An Empirical Approach of Performance and Energy-Aware Scheduling [PEAS] Mechanism in the HPC-Cloud Model

An Empirical Approach of Performance and Energy-Aware Scheduling [PEAS] Mechanism in the HPC-Cloud Model
  IJETT-book-cover           
  
© 2022 by IJETT Journal
Volume-70 Issue-7
Year of Publication : 2022
Authors : Sharavana. K, Josephine Prem Kumar, Shivamurthy
DOI : 10.14445/22315381/IJETT-V70I7P224

How to Cite?

Sharavana. K, Josephine Prem Kumar, Shivamurthy, "An Empirical Approach of Performance and Energy-Aware Scheduling [PEAS] Mechanism in the HPC-Cloud Model" International Journal of Engineering Trends and Technology, vol. 70, no. 7, pp. 238-249, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I7P224

Abstract
Cloud Computing provides the advantage of flexibility, elasticity, scaling, and customization to the HPC community as it attracts users that cannot afford to use the dedicated HPC infrastructure. HPC infrastructure is proven costly, as it requires upfront investment despite the advantage of processing the complex task. Interconnection of HPC and cloud environment creates the complex infrastructure for parallel computation and further creates a major issue in managing the makespan and energy performance trade-off. This research presents the PEAS (Performance and Energy-aware scheduling)-mechanism; PEAS is designed for parallel computation with task scheduling and optimal resource allocation at data centers. At first, a system model is designed for the parallel computing process; later, a novel and efficient scheduling algorithm is designed for task scheduling, and at last energy-aware mathematical model is designed for optimal energy utilization. PEAS are evaluated considering the HPC aware scientific workflow like cyber shake and montage workflow considering the evaluation parameter as Make span, Energy consumption, and Power utilization. Moreover, PEAS is proven to be more efficient than any other existing model available to date.

Keywords
Cloud Computing, HPC, Scientific Workflow, HPC Cloud.

