More Web Proxy on the site http://driver.im/

review-article

Multi-Objective Task and Workflow Scheduling Approaches in Cloud Computing: a Comprehensive Review

Authors:

Mehdi Hosseinzadeh,

Marwan Yassin Ghafour,

Hawkar Kamaran Hama,

Afsane KhoshnevisAuthors Info & Claims

Journal of Grid Computing, Volume 18, Issue 3

Pages 327 - 356

https://doi.org/10.1007/s10723-020-09533-z

Published: 01 September 2020 Publication History

Abstract

Efficient task and workflow scheduling are very important for improving resource management and reducing power consumption in cloud computing data centers (DCs). However, regarding numerous tasks, virtual machines, and several objectives which should be taken into account, scheduling is considered to be an NP-Hard problem. Multi-objective optimization is an interesting technique to deal with multiple conflicting goals which have been utilized by various schemes to solve the task and workflow scheduling problems. This paper focuses on the metaheuristic multi-objective optimization context and presents a comprehensive survey and overview of the multi-objective scheduling approaches designed for various cloud computing environments. It classifies the scheduling schemes regarding their applied multi-objective optimization algorithms and describes how they have adapted the optimization algorithms to solve scheduling problems. Furthermore, a comparison of the multi-objective scheduling schemes is provided, which illuminates future research directions, and finally concluding remarks are presented.

References

[1]

Masdari, M., Zangakani, M.: Green cloud computing using proactive virtual machine placement: challenges and issues. Journal of Grid Computing. 1–33 (2019)

[2]

Masdari M, Nabavi SS, and Ahmadi V An overview of virtual machine placement schemes in cloud computing J. Netw. Comput. Appl. 2016 66 106-127

[3]

Singh S and Chana I A survey on resource scheduling in cloud computing: issues and challenges Journal of grid computing 2016 14 217-264

[4]

Rong H, Zhang H, Xiao S, Li C, and Hu C Optimizing energy consumption for data centers Renew. Sust. Energ. Rev. 2016 58 674-691

[5]

Rodriguez MA and Buyya R A taxonomy and survey on scheduling algorithms for scientific workflows in IaaS cloud computing environments Concurrency and Computation: Practice and Experience 2017 29 e4041

[6]

Masdari M, ValiKardan S, Shahi Z, and Azar SI Towards workflow scheduling in cloud computing: a comprehensive analysis J. Netw. Comput. Appl. 2016 66 64-82

[7]

Masdari M, Salehi F, Jalali M, and Bidaki M A survey of PSO-based scheduling algorithms in cloud computing J. Netw. Syst. Manag. 2017 25 122-158

[8]

Smanchat S and Viriyapant K Taxonomies of workflow scheduling problem and techniques in the cloud Futur. Gener. Comput. Syst. 2015 52 1-12

[9]

Midya S, Roy A, Majumder K, and Phadikar S Multi-objective optimization technique for resource allocation and task scheduling in vehicular cloud architecture: a hybrid adaptive nature inspired approach J. Netw. Comput. Appl. 2018 103 58-84

[10]

Verma A and Kaushal SA hybrid multi-objective particle swarm optimization for scientific workflow schedulingParallel Comput.2017621-193612600

[11]

Ahmad SG, Liew CS, Munir EU, Ang TF, and Khan SU A hybrid genetic algorithm for optimization of scheduling workflow applications in heterogeneous computing systems Journal of Parallel and Distributed Computing 2016 87 80-90

[12]

Shishido HY, Estrella JC, Toledo CFM, and Arantes MS Genetic-based algorithms applied to a workflow scheduling algorithm with security and deadline constraints in clouds Computers & Electrical Engineering 2018 69 378-394

[13]

Kaur P and Mehta S Resource provisioning and work flow scheduling in clouds using augmented shuffled frog leaping algorithm Journal of Parallel and Distributed Computing 2017 101 41-50

[14]

Casas I, Taheri J, Ranjan R, Wang L, and Zomaya AY GA-ETI: an enhanced genetic algorithm for the scheduling of scientific workflows in cloud environments Journal of computational science 2018 26 318-331

