Abstract
Cloud computing has been considered as one of the large-scale platforms that support various type of services including compute, storage, compute, and analytic to the users and organizations with high agility, scalability, and resiliency intact. The users of the Cloud are increasing at an enormous rate which also resulted in issues related to handling and scheduling the users’ requested workload effectively and efficiently on the available Cloud resources. The aim of the Cloud service providers is to maximize resource utilization and in turn increased revenue generation. In the last few years, Cloud Task scheduling has been considered as an important area of research for the researchers. As different scheduling heuristics are associated with different underlying assumptions; thus, performing a precise comparison cannot be guaranteed. This work empirically compares and provides an insight into the performance of some renown state-of-the-art task scheduling heuristics concerning the Makespan, average resource utilization ratio, Throughput. Those approaches include task-aware, resource-aware, and some hybrid approaches. The experiments were then extended by evaluating the performance using average response time for all the compared approaches. The simulation experiments are conducted by utilizing Heterogeneous Computing Scheduling Problems (HCSP) and Google Cloud Jobs (GOCJ) benchmark datasets using CloudSim a renowned simulation tool for Cloud. Based on the findings of the comparative analysis and results discussion, we have highlighted some important aspects of the underlying approaches and for future work we will propose a task-cum-resource aware task scheduling approach.
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References
M. Ibrahim, M.A. Iqbal, M. Aleem, M.A. Islam, Sim-cumulus: an academic cloud for the provisioning of network-simulation-as-a-service (NSaaS), IEEE Access. 6 (2018), 27313–27323.
M.A. Iqbal, M. Aleem, M.A. Islam, M. Ibrahim, S. Anwar, Amazon cloud computing platform EC2 and VANET simulations, Int. J. Ad Hoc Ubiquit. Comput. 30 (2019), 127–136.
F. Durao, J.F.S. Carvalho, A. Fonseka, V.C. Garcia, A systematic review on Cloud computing, J. Supercomput. 68 (2014), 1321–1346.
S. Groot, Research on efficient resource utilization in data intensive distributed systems, Ph.D. dissertation, University of Tokyo, Japan, 2013.
A. Beloglazov, R. Buyya, Managing overloaded hosts for dynamic consolidation of virtual machines in Cloud data centers under quality of service constraints, IEEE Trans. Parallel Distrib. Syst. 24 (2012), 1366–1379.
H. Jin, W. Gao, S. Wu, X. Shi, X. Wu, F. Zhou, Optimizing the live migration of virtual machine by CPU scheduling, J. Netw. Comput. Appl. 34 (2011), 1088–1096.
V. Sivagami, K. Easwarakumar, An improved dynamic fault tolerant management algorithm during vm migration in Cloud data center, Future Gen. Comput. Syst. 98 (2019), 35–43.
B. Jennings, R. Stadler, Resource management in clouds: survey and research challenges, J. Netw. Syst. Manage. 23 (2015), 567–619.
S.K. Panda, P.K. Jana, SLA-based task scheduling algorithms for heterogeneous multi-Cloud environment, J. Supercomput. 73 (2017), 2730–2762.
H. Chen, F. Wang, N. Helian, G. Akanmu, User-priority guided MinMin scheduling algorithm for load balancing in cloud computing, 2013 National Conference on Parallel computing technologies (PARCOMPTECH), IEEE, Bangalore, India, 2013, pp. 1–8.
S. Rehman, N. Javaid, S. Rasheed, K. Hassan, F. Zafar, M. Naeem, MinMin scheduling algorithm for efficient resource distribution using cloud and fog in smart buildings, International Conference on Broadband and Wireless Computing, Communication and Applications, Springer, Switzerland, 2018, pp. 15–27.
J. Maipan-uku, A. Muhammed, A. Abdullah, M. Hussin, Max-average: an extended max-min scheduling algorithm for grid computing environtment, J. Telecommun. Electron. Comput. Eng. 8 (2016), 43–47.
N.A. Mehdi, A. Mamat, Z.T. Abdul-Mehdi, Minimum completion time for power-aware scheduling in cloud computing, 2011 Developments in E-systems Engineering, IEEE, Dubai, UAE, 2011.
Y. Mao, X. Chen, X. Li, Max–min task scheduling algorithm for load balance in cloud computing, Proceedings of International Conference on Computer Science and Information Technology, Springer, New Delhi, 2014, pp. 457–465.
S. Parsa, R. Entezari-Maleki, RASA: a new grid task scheduling algorithm, Int. J. Digit. Content Technol. Appl. 3 (2009), 152–160.
E.K. Tabak, B.B. Cambazoglu, C. Aykanat, Improving the performance of independent task assignment heuristics MinMin, MaxMin and Sufferage, IEEE Trans. Parallel Distrib. Syst. 25 (2013), 1244–1256.
S. Taherian Dehkordi, V. Khatibi Bardsiri, TASA: a new task scheduling algorithm in cloud computing, J. Adv. Comput. Eng. Technol. 1 (2015), 25–32.
A.R. Arunarani, D. Manjula, V. Sugumaran, Task scheduling techniques in Cloud computing: a literature survey, Future Gen. Comput. Syst. 91 (2019), 407–415.
A. Hussain, M. Aleem, M.A. Iqbal, M.A. Islam, A rigorous evaluation of state-of-the-art scheduling algorithms for Cloud computing, IEEE Access 6 (2018), 75033–75047.
N. Alaei, F. Safi-Esfahani, RePro-active: a reactive–proactive scheduling method based on simulation in cloud computing, J. Supercomput. 74 (2018), 801–829.
Y. Wang, J-T. Zhou, Y. Jiao, X. Song, Comparative analysis of evolutionary algorithms based on swarm intelligence for qos optimization of cloud services, 2019 IEEE 23rd International Conference on Computer Supported Cooperative Work in Design (CSCWD), IEEE, Porto, Portugal, 2019, pp. 434–439.
B. Li, Y. Pei, H. Wu, B. Shen, Heuristics to allocate high-performance Cloudlets for computation offloading in mobile ad hoc clouds, J. Supercomput. 71 (2015), 3009–3036.
S.H.H. Madni, M.S.A. Latiff, M. Abdullahi, M.J. Usman, Performance comparison of heuristic algorithms for task scheduling in IaaS cloud computing environment, PLoS ONE 12 (2017), e0176321.
A. Hussain, M. Aleem, GoCJ: Google cloud jobs dataset for distributed and cloud computing infrastructures, Data 3 (2018), 38.
A. Hussain, M. Aleem, M.A. Iqbal, M.A. Islam, Investigation of cloud scheduling algorithms for resource utilization using CloudSim, Comput. Inform. 38 (2019), 525–554.
T.D. Braunt, H.J. Siegel, N. Beck, L.L. Boloni, M. Maheswarans, A.I. Reuthert, et al. A comparison study of eleven static heuristics for mapping a class of independent tasks onto ileterogeneous distributed computing systems, ECE Technical Reports, 2000, p. 19.
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Ibrahim, M., Nabi, S., Baz, A. et al. Towards a Task and Resource Aware Task Scheduling in Cloud Computing: An Experimental Comparative Evaluation. Int J Netw Distrib Comput 8, 131–138 (2020). https://doi.org/10.2991/ijndc.k.200515.003
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DOI: https://doi.org/10.2991/ijndc.k.200515.003