The International Arab Journal of Information Technology (IAJIT)


A Novel Resource Scheduler for Resource Allocation and Scheduling in Big Data Using Hybrid Optimization Algorithm at Cloud Environment

Big Medical Data (BMD) is generated by cellular telephones, clinics, academics, suppliers, and organizations. Collecting, finding, analyzing, and managing the big data to make people's lives better, comprehending novel illnesses, and treatments, predicting results at initial phases, and making real-time choices are the actual issues in healthcare systems. Dealing with big medical data in resource scheduling is a major issue that aims to offer higher quality healthcare services. Hadoop MapReduce has been widely used for parallel processing of large data tasks and efficient job scheduling. The number of big data tasks is constantly growing; it is becoming more essential to minimize their energy usage to reduce the environmental effect and operating expenses. Hence to overcome these disadvantages, we propose a novel resource scheduler for big data using a Hybrid 2-GW Optimization Algorithm (H2-GWOA). We employ the Improved GlowWorm Swarm Optimization Algorithm (IGSOA) and Mean GreyWolf Optimization Algorithm (MGWOA) for optimizing the MapReduce framework in heterogeneous big data. The CloudSim platform was used for the simulations. The performance of the proposed scheduler is proved to be better than the conventional methods in terms of metrics like latency, makespan, resource utilization, skewness, and Central Processing Unit (CPU) consumption.

[1] Aggarwal A., Dimri P., Agarwal A., Verma M., and Alhumyani H., “IFFO: An Improved Fruit Fly Optimization Algorithm for Multiple Workflow Scheduling Minimizing Cost and Makespan in Cloud Computing Environments,” Mathematical Problems in Engineering, vol. 2021, pp. 1-9, 2021.

[2] Calheiros R., Ranjan R., Beloglazov A., Rose C., and Buyya R., “Cloudsim: A Toolkit for Modeling and Simulation of Cloud Computing Environments and Evaluation of Resource Provisioning Algorithms,” Software Practice and Experience, vol. 41, no. 1, pp. 23-50, 2011.

[3] Cao B., Wang K., Xu J., Hou C., and Fan J., “Dynamic Pricing for Resource Consumption in Cloud Service,” Wireless Communications and Mobile Computing, vol. 2018, pp. 1-11, 2018. Https://Doi.Org/10.1155/2018/4263831

[4] Chen X., Wang H., Ma Y., Zheng X., and Guo L., “Self-Adaptive Resource Allocation for Cloud- Based Software Services Based on Iterative Qos Prediction Model,” Future Generation Computer Systems, vol. 105, pp. 287-296, 2020.

[5] Chou L., Chen H., Tseng F., Chao H., and Chang (23) 2 12QPnQn   872 The International Arab Journal of Information Technology, Vol. 20, No. 6, November 2023 Y., “DPRA: Dynamic Power-Saving Resource Allocation for Cloud Data Center Using Particle Swarm Optimization,” IEEE Systems Journal, vol. 12, no. 2, pp. 1554-1565, 2018. doi: 10.1109/JSYST.2016.2596299.

[6] Galetsi P., Katsaliaki K., and Kumar S., “Big Data Analytics in Health Sector: Theoretical Framework, Techniques and Prospects,” International Journal of Information Management, vol. 50, pp. 206-216, 2020.

[7] Haghighi A., Heydari S., and Shahbazpanahi S., “Dynamic Qos-Aware Resource Assignment in Cloud-Based Content-Delivery Networks,” IEEE Access, vol. 6, pp. 2298-2309, 2018. doi: 10.1109/ACCESS.2017.2782776.

[8] Horri A., Mozafari M., and Dastghaibyfard G., “Novel Resource Allocation Algorithms to Performance and Energy Efficiency in Cloud Computing,” The Journal of Supercomputing, vol. 69, no. 3, pp. 1445-1461, 2014.

[9] Kumar M. and Sharma S., “PSO-COGENT: Cost and Energy Efficient Scheduling in Cloud Environment with Deadline Constraint,” Sustainable Computing: Informatics and Systems, vol. 19, pp. 147-164, 2018.