[15]

Abdullahi M, Ngadi MA, Dishing SI, and Ahmad BIE An efficient symbiotic organisms search algorithm with chaotic optimization strategy for multi-objective task scheduling problems in cloud computing environment Journal of Network and Computer Applications 2019 133 60-74

[16]

G. Portaluri and S. Giordano, “Power efficient resource allocation in cloud computing data centers using multi-objective genetic algorithms and simulated annealing,” in 2015 IEEE 4th International Conference on Cloud Networking (CloudNet), pp. 319–321 (2015)

[17]

C. Szabo and T. Kroeger, “Evolving multi-objective strategies for task allocation of scientific workflows on public clouds,” in 2012 IEEE Congress on Evol. Comput., pp. 1–8 (2012)

[18]

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

[19]

Ghasemi-Falavarjani S, Nematbakhsh M, and Ghahfarokhi BS Context-aware multi-objective resource allocation in mobile cloud Computers & Electrical Engineering 2015 44 218-240

[20]

F. Ebadifard and S. M. Babamir, “Optimizing multi objective based workflow scheduling in cloud computing using black hole algorithm,” in 2017 3th International Conference on Web Research (ICWR), pp. 102–108 (2017)

[21]

Tsai J-T, Fang J-C, and Chou J-HOptimized task scheduling and resource allocation on cloud computing environment using improved differential evolution algorithmComput. Oper. Res.2013403045-30551348.68025

[22]

F. Wu, Q. Wu, Y. Tan, and W. Wang, “Unified multi-constraint and multi-objective workflow scheduling for cloud system,” in International Conference on Algorithms and Architectures for Parallel Processing, pp. 635–650 (2015)

[23]

Grandinetti L, Pisacane O, and Sheikhalishahi M An approximate ϵ-constraint method for a multi-objective job scheduling in the cloud Futur. Gener. Comput. Syst. 2013 29 1901-1908

[24]

M. R. Hoseinyfarahabady, H. R. Samani, L. M. Leslie, Y. C. Lee, and A. Y. Zomaya, “Handling uncertainty: Pareto-efficient bot scheduling on hybrid clouds,” in 2013 42nd International Conference on Parallel Processing, pp. 419–428 (2013)

[25]

Guzek M, Pecero JE, Dorronsoro B, and Bouvry P Multi-objective evolutionary algorithms for energy-aware scheduling on distributed computing systems Appl. Soft Comput. 2014 24 432-446

[26]

M. E. Frincu and C. Craciun, “Multi-objective meta-heuristics for scheduling applications with high availability requirements and cost constraints in multi-cloud environments,” in 2011 fourth IEEE international conference on utility and cloud computing, pp. 267–274 (2011)

[27]

I. Pietri, Y. Chronis, and Y. Ioannidis, “Multi-objective optimization of scheduling dataflows on heterogeneous cloud resources,” in 2017 IEEE International Conference on Big Data (Big Data), pp. 361–368 (2017)

[28]

Knowles JD and Corne DW Approximating the nondominated front using the Pareto archived evolution strategy Evol. Comput. 2000 8 149-172

[29]

D. W. Corne, N. R. Jerram, J. D. Knowles, and M. J. Oates, “PESA-II: Region-based selection in evolutionary multi-objective optimization,” in Proceedings of the 3rd Annual Conference on Genetic and Evolutionary Computation, pp. 283–290 (2001)

[30]

E. Zitzler, M. Laumanns, and L. Thiele, “SPEA2: Improving the strength Pareto evolutionary algorithm,” TIK-report, vol. 103, (2001)

[31]

Deb K, Pratap A, Agarwal S, and Meyarivan T A fast and elitist multi-objective genetic algorithm: NSGA-II IEEE Trans. Evol. Comput. 2002 6 182-197

[32]

C. C. Coello and M. S. Lechuga, “MOPSO: A proposal for multiple objective particle swarm optimization,” in Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No. 02TH8600), pp. 1051–1056 (2002)

[33]

Zhang Q and Li H MOEA/D: a multi-objective evolutionary algorithm based on decomposition IEEE Trans. Evol. Comput. 2007 11 712-731

