Research on Worker Allocation Optimization Based on Real-Time Data in Cloud Computing
DOI:
https://doi.org/10.54691/sp9z1f25Keywords:
Cloud computing; Worker allocation; Real time data; Multi objective optimization.Abstract
The core of cloud computing task scheduling optimization for real-time data is to monitor the workload, task requirements, and resource utilization status of the real-time monitoring system, and then flexibly adjust the task allocation plan to improve resource utilization efficiency and accelerate response time. This article deeply analyzes the application of multi-objective optimization technology, optimization of data transmission and processing flow, changes in scheduling strategies based on real-time data, and the improvement of intelligent and adaptive task assignment capabilities, and constructs a comprehensive optimization architecture. By adopting these methods, the performance of cloud computing platforms can be significantly enhanced, task processing latency can be reduced, and scientific allocation of resources can be achieved.
Downloads
References
[1] Alanagh A Y ,Firouzi M ,Kenari R A , et al.Introducing an adaptive model for auto‐scaling cloud computing based on workload classification[J].Concurrency and Computation: Practice and Experience, 2023,35(22):
[2] Min C ,Yaoyu L ,Xupeng W , et al.Energy-aware intelligent scheduling for deadline-constrained workflows in sustainable cloud computing[J].Egyptian Informatics Journal,2023,24(2):277-290.
[3] Youssef S ,Soufiane J ,Said K E , et al.Reducing energy footprint in cloud computing: a study on the impact of clustering techniques and scheduling algorithms for scientific workflows [J]. Computing, 2023,105(10):2231-2261.
[4] Youssef S ,Soufiane J ,Said K E , et al.Reducing energy footprint in cloud computing: a study on the impact of clustering techniques and scheduling algorithms for scientific workflows [J]. Computing, 2023, 105(10):2231-2261.
[5] Buyya R ,Ilager S ,Arroba P .Energy‐efficiency and sustainability in new generation cloud computing: A vision and directions for integrated management of data centre resources and workloads [J]. Software: Practice and Experience,2023,54(1):24-38.
[6] Ahmad Z ,Acarer T ,Kim W .Optimization of Maritime Communication Workflow Execution with a Task-Oriented Scheduling Framework in Cloud Computing[J].Journal of Marine Science and Engineering,2023,11(11):
[7] Mirsaeid S H .A survey study on task scheduling schemes for workflow executions in cloud computing environment: classification and challenges[J].The Journal of Supercomputing, 2023, 80(7): 9384-9437.
[8] VermaP ,MauryaK A ,YadavS R .A survey on energy‐efficient workflow scheduling algorithms in cloud computing[J].Software: Practice and Experience,2023,54(5):637-682.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Frontiers in Science and Engineering
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
