article

A novel hybrid of Shortest job first and round Robin with dynamic variable quantum time task scheduling technique

Authors:

Shahenda Sarhan,

Manar JoundyAuthors Info & Claims

Journal of Cloud Computing: Advances, Systems and Applications, Volume 6, Issue 1

Article No.: 85, Pages 1 - 12

https://doi.org/10.1186/s13677-017-0085-0

Published: 01 December 2017 Publication History

Abstract

Cloud computing is a ubiquitous network access model to a shared pool of configurable computing resources where available resources must be checked and scheduled using an efficient task scheduler to be assigned to clients. Most of the existing task schedulers, did not achieve the required standards and requirements as some of them only concentrated on waiting time or response time reduction or even both neglecting the starved processes at all. In this paper, we propose a novel hybrid task scheduling algorithm named (SRDQ) combining Shortest-Job-First (SJF) and Round Robin (RR) schedulers considering a dynamic variable task quantum. The proposed algorithms mainly relies on two basic keys the first having a dynamic task quantum to balance waiting time between short and long tasks while the second involves splitting the ready queue into two sub-queues, Q1 for the short tasks and the other for the long ones.

Assigning tasks to resources from Q1 or Q2 are done mutually two tasks from Q1 and one task from Q2. For evaluation purpose, three different datasets were utilized during the algorithm simulation conducted using CloudSim environment toolkit 3.0.3 against three different scheduling algorithms SJF, RR and Time Slice Priority Based RR (TSPBRR) Experimentations results and tests indicated the superiority of the proposed algorithm over the state of art in reducing waiting time, response time and partially the starvation of long tasks.

References

[1]

Lu CW, Hsieh CM, Chang CH, Yang CT (2013, July) An improvement to data Service in Cloud Computing with Content Sensitive Transaction Analysis and Adaptation, Computer Software and applications Conference workshops (COMPSACW), 2013 IEEE 37th annual, vol 463-468

Digital Library

[2]

Azeez A, Perera S, Gamage D, Linton R, Siriwardana P, Leelaratne D, Fremantle P (2010, July) Multi-tenant SOA middleware for cloud computing, Cloud computing (cloud), 2010, IEEE 3rd International Conference on, pp 458---465

Digital Library

[3]

Demchenko Y, de Laat C (2011, March) Defining generic architecture for cloud infrastructure as a Service model, The International symposium on grids and clouds and the open grid forum Academia Sinica, pp 2---10

[4]

Calheiros RN, Ranjan R, Beloglazov A, De Rose CA, Buyya R (2011) CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and Experience 41(1):23---50

Digital Library

[5]

Ganapathi A, Chen Y, Fox A, Katz R, Patterson D (2010, March) Statistics-driven workload modeling for the cloud, Data Engineering workshops (ICDEW), 2010 IEEE 26th International Conference on, pp 87---92

[6]

Garg SK, Buyya R (2011, December) Networkcloudsim: Modelling parallel applications in cloud simulations, Utility and cloud computing (UCC), 2011 fourth IEEE International Conference on, pp 105---113

Digital Library

[7]

Buyya R, Ranjan R, Calheiros RN (2009, June) Modeling and simulation of scalable cloud computing environments and the Cloudsim toolkit: challenges and opportunities, High Performance Computing & Simulation, 2009. HPCS'09. International Conference on, pp 1---11

[8]

Das AK, Adhikary T, Razzaque MA, Hong CS (2013, January) An intelligent approach for virtual machine and QoS provisioning in cloud computing, Information Networking (ICOIN), 2013 International Conference on, pp 462---467

Digital Library

[9]

Bhoi U, Ramanuj PN (2013) Enhanced max-min task scheduling algorithm in cloud computing. International Journal of Application or Innovation in Engineering and Management (IJAIEM) 2(4):259---264

[10]

Wang SC, Yan KQ, Liao WP, Wang SS (2010) Towards a load balancing in a three-level cloud computing network, Computer Science and information technology (ICCSIT), 2010 3rd IEEE International Conference on, 1, pp 108---113