[10] Lakkadwala P. and Kanungo P., “Memory Utilization Techniques for Cloud Resource Management in Cloud Computing Environment: A Survey,” International Conference on Computing Communication and Automation, Greater Noida, pp. 1-5, 2018. doi: 10.1109/CCAA.2018.8777457

[11] Lee H., Jeong Y., and Jang H., “Performance Analysis Based Resource Allocation for Green Cloud Computing,” The Journal of Supercomputing, vol. 69, no. 3, pp. 1013-1026, 2014.

[12] Lim A., Ma H., Rodrigues B., Tan S., and Xiao F., “New Meta-Heuristics for the Resource- Constrained Project Scheduling Problem,” Flexible Services and Manufacturing Journal, vol. 25, no. 1-2, pp. 48-73, 2013.

[13] Liu H., Liu S., and Zheng K., “A Reinforcement Learning-Based Resource Allocation Scheme for Cloud Robotics,” IEEE Access, vol. 6, pp. 17215- 17222, 2018.

[14] Mishra M., Das A., Kulkarni P., and Sahoo A., “Dynamic Resource Management Using Virtual Machine Migrations,” IEEE Communication Magazine, vol. 50, no. 9, pp. 34-40, 2012. 10.1109/MCOM.2012.6295709

[15] Peng Z., Barzegar B., Yarahmadi M., Motameni H., and Pirouzmand P., “Energy-Aware Scheduling of Workflow Using a Heuristic Method on Green Cloud,” Scientific Programming, vol. 2020, pp. 1-14, 2020.

[16] Qi L., Chen Y., Yuan Y., Fu S., Zhang X., and Xu X., “A Qos-Aware Virtual Machine Scheduling Method for Energy Conservation in Cloud-Based Cyber-Physical Systems,” World Wide Web, vol. 23, no. 2, pp. 1275-1297, 2020.

[17] Shukur H., Zeebaree S., Zebari R., Zeebaree D., Ahmed O., and Salih A., “Cloud Computing Virtualization of Resources Allocation for Distributed Systems,” JASTT, vol. 1, no. 3, pp. 98- 105, 2020.

[18] Stanik A., Koerner M., and Lymberopoulos L., “SLA-driven Federated Cloud Networking: Quality of Service for Cloud-Based Software Defined Networks,” Procedia Computer Science, vol. 34, pp. 655-660, 2014.

[19] Subhash L. and Udayakumar R., “Sunflower Whale Optimization Algorithm for Resource Allocation Strategy in Cloud Computing Platform,” Wireless Personal Communications, vol. 116, no. 4, pp. 3061-3080, 2021.

[20] Tian W., He M., Guo W., Huang W., and Shi X., “On Minimizing Total Energy Consumption in the Scheduling of Virtual Machine Reservations,” Journal of Network and Computer Applications, vol. 113, pp. 64-74, 2018.

[21] Tong S., Liu Y., Cho H., Chiang H., and Zhang Z., “Joint Radio Resource Allocation in Fog Radio Access Network for Healthcare,” Peer-to-Peer Networking and Applications, vol. 12, no. 5, pp. 1277-1288, 2019. 018-0707-4

[22] Xu X., Tang M., and Tian Y., “Qos-Guaranteed Resource Provisioning for Cloud-Based Mapreduce in Dynamical Environments,” Future Generation Computer Systems, vol. 78, pp. 18-30, 2018.

[23] Xu X., Fu S., Cai Q., Tian W. and Liu W., “Dynamic Resource Allocation for Load Balancing in Fog Environment,” Wireless Communications and Mobile Computing, vol. 2018, pp. 1-15, 2018.

[24] Yang C., Chen S., Liu J., Chan Y., Chen C., and Verma V., “An Energy-Efficient Cloud System with Novel Dynamic Resource Allocation Methods,” Journal of Supercomput, vol. 75, no. 8, pp. 4408-4429, 2019.