Details

PDF BIBTEX RIS

Title

An enhanced performance evaluation of workflow computing and scheduling using hybrid classification approach in the cloud environment

Journal title

Bulletin of the Polish Academy of Sciences Technical Sciences

Yearbook

2021

Volume

69

Issue

4

Authors

Tharani, P. ; Kalpana, A.M.

Affiliation

Tharani, P. : Department of Computer Science and Engineering, Government College of Engineering, Salem-636011, Tamil Nadu, India ; Kalpana, A.M. : Department of Computer Science and Engineering, Government College of Engineering, Salem-636011, Tamil Nadu, India

Keywords

cloud ; workflow scheduling ; machine learning ; CNN ; AlexNet

Divisions of PAS

Nauki Techniczne

Coverage

e137728

Bibliography

  1.  A.M. Manasrah and H. Ba Ali, “Workflow scheduling using hybrid GA-PSO algorithm in cloud computing,” Wireless Commun. Mob. Comput., vol. 15, no. 3, pp. 1–16, 2018, doi: 10.1155/2018/1934784.
  2.  S.G. Ahmad, C.S. Liew, E.U. Munir, A.T. Fong, and S.U. Khan, “A hybrid genetic algorithm for optimization of scheduling workflow applications in heterogeneous computing systems,” J. Parallel Distrib. Comput., vol. 87, no. 2, pp. 80–90, 2016, doi: 10.1016/j. jpdc.2015.10.001.
  3.  H. Alaskar, A. Hussain, N. Al-Aseem, P. Liatsis, and D. Al-Jumeily, “Application of convolutional neural networks for automated ulcer detection in wireless capsule endoscopy images,” Sensors., vol. 19, no. 6, pp. 1265–1281, 2019, doi: 10.3390/s19061265.
  4.  L. Teylo, L. Arantes, P. Sens, and L.M.A. Drummond, “A dynamic task scheduler tolerant to multiple hibernations in cloud environments,” J. Cluster Comput., vol. 21, no. 5, pp. 1–23, 2020, doi: 10.1007/s10586-020-03175-2.
  5.  M. Sardaraz and M. Tahir, “A parallel multi-objective genetic algorithm for scheduling scientific workflows in cloud computing,” Int. J. Distri. Sensor Net., vol. 16, no. 8, pp. 1–10, 2020, doi: 10.1177/1550147720949142.
  6.  M. Hosseinzadeh, M.Y. Ghafour, and H.K. Hama, “Multi-objective task and workflow scheduling approaches in cloud computing: a comprehensive review,” J. Grid Comput., vol. 18, no. 3, pp. 327–356, 2020, doi: 10.1007/s10723-020-09533-z.
  7.  C.L. Chen, M.L. Chiang, and C.B. Lin, “The high performance of a task scheduling algorithm using reference queues for cloud- computing data centers,” Electronics, . vol. 9, no. 1, pp. 371–379, 2020, doi: 10.3390/electronics9020371.
  8.  Y. Hu, H. Wang, and W. Ma, “Intelligent Cloud workflow management and scheduling method for big data applications,” J. Cloud Comp, vol. 9, no. 1, pp. 1–13, 2020, doi: 10.1186/s13677-020-00177-8.
  9.  M. Grochowski, A. Kwasigroch, and A. Mikołajczyk,” Selected technical issues of deep neural networks for image classification purposes,” Bull. Polish Acad. Sci. Tech. Sci., vol. 67, no. 2, pp. 363–376, 2019, doi: 10.24425/bpas.2019.128485.
  10.  A.A. Nasr, N.A. El-Bahnasawy, and G. Attiya, “Cost-effective algorithm for workflow scheduling in cloud computing under deadline constraint,” Arab. J. Sci. Eng., vol. 44, no. 4, pp. 3765–3780, 2019, doi: 10.1007/s13369-018-3664-6.