[11]

Venters W, Whitley EA (2012) A critical review of cloud computing: researching desires and realities. J Inf Technol 27(3):179---197

[12]

Santra S, Dey H, Majumdar S, Jha GS (2014, July) New simulation toolkit for comparison of scheduling algorithm on cloud computing, Control, instrumentation, communication and computational technologies (ICCICCT), 2014 International Conference on, pp 466---469

[13]

Fang Y, Wang F, Ge J (2010) A task scheduling algorithm based on load balancing in cloud computing. Web information systems and Mining. Springer, Berlin Heidelberg, pp 271---277

Digital Library

[14]

Lin CC, Liu P, Wu JJ (2011, July) Energy-aware virtual machine dynamic provision and scheduling for cloud computing, CLOUD computing (CLOUD), 2011 IEEE International Conference on, pp 736---737

Digital Library

[15]

Ghanbari S, Othman M (2012) A priority based job scheduling algorithm in cloud computing. Procedia Engineering 50:778---785

[16]

Maguluri ST, Srikant R, Ying L (2012) Stochastic models of load balancing and scheduling in cloud computing clusters, INFOCOM, 2012 Proceedings IEEE, pp 702---710

[17]

Gulati A, Chopra RK (2013) Dynamic round Robin for load balancing in a cloud computing. IJCSMC 2(6):274---278

[18]

Agha AEA, Jassbi SJ (2013) A new method to improve round Robin scheduling algorithm with quantum time based on harmonic-arithmetic mean (HARM). International Journal of Information Technology and Computer Science 5(7):56---62

[19]

Tsai JT, Fang JC, Chou JH (2013) Optimized task scheduling and resource allocation on cloud computing environment using improved Differential Evolution algorithm. Comput Oper Res 40(12):3045---3055

Digital Library

[20]

Ergu D, Kou G, Peng Y, Shi Y, Shi Y (2013) The analytic hierarchy process: task scheduling and resource allocation cloud computing environment. J Supercomput 64(3):835---848

Digital Library

[21]

Karthick AV, Ramaraj E, Subramanian RG (2014, February) An efficient multi queue job scheduling for cloud computing, Computing and communication technologies (WCCCT), 2014 world congress on, pp 164---166

[22]

Lakra AV, Yadav DK (2015) Multi-objective tasks scheduling algorithm for cloud computing throughput optimization. Procedia Computer Science 48:107---113

[23]

Dash AR, Samantra SK (2016) An optimized round Robin CPU scheduling algorithm with dynamic time quantum. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT) 5(1):7---26.

[24]

Tunc C, Kumbhare N, Akoglu A, Hariri S, Machovec D, Siegel HJ (2016, December) Value of Service based task scheduling for cloud computing systems, Cloud and autonomic computing (ICCAC), 2016 International Conference on, pp 1---11.

[25]

Razaque A, Vennapusa NR, Soni N, Janapati GS (2016, April) Task scheduling in cloud computing, Long Island systems, applications and technology Conference (LISAT), 2016 IEEE, pp 1---5.

[26]

Mizan, T., Al Masud, S. M. R., & Latip, R. (2012). Modified bees life algorithm for job scheduling in hybrid cloud.

[27]

Ge Y, Wei G (2010) GA-based task scheduler for the cloud computing systems. In Web Information Systems and Mining (WISM), 2010 International Conference on, Vol. 2. IEEE, Sanya, China, p. 181---186

Digital Library

[28]

Raju R, Babukarthik RG, Dhavachelvan P (2013) Hybrid ant Colony optimization and cuckoo search algorithm for job scheduling, Advances in computing and information technology. Springer, Berlin Heidelberg, pp 491---501

[29]

Ramezani F, Lu J, Hussain F (2013, December) Task scheduling optimization in cloud computing applying multi-objective particle swarm optimization, International Conference on Service-oriented computing. Berlin, Springer Berlin Heidelberg, pp 237---251