[34]

Jain H and Deb K An evolutionary many-objective optimization algorithm using reference-point based nondominated sorting approach, part II: handling constraints and extending to an adaptive approach IEEE Trans. Evol. Comput. 2013 18 602-622

[35]

Hirsch M, Rodríguez JM, Mateos C, and Zunino A A two-phase energy-aware scheduling approach for cpu-intensive jobs in mobile grids Journal of Grid Computing 2017 15 55-80

[36]

Kliazovich D, Pecero JE, Tchernykh A, Bouvry P, Khan SU, and Zomaya AY CA-DAG: modeling communication-aware applications for scheduling in cloud computing Journal of Grid Computing 2016 14 23-39

[37]

Masdari, M., Khezri, H.: Efficient VM migrations using forecasting techniques in cloud computing: a comprehensive review. Clust. Comput. 1–30 (2020)

[38]

Wang S, Li K, Mei J, Xiao G, and Li K A reliability-aware task scheduling algorithm based on replication on heterogeneous computing systems Journal of Grid Computing 2017 15 23-39

[39]

Guerrero C, Lera I, and Juiz C Migration-aware genetic optimization for mapreduce scheduling and replica placement in hadoop Journal of Grid Computing 2018 16 265-284

[40]

Tang Z, Qi L, Cheng Z, Li K, Khan SU, and Li K An energy-efficient task scheduling algorithm in DVFS-enabled cloud environment Journal of Grid Computing 2016 14 55-74

[41]

Masdari, M., Khoshnevis, A.: A survey and classification of the workload forecasting methods in cloud computing. Clust. Comput. 1–26 (2019)

[42]

Liu J, Pacitti E, Valduriez P, and Mattoso M A survey of data-intensive scientific workflow management Journal of Grid Computing 2015 13 457-493

[43]

G. B. Berriman, E. Deelman, J. C. Good, J. C. Jacob, D. S. Katz, C. Kesselman, A. C. Laity, T. A. Prince, G. Singh, and M.-H. Su, “Montage: a grid-enabled engine for delivering custom science-grade mosaics on demand,” in Optimizing Scientific Return for Astronomy through Information Technologies, pp. 221–232 (2004)

[44]

S. Bharathi, A. Chervenak, E. Deelman, G. Mehta, M.-H. Su, and K. Vahi, “Characterization of scientific workflows,” in 2008 third workshop on workflows in support of large-scale science, pp. 1–10 (2008)

[45]

E. Deelman, J. Blythe, Y. Gil, C. Kesselman, G. Mehta, S. Patil, M.-H. Su, K. Vahi, and M. Livny, “Pegasus: Mapping scientific workflows onto the grid,” in European Across Grids Conference, pp. 11–20 (2004)

[46]

T. Fahringer, R. Prodan, R. Duan, J. Hofer, F. Nadeem, F. Nerieri, S. Podlipnig, J. Qin, M. Siddiqui, and H.-L. Truong, “Askalon: A development and grid computing environment for scientific workflows,” in Workflows for e-Science, ed: Springer, pp. 450–471 (2007)

[47]

H. M. Fard, R. Prodan, J. J. D. Barrionuevo, and T. Fahringer, “A multi-objective approach for workflow scheduling in heterogeneous environments,” in 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012), pp. 300–309 (2012)

[48]

Keiner J, Kunis S, and Potts DUsing NFFT 3---a software library for various nonequispaced fast Fourier transformsACM Transactions on Mathematical Software (TOMS)2009361-3027382001364.65303

[49]

K. A. Ocaña, D. de Oliveira, F. Horta, J. Dias, E. Ogasawara, and M. Mattoso, “Exploring molecular evolution reconstruction using a parallel cloud based scientific workflow,” in Brazilian Symposium on Bioinformatics, pp. 179–191 (2012)

[50]

C.-L. Huang, Y.-Z. Jiang, Y. Yin, W.-C. Yeh, V. Y. Y. Chung, and C.-M. Lai, “Multi Objective Scheduling in Cloud Computing Using MOSSO,” in 2018 IEEE Congress on Evolutionary Computation (CEC), pp. 1–8 (2018)