Reference
[1] J. Emeras, S. Varrette, V. Plugaru, and P. Bouvry, “Amazon Elastic Compute Cloud (EC2) versus In-House HPC Platform: A Cost Analysis,” in IEEE Transactions on Cloud Computing, vol. 7, no. 2, pp. 456-458, 2019. Doi: 10.1109/TCC.2016.2628371.
[2] A. Pupykina, and G. Agosta, “Survey of Memory Management Techniques for HPC and Cloud Computing,” in IEEE Access, vol. 7, pp. 167351-167373, 2019. Doi: 10.1109/ACCESS.2019.2954169.
[3] D. Dhinakaran, and P. M. Joe Prathap, “Preserving Data Confidentiality in Association Rule Mining Using Data Share Allocator Algorithm,” Intelligent Automation & Soft Computing, vol. 33, no. 3, pp. 1877–1892, 2022. Doi:10.32604/iasc.2022.024509.
[4] B. Murugeshwari, D. Selvaraj, K. Sudharson and S. Radhika, “Data Mining with Privacy Protection Using Precise Elliptical Curve Cryptography,” Intelligent Automation & Soft Computing, vol. 35, no. 1, pp. 839–851, 2023.
[5] A. C. Zhou, J. Lao, Z. Ke, Y. Wang, and R. Mao, “FarSpot: Optimizing Monetary Cost for HPC Applications in the Cloud Spot Market,” in IEEE Transactions on Parallel and Distributed Systems. Doi: 10.1109/TPDS.2021.3134644.
[6] D. Dhinakaran, D. A. Kumar, S. Dinesh, D. Selvaraj, and K. Srikanth, “Recommendation System for Research Studies Based on GCR,” International Mobile and Embedded Technology Conference (MECON), Noida, India, pp. 61-65, 2022. Doi: 10.1109/MECON53876.2022.9751920.
[7] A. Saad, and A. El-Mahdy, “HPC Cloud Seer: A Performance Model Based Predictor for Parallel Applications on the Cloud,” in IEEE Access, vol. 8, pp. 87978-87993, 2020. Doi: 10.1109/ACCESS.2020.2992880.
[8] VMware, “Host Power Management in Vmware Vsphere 5.5,” Tech. Rep. EN- 001262-00, VMware Inc, 2013.
[9] A. Mazouz, A. Laurent, B. Pradelle, and W. Jalby, “Evaluation of CPU Frequency Transition Latency,” Comput. Sci. - Res. Dev., vol. 29, no. 3, pp. 187–195, 2014. Crossref, http://dx. doi.org/10.1007/s00450-013-0240-x.
[10] R. Schöne, D. Molka, and M. Werner, “Wake-up latencies for Processor Idle States on Current X86 Processors,” Comput. Sci. - Res. Dev, vol. 30, no. 2, pp. 219–227, 2015. Crossref, http://dx.doi.org/10.1007/s00450-014-0270-z.
[11] J. Kolodziej, “Evolutionary Hierarchical Multi-Criteria Metaheuristics for Scheduling in Large-Scale Grid Systems,” Springer, 2012.
[12] M.R. Garey, D.S. Johnson, “Computers and Intractability: A Guide to the Theory of NP-Completeness,” W. H. Freeman and Company, 1979.
[13] Z. Li, J. Ge, H. Hu, W. Song, H. Hu, and B. Luo, “Cost and Energy Aware Scheduling Algorithm for Scientific Workflows with Deadline Constraint in Clouds,” IEEE Trans. Serv. Comput., vol. 11, no. 4, pp. 713–726, 2015. Crossref, http://dx.doi.org/10.1109/TSC. 2015.2466545.
[14] L. Wang, S.U. Khan, D. Chen, J. Kolodziej, R. Ranjan, C. Xu, and A. Zomaya, “Energyaware Parallel Task Scheduling in a Cluster,” Future Gener. Comput. Syst., vol. 29, no. 7, pp. 1661–1670, 2013. Crossref, http://dx.doi.org/10.1016/j.future.2013.02.010.
[15] Y. Mhedheb, F. Jrad, J. Tao, J. Zhao, J. Kolodziej, and A. Streit, “Load and Thermalaware 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. Crossref, http://dx.doi.org/10.1007/978-3-319- 03859-9_8.
[16] K. Mizotani, Y. Hatori, Y. Kumura, M. Takasu, H. Chishiro, and N. Yamasaki, “An Integration of Imprecise Computation Model and Real-Time Voltage and Frequency Scaling,” in: Proceedings of the 30th International Conference on Computers and Their Applications (CATA’15), pp. 63–70, 2015.
[17] H. Yu, B. Veeravalli, Y. Ha, and S. Luo, “Dynamic Scheduling of Imprecise Computation Tasks on Real-Time Embedded Multiprocessors,” in: Proceedings of the 2013 IEEE 16th International Conference on Computational Science and Engineering (CSE’13), pp. 770–777, 2013. Crossref, http://dx.doi.org/10.1109/CSE.2013.118.
[18] Malcolm Atkinson, Sandra Gesing, Johan Montagnat, and Ian Taylor, “Scientific Workflows: Past, Present and Future,” Future Generation Computer Systems, vol. 75, pp. 216-227, 2017.
[19] C.C. Lin, Y.C. Syu, C.J. Chang, J.J. Wu, P. Liu, P.W. Cheng, and W.T. Hsu, “Energy Efficient Task Scheduling for Multi-Core Platforms with Per-Core DVFS,” J. Parallel Distrib. Comput, vol. 86, pp. 71–81, 2015. Crossref, http://dx.doi.org/10.1016/j.jpdc. 2015.08.004.
