Hybrid metaheuristic model based performance-aware optimization for map reduce scheduling

References

• Chen C, Lin J, Kuo S. Mapreduce scheduling for deadline-constrained jobs in heterogeneous cloud computing systems. IEEE Trans Cloud Comput. 1 Jan.-March 2018;6(1):127–140. doi:10.1109/TCC.2015.2474403
   Web of Science ®Google Scholar
• Fan Y, Liu W, Guo D, et al. Shuffle scheduling for MapReduce jobs based on periodic network status. IEEE/ACM Trans Netw. Aug. 2020;28(4):1832–1844. doi:10.1109/TNET.2020.2993945
   Web of Science ®Google Scholar
• Cheng D, Zhou X, Xu Y, et al. Deadline-aware MapReduce job scheduling with dynamic resource availability. IEEE Trans Parallel Distribut Syst. 1 April 2019;30(4):814–826. doi:10.1109/TPDS.2018.2873373
   Web of Science ®Google Scholar
• Zhao H, Zheng Q, Zhang W, et al. Prediction-based and locality-aware task scheduling for parallelizing video transcoding over heterogeneous MapReduce cluster. IEEE Trans Circuits Syst Video Technol. April 2018;28(4):1009–1020. doi:10.1109/TCSVT.2016.2634579
   Web of Science ®Google Scholar
• Chen Q, Yao J, Li B, et al. PISCES: optimizing multi-job application execution in MapReduce. IEEE Trans Cloud Comput. 1 Jan.-March 2019;7(1):273–286. doi:10.1109/TCC.2016.2603509
   Web of Science ®Google Scholar
• Li J, Wang J, Lyu B, et al. An improved algorithm for optimizing MapReduce based on locality and overlapping. Tsinghua Sci Technol. December 2018;23(6):744–753. doi:10.26599/TST.2018.9010115
   Web of Science ®Google Scholar
• Hsieh S, Chen C, Chen C, et al. Novel scheduling algorithms for efficient deployment of MapReduce applications in heterogeneous computing environments. IEEE Trans Cloud Comput. 1 Oct.-Dec. 2018;6(4):1080–1095. doi:10.1109/TCC.2016.2552518
   Web of Science ®Google Scholar
• Soualhia M, Khomh F, Tahar S. A dynamic and failure-aware task scheduling framework for Hadoop. IEEE Trans Cloud Comput. 1 April-June 2020;8(2):553–569. doi:10.1109/TCC.2018.2805812
   Web of Science ®Google Scholar
• Chen C, Hung L, Hsieh S, et al. Heterogeneous job allocation scheduler for Hadoop MapReduce using dynamic grouping integrated neighboring search. IEEE Trans Cloud Comput. 1 Jan.-March 2020;8(1):193–206. doi:10.1109/TCC.2017.2748586
   Web of Science ®Google Scholar
• Ehsan M, Chandrasekaran K, Chen Y, et al. Cost-efficient tasks and data co-scheduling with AffordHadoop. IEEE Trans Cloud Comput. 1 July-Sept. 2019;7(3):719–732. doi:10.1109/TCC.2017.2702661
   Web of Science ®Google Scholar
• Wang H, Yu X, Xu H, et al. Integrating coflow and circuit scheduling for optical networks. IEEE Trans Parallel Distrib Syst. 1 June 2019;30(6):1346–1358. doi:10.1109/TPDS.2018.2889251
   Web of Science ®Google Scholar
• Li M, Meng L, Wang J, et al. Application and performance optimization of MapReduce model in image segmentation. IEEE Access. 2020;8:31835–31844. doi:10.1109/ACCESS.2019.2963343
   Web of Science ®Google Scholar
• Ullah I, Khan MS, Amir M, et al. LSTPD: least slack time-based preemptive deadline constraint scheduler for Hadoop clusters. IEEE Access. 2020;8:111751–111762. doi:10.1109/ACCESS.2020.3002565
   Web of Science ®Google Scholar
• Chen W, Zhou X, Rao J. Preemptive and Low latency datacenter scheduling via lightweight containers. IEEE Trans Parallel Distrib Syst. 1 Dec. 2020;31(12):2749–2762. doi:10.1109/TPDS.2019.2957754
   Web of Science ®Google Scholar
• Chandrashekar C, Krishnadoss P, Kedalu Poornachary V, et al. HWACOA scheduler: hybrid weighted ant colony optimization algorithm for task scheduling in cloud computing. Appl Sci. 2023;13:3433), doi:10.3390/app13063433
   Google Scholar
• Chakraborty S, Saha AK, Chhabra A. Improving whale optimization algorithm with elite strategy and its application to engineering-design and cloud task scheduling problems. Cogn Comput. 2023; doi:10.1007/s12559-022-10099-z
   Web of Science ®Google Scholar
• Gandomi A, Reshadi M, Movaghar A, et al. HybSMRP: a hybrid scheduling algorithm in Hadoop MapReduce framework. J Big Data. 2019;6:106), doi:10.1186/s40537-019-0253-9
   Google Scholar
• Harshala Shingne RS. Swarm optimized deep learning scheduling in cloud for resource-intensive Iot systems, 30 May 2023, PREPRINT (Version 1) available at Research Square. doi:10.21203/rs.3.rs-2984667/v1.