[51]

Zuo L, Shu L, Dong S, Zhu C, and Hara T A multi-objective optimization scheduling method based on the ant colony algorithm in cloud computing IEEE Access 2015 3 2687-2699

[52]

Zuo L, Shu L, Dong S, Chen Y, and Yan L A multi-objective hybrid cloud resource scheduling method based on deadline and cost constraints IEEE Access 2017 5 22067-22080

[53]

Chen, Z.-G., Zhan, Z.-H., Lin, Y., Gong, Y.-J., Gu, T.-L., Zhao, F., Yuan, H.-Q., Chen, X., Li, Q., Zhang, J.: Multi-objective cloud workflow scheduling: a multiple populations ant colony system approach. IEEE transactions on cybernetics. 1–15 (2018)

[54]

Masdari M, Barshande S, and Ozdemir S CDABC: chaotic discrete artificial bee colony algorithm for multi-level clustering in large-scale WSNs J. Supercomput. 2019 75 7174-7208

[55]

Jena RTask scheduling in cloud environment: a multi-objective ABC frameworkJ. Inf. Optim. Sci.2017381-193610363

[56]

O. Udomkasemsub, L. Xiaorong, and T. Achalakul, “A multiple-objective workflow scheduling framework for cloud data analytics,” in Computer Science and Software Engineering (JCSSE), 2012 International Joint Conference on, pp. 391–398 (2012)

[57]

Kaur M and Kadam S A novel multi-objective bacteria foraging optimization algorithm (MOBFOA) for multi-objective scheduling Appl. Soft Comput. 2018 66 183-195

[58]

Srichandan S, Kumar TA, and Bibhudatta S Task scheduling for cloud computing using multi-objective hybrid bacteria foraging algorithm Future Computing and Informatics Journal 2018 3 210-230

[59]

D. Gabi, A. Zainal, A. S. Ismail, and Z. Zakaria, “Scalability-Aware scheduling optimization algorithm for multi-objective cloud task scheduling problem,” in 2017 6th ICT International Student Project Conference (ICT-ISPC), pp. 1–6 (2017)

[60]

Xu H, Yang B, Qi W, and Ahene E A multi-objective optimization approach to workflow scheduling in clouds considering fault recovery KSII Transactions on Internet and Information Systems (TIIS) 2016 10 976-995

[61]

Zhang M, Li H, Liu L, and Buyya R An adaptive multi-objective evolutionary algorithm for constrained workflow scheduling in clouds Distributed and Parallel Databases 2018 36 339-368

[62]

Bindu GH, Ramani K, and Bindu CS Energy aware multi objective genetic algorithm for task scheduling in cloud computing International Journal of Internet Protocol Technology 2018 11 242-249

[63]

Vila, S., Guirado, F., Lerida, J.L., Cores, F.: Energy-saving scheduling on IaaS HPC cloud environments based on a multi-objective genetic algorithm. J. Supercomput. 1–13 (2018)

[64]

M. Geethanjali, J. A. J. Sujana, and T. Revathi, “Ensuring truthfulness for scheduling multi-objective real time tasks in multi cloud environments,” in Recent Trends in Information Technology (ICRTIT), 2014 International Conference on, pp. 1–7 (2014)

[65]

Szabo C, Sheng QZ, Kroeger T, Zhang Y, and Yu J Science in the cloud: allocation and execution of data-intensive scientific workflows Journal of Grid Computing 2014 12 245-264

[66]

Kessaci Y, Melab N, and Talbi E-G A Pareto-based metaheuristic for scheduling HPC applications on a geographically distributed cloud federation Clust. Comput. 2013 16 451-468

[67]

Tao F, Feng Y, Zhang L, and Liao TW CLPS-GA: a case library and Pareto solution-based hybrid genetic algorithm for energy-aware cloud service scheduling Appl. Soft Comput. 2014 19 264-279

[68]

A. A. Beegom and M. Rajasree, “A particle swarm optimization based pareto optimal task scheduling in cloud computing,” in International Conference in Swarm Intelligence, pp. 79–86 (2014)