  11.  Z. Swiderska-Chadaj, T. Markiewicz, J. Gallego, G. Bueno, B. Grala, and M. Lorent, “Deep learning for damaged tissue detection and segmentation in Ki-67 brain tumor specimens based on the U-net model,” Bull. Polish Acad. Sci. Tech. Sci., vol. 66, no. 6, pp. 849–856, 2018, doi: 10.24425/bpas.2018.125932.
  12.  Y. Cui and Z. Xiaoqing, “Workflow tasks scheduling optimization based on genetic algorithm in Clouds,” in IEEE 3rd Int. Conf. on Cloud Computing and Big Data Analysis (ICCCBDA), Chengdu, 2018, pp. 6–10, doi: 10.1109/ICCCBDA.2018.8386458.
  13.  T. Wang, Z. Liu, Y. Chen, Y. Xu, and X. Dai, “Load balancing task scheduling based on Genetic algorithm in Cloud Computing,” in IEEE 12th Int. Conf. on Dependable, Autonomic and Secure Computing, Dalian, 2014, pp. 146–152, doi: 10.1109/DASC.2014.35.
  14.  X. Zhao, Y. Wu, G. Song, Z. Li, Y. Zhang, and Y. Fan, “A deep learning model integrating FCNNs and CRFs for brain tumor segmentation,” Med. Imag. Anal., vol. 43, no. 4, pp.  98–111, 2018, doi: 10.1016/j.media.2017.10.002.
  15.  C. Liu, R. Zhao, W. Xie, and M. Pang, “Pathological lung segmentation based on random forest combined with deep model and multi- scale superpixels,” Neu. Proc, Let., vol. 52, no. 2, pp. 1631–1649, 2020, doi: 10.1007/s11063-020-10330-8.
  16.  C. Zhang, J. Zhao, Ji. Niu, and D. Li, “New convolutional neural network model for screening and diagnosis of mammograms,” PLoS One., vol. 15, no. 8, pp. 67–80, 2020, doi: 10.1371/journal.pone.0237674.
  17.  L.D. Nicanor, H.R. Orozco Aguirre, and V.M. Landassuri Moreno, “An assessment model to establish the use of services resources in a cloud computing scenario,” J. High Perf. Vis. Int., vol. 12, no. 5, pp. 83–100, 2020, doi: 10.1007/978-981-15-6844-2_7.
  18.  V. Magudeeswaran and J. Fenshia Singh, “Contrast limited fuzzy adaptive histogram equalization for enhancement of brain images,” Int. J. Imag. Sys. and Tech., vol. 27, no. 1, pp. 98–103, 2017, doi: 10.1002/ima.22214.
  19.  S.P. Cleary and J.S. Prell, “Liberating native mass spectrometry from dependence on volatile salt buffers by use of Gábor transform,” Int. J. Imag. Syst. Tech., vol. 20, no. 4, pp. 519–523, 2019, doi: 10.1002/cphc.201900022.
  20.  V. Srivastava, R.K. Purwar, and A. Jain, “A dynamic threshold‐based local mesh ternary pattern technique for biomedical image retrieval,” Int. J. Imag. Sysy. Tech., vol. 29, no. 2, pp. 168–179, 2019, doi: 10.1002/ima.22296.
  21.  J.H. Johnpeter and T. Ponnuchamy, “Computer aided automated detection and classification of brain tumors using CANFIS classification method,” Int. J. Imag. Sysy. Tech., vol. 29, no. 4, pp. 431–438, 2019, doi: 10.1002/ima.22318.
  22.  N. Kumar and D. Kumar, “Classification using artificial neural network optimized with bat algorithm”, Int. J. Innovative Tech. Exploring Eng. (IJITEE), vol. 9, no. 3, pp.  696–700, 2020, doi: 10.35940/ijitee.C8378.019320.
  23.  A.S. Mahboob and S.H. Zahiri, “Automatic and heuristic complete design for ANFIS classifier, network: computation in neural systems,” J. Net. Comput. Neu. Syst., vol. 30, no. 4, pp. 31–57, 2019, doi: 10.1080/0954898X.2019.1637953.
  24.  C. Shorten and T.M. Khoshgoftaar, “A survey on image data augmentation for deep learning,” J. Big. Data., vol.  60, no. 6, pp. 1–48, 2019, doi: 10.1186/s40537-019-0197-0.

Date

26.06.2021

Type

Article

Identifier

DOI: 10.24425/bpasts.2021.137728
×