   Google Scholar
• Tan H, et al. Joint online coflow routing and scheduling in data center networks. IEEE/ACM Trans Netw. Oct. 2019;27(5):1771–1786. doi:10.1109/TNET.2019.2930721
   Web of Science ®Google Scholar
• Nguyen HK, Khodaei A, Han Z. A big data scale algorithm for optimal scheduling of integrated microgrids. IEEE Trans Smart Grid. Jan. 2018;9(1):274–282. doi:10.1109/TSG.2016.2550422
   Web of Science ®Google Scholar
• Farhat F, Zad Tootaghaj D, He Y, et al. Stochastic modeling and optimization of stragglers. IEEE Trans Cloud Comput. 1 Oct.-Dec. 2018;6(4):1164–1177. doi:10.1109/TCC.2016.2552516
   Web of Science ®Google Scholar
• Amir M, Rahmani M, Conti M. SPO: A secure and performance-aware optimization for MapReduce scheduling. J Netw Comput Applic. 2021;176.
   Web of Science ®Google Scholar
• Oguz S, Demirci GV, Turk A, et al. Locality-aware and load-balanced static task scheduling for MapReduce. Fut Gener Comput Syst. 2018.
   PubMed Web of Science ®Google Scholar
• Medhat D, Yousef AH, Salama C. Cost-aware load balancing for multilingual record linkage using MapReduce. Ain Shams Eng J. 2020.
   Web of Science ®Google Scholar
• Zhaoa L, Li Y, Fogelman-Soulie F, et al. A holistic cross-layer optimization approach for mitigating stragglers in in-memory data processing. J Syst Arch. 2020;111.
   Web of Science ®Google Scholar
• Song J, Ma Z, Thomas R, et al. Energy efficiency optimization in Big data processing platform by improving resources utilization. SUSCOM. 2018;298.
   Google Scholar
• Bagui S, Devulapalli K, Coffey J. A heuristic approach for load balancing the FP-growth algorithm on MapReduce, Array, 2020.
   Google Scholar
• Cheng D, Zhou X, Xu Y, et al. Deadline-aware mapreduce job scheduling with dynamic resource availability, IEEE, 2018.
   Google Scholar
• Guerrero C, Lera I, Juiz C. Migration-aware genetic optimization for MapReduce scheduling and replica placement in Hadoop. J Grid Computing. 2018.
   Web of Science ®Google Scholar
• Mansouri N. An efficient task scheduling based on seagull optimization algorithm for heterogeneous cloud computing platforms. Intern J Eng. 2022;35(2):433–450. doi:10.5829/IJE.2022.35.02B.20
   Google Scholar
• Chhabra A, Huang KC, Bacanin N, et al. Optimizing bag-of-tasks scheduling on cloud data centers using hybrid swarm-intelligence meta-heuristic. J Supercomput. 2022;78(7):9121–9183. doi:10.1007/s11227-021-04199-0
   Web of Science ®Google Scholar
• https://www.javatpoint.com/cloud-computing-architecture
   Google Scholar
• Seddigh M, Taheri H, Sharifian S. “Dynamic prediction scheduling for virtual machine placement via ant colony optimization”. Signal Processing and Intelligent Systems Conference (SPIS) (pp. 104-108). IEEE. 2015, December.
   Google Scholar
• Mirjalili S. Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowl Based Syst. November 2015;89:228–249. doi:10.1016/j.knosys.2015.07.006
   Web of Science ®Google Scholar
• Kaura S, Awasthia LK, Sangala AL, et al. Tunicate Swarm Algorithm: A new bio-inspired based metaheuristic paradigm for global optimization. Eng Applic Artif Intell. 2020;90.
   Web of Science ®Google Scholar
• Kalia K, Gupta N. Analysis of Hadoop MapReduce scheduling in the heterogeneous environment. Ain Shams Eng J. 2021;12(1):1101–1110. doi:10.1016/j.asej.2020.06.009
   Web of Science ®Google Scholar
• Rjoub G, Bentahar J, Wahab OA. Bigtrustscheduling: trust-aware big data task scheduling approach in cloud computing environments. Fut Gener Comput Syst. 2020;110:1079–1097. doi:10.1016/j.future.2019.11.019
   Web of Science ®Google Scholar