Digital Library

[30]

Gan GN, Huang TL, Gao S (2010). Genetic simulated annealing algorithm for task scheduling based on cloud computing environment. In Intelligent Computing and Integrated Systems (ICISS), 2010 International Conference on (pp. 60-63). IEEE, Guilin

[31]

Wang L, Siegel HJ, Roychowdhury VP, Maciejewski AA (1997) Task matching and scheduling in heterogeneous computing environments using a genetic-algorithm-based approach. Journal of parallel and distributed computing 47(1):8---22

Digital Library

[32]

Zhao C, Zhang S, Liu Q, Xie J, Hu J (2009) Independent tasks scheduling based on genetic algorithm in cloud computing. In Wireless Communications, Networking and Mobile Computing, 2009. WiCom'09. 5th International Conference on. IEEE, Beijing, p. 1---4

Digital Library

[33]

Gu J, Hu J, Zhao T, Sun G (2012) A new resource scheduling strategy based on genetic algorithm in cloud computing environment. Journal of Computers 7(1):42---52

[34]

Mocanu EM, Florea M, Andreica MI, ¿ă¿puş N (2012) Cloud computing--task scheduling based on genetic algorithms. In Systems Conference (SysCon), 2012 IEEE International. IEEE, Vancouver, p. 1---6

[35]

Kaur S, Verma A (2012) An efficient approach to genetic algorithm for task scheduling in cloud computing environment. International Journal of Information Technology and Computer Science (IJITCS) 4(10):74

[36]

Jang SH, Kim TY, Kim JK, Lee JS (2012) The study of genetic algorithm-based task scheduling for cloud computing. International Journal of Control and Automation 5(4):157---162

[37]

Kruekaew B, Kimpan W (2014) Virtual machine scheduling management on cloud computing using artificial bee colony, Proceedings of the International MultiConference of engineers and computer scientists, vol 1, pp 12---14

[38]

Bilgaiyan S, Sagnika S, Das M (2014) An analysis of task scheduling in cloud computing using evolutionary and swarm-based algorithms. Int J Comput Appl 89(2):11---18

[39]

Navimipour NJ, Milani FS (2015) Task scheduling in the cloud computing based on the cuckoo search algorithm. International Journal of Modeling and Optimization 5(1):44

[40]

Verma, A., & Kaushal, S. (2014) Bi-criteria priority based particle swarm optimization workflow scheduling algorithm for cloud. In Engineering and Computational Sciences (RAECS), 2014 Recent Advances in. IEEE, p. 1---6

[41]

Alla HB, Alla SB, Ezzati A, Mouhsen A (2017) A novel architecture with dynamic queues based on fuzzy logic and particle swarm optimization algorithm for task scheduling in cloud computing, Advances in Ubiquitous Networking 2, 397, 205---217. Springer, Singapore

[42]

Sebastio S, Gnecco G, Bemporad A (2017) Optimal distributed task scheduling in volunteer clouds. Comput Oper Res 81:231---246

Digital Library

[43]

Maharana D, Sahoo B, Sethi S (2017) Energy-efficient real-time tasks scheduling in cloud data centers. International Journal of Science Engineering and Advance Technology, IJSEAT 4(12):768---773

[44]

Liu Y, Xu X, Zhang L, Wang L, Zhong RY (2017) Workload-based multi-task scheduling in Cloud Manufacturing. Robot Comput Integr Manuf 45:3---20

Digital Library

[45]

Sofia AS, Kumar PG (2017) Energy efficient task scheduling to implement green cloud. Asian Journal of Research in Social Sciences and Humanities 7(2):443---458

[46]

Li, K. (2017). Scheduling parallel tasks with energy and time constraints on multiple Manycore processors In A cloud computing environment. Future generation computer systems.

[47]

Rimal BP, Maier M (2017) Workflow scheduling in multi-tenant cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 28(1):290---304.