[69]

F. Azimzadeh and F. Biabani, “Multi-objective job scheduling algorithm in cloud computing based on reliability and time,” in 2017 3th International Conference on Web Research (ICWR), pp. 96–101 (2017)

[70]

Y. Kessaci, N. Melab, and E.-G. Talbi, “A pareto-based GA for scheduling HPC applications on distributed cloud infrastructures,” in High Performance Computing and Simulation (HPCS), 2011 International Conference on, pp. 456–462 (2011)

[71]

Ye X, Liu S, Yin Y, and Jin Y User-oriented many-objective cloud workflow scheduling based on an improved knee point driven evolutionary algorithm Knowl.-Based Syst. 2017 135 113-124

[72]

Mezmaz M, Melab N, Kessaci Y, Lee YC, Talbi E-G, Zomaya AY, and Tuyttens D A parallel bi-objective hybrid metaheuristic for energy-aware scheduling for cloud computing systems Journal of Parallel and Distributed Computing 2011 71 1497-1508

[73]

K. Sreenu and S. Malempati, “FGMTS: fractional grey wolf optimizer for multi-objective task scheduling strategy in cloud computing,” Journal of Intelligent & Fuzzy Systems, pp. 1–14, (2018)

[74]

Khalili A and Babamir SM Optimal scheduling workflows in cloud computing environment using Pareto-based Grey wolf optimizer Concurrency and Computation: Practice and Experience 2017 29

[75]

G. Ismayilov and H. R. Topcuoglu, “Dynamic Multi-objective Workflow Scheduling for Cloud Computing Based on Evolutionary Algorithms,” in 2018 IEEE/ACM International Conference on Utility and Cloud Computing Companion (UCC Companion), pp. 103–108 (2018)

[76]

Wang X, Wang Y, and Cui Y A new multi-objective bi-level programming model for energy and locality aware multi-job scheduling in cloud computing Futur. Gener. Comput. Syst. 2014 36 91-101

[77]

Lei H, Wang R, Zhang T, Liu Y, and Zha YA multi-objective coevolutionary algorithm for energy-efficient scheduling on a green data centerComput. Oper. Res.201675103-11735210261349.90370

[78]

Fard HM, Prodan R, and Fahringer TMulti-objective list scheduling of workflow applications in distributed computing infrastructuresJournal of Parallel and Distributed Computing2014742152-21651283.68111

[79]

Sofia AS and GaneshKumar P Multi-objective task scheduling to minimize energy consumption and makespan of cloud computing using NSGA-II J. Netw. Syst. Manag. 2018 26 463-485

[80]

Liu J, Pacitti E, Valduriez P, De Oliveira D, and Mattoso M Multi-objective scheduling of scientific workflows in multisite clouds Futur. Gener. Comput. Syst. 2016 63 76-95

[81]

Zhu Z, Zhang G, Li M, and Liu X Evolutionary multi-objective workflow scheduling in cloud IEEE Transactions on parallel and distributed Systems 2016 27 1344-1357

[82]

Lakra AV and Yadav DK Multi-objective tasks scheduling algorithm for cloud computing throughput optimization Procedia Computer Science 2015 48 107-113

[83]

Ding S, Chen C, Xin B, and Pardalos PM A bi-objective load balancing model in a distributed simulation system using NSGA-II and MOPSO approaches Appl. Soft Comput. 2018 63 249-267

[84]

J. Gasior and F. Seredynski, “Multi-objective security driven job scheduling for computational cloud systems,” in P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC), 2013 Eighth International Conference on, pp. 582–587 (2013)

[85]

R. D. Friese, “Efficient genetic algorithm encoding for large-scale multi-objective resource allocation,” in 2016 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), pp. 1360–1369 (2016)

[86]

Liu Q, Cai W, Shen J, Fu Z, Liu X, and Linge N A speculative approach to spatial-temporal efficiency with multi-objective optimization in a heterogeneous cloud environment Security and Communication Networks 2016 9 4002-4012

[87]

