distrib Syst 7(1):46–55
Pan Z, Liu S, Sangaiah AK, Muhammad K (2018) Visual attention feature (VAF): a novel strategy for visual tracking based on cloud platform in intelligent surveillance systems. J Parallel Distrib Comput 120:182–194
Papadimitriou CH, Yannakakis M (1990) Towards an architecture-independent analysis of parallel algorithms. SIAM J Comput 19(2):322–328
Wu Q, Li Y, Lin Y (2014) The nonlocal sparse reconstruction algorithm by similarity measurement with shearlet feature vector. Math Problems Eng, 2014,(2014-3-4) 2014(1):1–8
Rui HG, Ping Z, Bo WG (2009) Key techniques of multi-core processor and its development trends. Comput Eng Des 30(10):2414–2418
Shetti KR, Fahmy SA , Bretschneider T (2013) Optimization of the HEFT algorithm for a CPU-GPU environment. In: International conference on parallel and distributed computing, applications and technologies, PDCAT 2013, Taipei, Taiwan, December 16-18, 2013, pp 212–218
Shi C, Dou Z, Lin Y, Li W (2018) Dynamic threshold-setting for rf-powered cognitive radio networks in non-gaussian noise. Physl Commun 27:99–105
Sun J, Wang W, Zhang K, Zhang L, Lin Y, Han Q, Da Q, Kou L (2018) A multi-focus image fusion algorithm in 5g communications. Multimed Tools Appl 3:1–20
Tang B, Tu Y, Zhang Z, Lin Y (2018) Digital signal modulation classification with data augmentation using generative adversarial nets in cognitive radio networks. IEEE Access 6:15713–15722
Topcuoglu H, Hariri S, Wu M-Y (2002) Performance-effective and low-complexity task scheduling for heterogeneous computing. IEEE Trans Parallel Distrib Syst 13(3):260–274
Wang H, Guo L, Dou Z, Lin Y (2018) A new method of cognitive signal recognition based on hybrid information entropy and d-s evidence theory. Mobile Netw Appl 4:1–9
Wang H, Jingchao LI, Guo L, Dou Z, Lin Y, Zhou R (2017) Fractal complexity-based feature extraction algorithm of communication signals. Fractals-complex Geometry Patterns Scaling in Nature Society 25 (5):1740008
Wu Q, Li Y, Lin Y (2016) The application of nonlocal total variation in image denoising for mobile transmission. Multimed Tools Appl 76(16):1–13
Xue W (2014) Research on dependent task scheduling strategy based on heterogeneous multi-core processor. Master’s thesis, Harbin Engineering University
Xue Z, Wang J, Ding G, Wu Q, Lin Y, Tsiftsis TA (2018) Device-to-device communications underlying uav-supported social networking. IEEE Access 6:34488–34502
Tu Y, Lin Y, Wang J (2018) Semi-supervised learning with generative adversarial networks on digital signal modulation classification. CMC-Comput Mater Continua 55(2):243–254
Yang T, Gerasoulis A (1994) DSC: scheduling parallel tasks on an unbounded number of processors. IEEE Trans Parallel Distrib Syst 5(9):951–967
Yun L, Xiaolei Z, Zhigao Z, Zheng D, Ruolin Z (2017) The individual identification method of wireless device based on dimensionality reduction and machine learning. J Supercomput 5:1–18
Zhang G, Kou L, Ye Y, Sun J, Lin Y, Da Q, Wang W (2017) An intelligent method of cancer prediction based on mobile cloud computing. Cluster Comput 3:1–9
Zhang Z, Guo X, Lin Y (2018) Trust management method of D2D communication based on RF fingerprint identification. IEEE Access 6:66082–66087
Zhou N, Qi D, Wang X, Zheng Z (2017) A static task scheduling algorithm for heterogeneous systems based on merging tasks and critical tasks. J Comput Meth Sci Eng 17(4):715–732
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This work was supported by National Key Research and Development Plan of China (No.2016YFB0801004).
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Tian, Q., Li, J., Xue, D. et al. A Hybrid Task Scheduling Algorithm Based on Task Clustering. Mobile Netw Appl 25, 1518–1527 (2020). https://doi.org/10.1007/s11036-019-01356-x
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DOI: https://doi.org/10.1007/s11036-019-01356-x