Digital Library

[48]

http://www.cloudbus.org/cloudsim/ {Accessed at : 25/4/2015}

[49]

http://www.read.seas.harvard.edu/~kohler/class/05s-osp/notes/notes5.html. Accessed 25 Apr 2015.

[50]

Mohapatra S, Mohanty S, Rekha KS (2013) Analysis of different variants in round Robin algorithms for load balancing in cloud computing. Int J Comput Appl 69(22):17---21.

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Hussain AAl-Turjman F(2022)Hybrid Genetic Algorithm for IOMT-Cloud Task SchedulingWireless Communications & Mobile Computing10.1155/2022/66042862022Online publication date: 1-Jan-2022
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Jamil BIjaz HShojafar MMunir KBuyya R(2022)Resource Allocation and Task Scheduling in Fog Computing and Internet of Everything Environments: A Taxonomy, Review, and Future DirectionsACM Computing Surveys10.1145/351300254:11s(1-38)Online publication date: 9-Sep-2022
https://dl.acm.org/doi/10.1145/3513002
Junaid MSohail ATurjman FAli R(2022)Agile Support Vector Machine for Energy-efficient Resource Allocation in IoT-oriented Cloud using PSOACM Transactions on Internet Technology10.1145/343354122:1(1-35)Online publication date: 28-Feb-2022
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A novel hybrid of Shortest job first and round Robin with dynamic variable quantum time task scheduling technique

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cover image Journal of Cloud Computing: Advances, Systems and Applications

Journal of Cloud Computing: Advances, Systems and Applications Volume 6, Issue 1

December 2017

524 pages

ISSN:2192-113X

EISSN:2192-113X

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Copyright © Copyright © 2017 The Author(s).

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Hindawi Limited

London, United Kingdom

Publication History

Published: 01 December 2017

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Cited By

Hussain AAl-Turjman F(2022)Hybrid Genetic Algorithm for IOMT-Cloud Task SchedulingWireless Communications & Mobile Computing10.1155/2022/66042862022Online publication date: 1-Jan-2022
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Jamil BIjaz HShojafar MMunir KBuyya R(2022)Resource Allocation and Task Scheduling in Fog Computing and Internet of Everything Environments: A Taxonomy, Review, and Future DirectionsACM Computing Surveys10.1145/351300254:11s(1-38)Online publication date: 9-Sep-2022
https://dl.acm.org/doi/10.1145/3513002
Junaid MSohail ATurjman FAli R(2022)Agile Support Vector Machine for Energy-efficient Resource Allocation in IoT-oriented Cloud using PSOACM Transactions on Internet Technology10.1145/343354122:1(1-35)Online publication date: 28-Feb-2022
https://dl.acm.org/doi/10.1145/3433541
Ullah ANawi NOuhame S(2022)Recent advancement in VM task allocation system for cloud computing: review from 2015 to2021Artificial Intelligence Review10.1007/s10462-021-10071-755:3(2529-2573)Online publication date: 1-Mar-2022
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Yuvaraj NKarthikeyan TPraghash K(2021)An Improved Task Allocation Scheme in Serverless Computing Using Gray Wolf Optimization (GWO) Based Reinforcement Learning (RIL) ApproachWireless Personal Communications: An International Journal10.1007/s11277-020-07981-0117:3(2403-2421)Online publication date: 1-Apr-2021
https://dl.acm.org/doi/10.1007/s11277-020-07981-0
Kumar PKumar R(2019)Issues and Challenges of Load Balancing Techniques in Cloud ComputingACM Computing Surveys10.1145/328101051:6(1-35)Online publication date: 4-Feb-2019
https://dl.acm.org/doi/10.1145/3281010
Mishra SPuthal DSahoo BJena SObaidat M(2018)An adaptive task allocation technique for green cloud computingThe Journal of Supercomputing10.1007/s11227-017-2133-474:1(370-385)Online publication date: 1-Jan-2018
https://dl.acm.org/doi/10.1007/s11227-017-2133-4

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