[20] W. Long, L. Yuqing, and X. Qingxin, “Using CloudSim to Model and Simulate Cloud Computing Environment," 2013 Ninth International Conference on Computational Intelligence and Security, Leshan, pp. 323-328, 2013.
[21] K. Sudharson, and V. Parthipan, “A Survey on ATTACK – Anti-Terrorism Technique for Adhoc Using Clustering and Knowledge Extraction, Advances in Computer Science and Information Technology,” Computer Science and Engineering, CCSIT 2012, Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, Springer, Berlin, Heidelberg, vol. 85, pp. 508-514, 2012.
[22] Sharavana. K, Asghar Pasha, and Dr. Josephin Premkumar K, “Approach for Deploying the Hybrid Cloud in Diverse Open Source Tools,” IOSR Journal of Computer Engineering (IOSR-JCE), vol. 20, no. 3, pp. 25-34, 2018.
[23] D. Dhinakaran, P.M. Joe Prathap, D. Selvaraj, D. Arul Kumar, and B. Murugeshwari, “Mining Privacy-Preserving Association Rules based on Parallel Processing in Cloud Computing,” International Journal of Engineering Trends and Technology, vol. 70, no. 30, pp. 284-294, 2022. Doi: 10.14445/22315381/IJETT-V70I3P232.
[24] S. Arun, and K. Sudharson, “DEFECT: Discover and Eradicate Fool Around Node in Emergency Network using Combinatorial Techniques,” Journal of Ambient Intelligence and Humanized Computing, pp. 1-2, 2020. Doi: https://doi.org/10.1007/s12652-020-02606- 7.
[25] J. Aruna Jasmine, V. Nisha Jenipher, J. S. Richard Jimreeves, K. Ravindran, and D. Dhinakaran, “A Traceability Set Up Using Digitalization of Data and Accessibility,” 2020 3rd International Conference on Intelligent Sustainable Systems (ICISS), pp. 907-910, 2020.
[26] N. Partheeban, K. Sudharson, and P.J. Sathish Kumar, “SPEC- Serial Property Based Encryption for Cloud,” International Journal of Pharmacy & Technology, vol. 8, no. 4, pp. 23702-23710, 2016.
[27] K. Sudharson, and V. Parthipan, “SOPE: Self-organized Protocol for Evaluating Trust in MANET Using Eigen Trust Algorithm,” 2011 3rd International Conference on Electronics Computer Technology, Kanyakumari, India, pp. 155-159, 2011.
[28] L. Rahul, and K. Sharavana, “Deployment of Virtual HPC Clusters on Demand from Volunteer Computing Resources,” International Journal of Advanced Research in Computer and Communication Engineering, vol. 3, no. 4, 2014.
[29] D. Dhinakaran, and P.M. Joe Prathap, “Ensuring Privacy of Data and Mined Results of Data Possessor in Collaborative ARM, Pervasive Computing and Social Networking,” Lecture Notes in Networks and Systems, Springer, Singapore, vol. 317, pp. 431-444, 2022. Doi: 10.1007/978-981-16-5640-8_34.
[30] I. Colonnelli, B. Cantalupo, I. Merelli, and M. Aldinucci, “StreamFlow: Cross-Breeding Cloud With HPC,” in IEEE Transactions on Emerging Topics in Computing, vol. 9, no. 4, pp. 1723-1737, 2021. Doi: 10.1109/TETC.2020.3019202.
[31] K. Sudharson, M. Akshaya, M. Lokeswari and K. Gopika, "Secure Authentication scheme using CEEK technique for Trusted Environment," 2022 International Mobile and Embedded Technology Conference (MECON), Noida, India, pp. 66-71, 2022.
[32] A. Fernandez, “Evaluation of the Performance of Tightly Coupled Parallel Solvers and MPI Communications in IAAS from the Public Cloud,” in IEEE Transactions on Cloud Computing. Doi: 10.1109/TCC.2021.3052844.
[33] K. Sudharson and S. Arun, “Security Protocol Function Using Quantum Elliptic Curve Cryptography Algorithm,” Intelligent Automation & Soft Computing, vol. 34, no. 3, pp. 1769–1784, 2022.
[34] D. Dhinakaran and P.M Joe Prathap, “Protection of Data Privacy from Vulnerability Using Two-Fish Technique with Apriori Algorithm in Data Mining,” Journal of Supercomputing, 2022. Crossref, https://doi.org/10.1007/s11227-022-04652-8.
[35] Margesh Keskar, Dhananjay D Maktedar, “Evolutionary Computing Driven ROI-Specific Spatio-Temporal Statistical Feature Learning Model for Medicinal Plant Disease Detection and Classification,” International Journal of Engineering Trends and Technology, vol. 70, no. 6, pp. 165-184, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I6P220.
[36] Mandeep Singh, Shashi Bhushan, “CS Optimized Task Scheduling for Cloud Data Management,” International Journal of Engineering Trends and Technology, vol. 70, no. 6, pp. 114-121, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I6P214.