P. T. Thant, C. Powell, M. Schlueter, and M. Munetomo, “Multi-objective level-wise scientific workflow optimization in IaaS public cloud environment,” Scientific programming, vol. 2017, (2017)

[88]

S. Nesmachnow, S. Iturriaga, B. Dorronsoro, and A. Tchernykh, “Multi-objective energy-aware workflow scheduling in distributed datacenters,” in International Conference on Supercomputing, pp. 79–93 (2015)

[89]

He H, Xu G, Pang S, and Zhao Z AMTS: adaptive multi-objective task scheduling strategy in cloud computing China Communications 2016 13 162-171

[90]

E. S. Alkayal, N. R. Jennings, and M. F. Abulkhair, “Efficient task scheduling multi-objective particle swarm optimization in cloud computing,” in Local Computer Networks Workshops (LCN Workshops), IEEE 41st Conference on, 2016, Pp. 17–24 (2016)

[91]

Ramezani F, Lu J, Taheri J, and Hussain FK Evolutionary algorithm-based multi-objective task scheduling optimization model in cloud environments World Wide Web 2015 18 1737-1757

[92]

Jena R Multi objective task scheduling in cloud environment using nested PSO framework Procedia Computer Science 2015 57 1219-1227

[93]

M. Feng, X. Wang, Y. Zhang, and J. Li, “Multi-objective particle swarm optimization for resource allocation in cloud computing,” in Cloud Computing and Intelligent Systems (CCIS), 2012 IEEE 2nd International Conference on, pp. 1161–1165 (2012)

[94]

H.-H. Li, Z.-G. Chen, Z.-H. Zhan, K.-J. Du, and J. Zhang, “Renumber coevolutionary multiswarm particle swarm optimization for multi-objective workflow scheduling on cloud computing environment,” in Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation, pp. 1419–1420 (2015)

[95]

Yao G, Ding Y, Jin Y, and Hao K Endocrine-based coevolutionary multi-swarm for multi-objective workflow scheduling in a cloud system Soft. Comput. 2017 21 4309-4322

[96]

R. Gupta, V. Gajera, and P. K. Jana, “An effective multi-objective workflow scheduling in cloud computing: a PSO based approach,” in 2016 Ninth International Conference on Contemporary Computing (IC3), pp. 1–6 (2016)

[97]

Yao G-s, Ding Y-s, and Hao K-r Multi-objective workflow scheduling in cloud system based on cooperative multi-swarm optimization algorithm J. Cent. South Univ. 2017 24 1050-1062

Cited By

Zhang ZXu CXu SHuang LZhang J(2024)Towards optimized scheduling and allocation of heterogeneous resource via graph-enhanced EPSO algorithmJournal of Cloud Computing: Advances, Systems and Applications10.1186/s13677-024-00670-413:1Online publication date: 23-May-2024
https://dl.acm.org/doi/10.1186/s13677-024-00670-4
Fan LZhou LWang M(2024)Optimizing data regeneration and storage with data dependency for cloud scientific workflow systemsExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.121984238:PDOnline publication date: 15-Mar-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.121984
Suresh PKeerthika PManjula Devi RKamalam GLogeswaran KSadasivuni KDevendran K(2024)Optimized task scheduling approach with fault tolerant load balancing using multi-objective cat swarm optimization for multi-cloud environmentApplied Soft Computing10.1016/j.asoc.2024.112129165:COnline publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1016/j.asoc.2024.112129
Show More Cited By

Recommendations

Multi-objective workflow scheduling in Amazon EC2

Nowadays, scientists and companies are confronted with multiple competing goals such as makespan in high-performance computing and economic cost in Clouds that have to be simultaneously optimised. Multi-objective scheduling of scientific applications in ...
A Pareto Front grid guided multi-objective evolutionary algorithm
Abstract
For multi-objective optimization problems with irregular Pareto Fronts, most widely used decomposition methods in MOEA/D (multi-objective evolutionary algorithms based on decomposition) have shown to be lack of the ability to balance ...
Highlights
- In order to improve the computational efficiency, we propose a new concept namely Pareto Front grid, which is defined as the grid which contains the best ...
Enhanced hybrid multi-objective workflow scheduling approach based artificial bee colony in cloud computing
Abstract
This paper presents a hybrid approach based Binary Artificial Bee Colony (BABC) and Pareto Dominance strategy for scheduling workflow applications considering different Quality of Services (QoS) requirements in cloud computing. The main purpose is ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Journal of Grid Computing

Journal of Grid Computing Volume 18, Issue 3

Sep 2020

241 pages

ISSN:1570-7873

Issue’s Table of Contents

© Springer Nature B.V. 2020.

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 September 2020

Accepted: 20 August 2020

Received: 15 April 2019

Author Tags

Qualifiers

Review-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

14
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 06 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Zhang ZXu CXu SHuang LZhang J(2024)Towards optimized scheduling and allocation of heterogeneous resource via graph-enhanced EPSO algorithmJournal of Cloud Computing: Advances, Systems and Applications10.1186/s13677-024-00670-413:1Online publication date: 23-May-2024
https://dl.acm.org/doi/10.1186/s13677-024-00670-4
Fan LZhou LWang M(2024)Optimizing data regeneration and storage with data dependency for cloud scientific workflow systemsExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.121984238:PDOnline publication date: 15-Mar-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.121984
Suresh PKeerthika PManjula Devi RKamalam GLogeswaran KSadasivuni KDevendran K(2024)Optimized task scheduling approach with fault tolerant load balancing using multi-objective cat swarm optimization for multi-cloud environmentApplied Soft Computing10.1016/j.asoc.2024.112129165:COnline publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1016/j.asoc.2024.112129
Alam MShahid MMustajab S(2024)Security challenges for workflow allocation model in cloud computing environment: a comprehensive survey, framework, taxonomy, open issues, and future directionsThe Journal of Supercomputing10.1007/s11227-023-05873-180:8(11491-11555)Online publication date: 1-May-2024
https://dl.acm.org/doi/10.1007/s11227-023-05873-1
Khaledian NVoelp MAzizi SShirvani M(2024)AI-based & heuristic workflow scheduling in cloud and fog computing: a systematic reviewCluster Computing10.1007/s10586-024-04442-227:8(10265-10298)Online publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1007/s10586-024-04442-2
Osmanpoor MShameli-Sendi AFaraji Daneshgar F(2024)Convergence of the Harris hawks optimization algorithm and fuzzy system for cloud-based task scheduling enhancementCluster Computing10.1007/s10586-023-04225-127:4(4909-4923)Online publication date: 9-Jan-2024
https://dl.acm.org/doi/10.1007/s10586-023-04225-1
Qin SPi DShao ZXu Y(2023)A Discrete Interval-Based Multi-Objective Memetic Algorithm for Scheduling Workflow With Uncertainty in Cloud EnvironmentIEEE Transactions on Network and Service Management10.1109/TNSM.2022.322415820:3(3020-3037)Online publication date: 1-Sep-2023
https://dl.acm.org/doi/10.1109/TNSM.2022.3224158
Rajak NRajak RPrakash S(2022)A Workflow Scheduling Method for Cloud Computing PlatformWireless Personal Communications: An International Journal10.1007/s11277-022-09882-w126:4(3625-3647)Online publication date: 1-Oct-2022
https://dl.acm.org/doi/10.1007/s11277-022-09882-w
Dogani JKhunjush FSeydali M(2022)K-AGRUED: A Container Autoscaling Technique for Cloud-based Web Applications in Kubernetes Using Attention-based GRU Encoder-DecoderJournal of Grid Computing10.1007/s10723-022-09634-x20:4Online publication date: 1-Dec-2022
https://dl.acm.org/doi/10.1007/s10723-022-09634-x
Ghafari RKabutarkhani FMansouri N(2022)Task scheduling algorithms for energy optimization in cloud environment: a comprehensive reviewCluster Computing10.1007/s10586-021-03512-z25:2(1035-1093)Online publication date: 1-Apr-2022
https://dl.acm.org/doi/10.1007/s10586-021-03512-z
Show More Cited By

View Options

View options

Media

Figures

Other

Tables

View Issue’s Table of Contents