A Predictive Priority-Based Dynamic Resource Provisioning Scheme With Load Balancing in Heterogeneous Cloud Computing

Sohani, Mayank; Jain, Shobhit

doi:10.1109/access.2021.3074833

Cited by 28 publications

(7 citation statements)

References 52 publications

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“…Cloud computing is a product of the highly developed network information technology, all available resources are shared in the form of "cloud", and users can apply to the "cloud" to provide required services and resources through the network [16]. The resources in the cloud computing platform are heterogeneous and dynamic, when scheduling and resource allocation of large-scale data tasks, the completion time and throughput of the applied system need to be considered, and the load balancing of system resources needs to be considered, therefore, the research on resource scheduling of cloud computing platform is also a difficult problem in the current research community [17]. Taking the cloud computing platform as the basis, the smart city management system is designed to meet the development requirements of the modern city process [18].…”

Section: Literature Reviewmentioning

confidence: 99%

Information Extraction and Data Planning of Smart City Based on Internet of Things

Farisi

2022

Journal of Sensors

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In order to solve the problem of slow resource scheduling in the process of smart city management, the author proposes a smart city information extraction and data planning system based on the Internet of Things. IoT requires different technologies for analysis for different data types. Managers use different IoT applications to analyze data from different devices and integrate relevant data for possible machine failure or emergency in smart home applications. Situation is predicted. The system includes a cloud platform intelligence center, uses the cloud management module to monitor various hardware devices, constructs the cloud computing resource scheduling objective function, uses the cultural particle swarm algorithm to solve the objective function, and obtains the cloud computing resource scheduling scheme. The experimental results show that the overall utilization rate of the system is the best, close to 100%. Conclusion. The system can realize the effective management of the smart city and monitor the city situation in real time. When implementing resource scheduling, the task completion time is short, the system utilization rate is high, and the resources can be maximized.

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Section: Literature Reviewmentioning

confidence: 99%

Information Extraction and Data Planning of Smart City Based on Internet of Things

Farisi

2022

Journal of Sensors

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“…To enhance the sustainability of data centers, cloud service providers employ proactive analysis of resource usage, coupled with the prediction of anticipated [127], [258]- [261]. The overarching strategy for sustainable resource management through predictive resource analysis is depicted in Fig.…”

Section: Resource Prediction-based Managementmentioning

confidence: 99%

A Comprehensive Survey on Sustainable Resource Management in Cloud Computing Environments

Saxena,

Singh

2024

Preprint

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“…Methodology used Parameters addressed Mustafa et al [7] ECTC Energy consumption, migrations, SLA violations Chen et al [8] OWC-A2C Resource utilization, execution costs Wang et al [9] IMPSO Makespan, cost Yao et al [10] TDSA Makespan, resource utilization Zhang et al [11] RMREC Energy consumption Malik et al [12] PSO based scheduler Makespan, energy consumption, load balancing Anitha and Vidyaraj [13] Clustering approach Prediction of SLA violation, workload Faragardi et al [14] GRP-HEFT Makespan Aktan and Bulut [15] DESA Task completion time, load balancing Hu et al [16] MOACO, CCA CPU utilization, response time Yao et al [10] TDSA Makespan Cui and Xiaoqing [17] Two-dimensional coding Execution costs Sohani and Jain [25] PMHEFT Makespan, efficiency, power consumption Mustafa et al [26] SLAAEERM Energy efficiency, SLA compliance. Talouki et al [18] TDA Makespan, speedup Talouki et al [19] Hybrid GA Makespan Shirvani and Talouki [20] HH-LiSch SLR, makespan, speedup Shirvani [21] HDPSO SLR, speedup, efficiency Tanha et al [22] TSAA SLR, speedup, makespan Shirvani and Talouki [23] Bi-objective scheduling Monetary cost, speedup, makespan Alaie et al [24] HDCSA SLR, speedup, efficiency GRP-HEFT, greedy resource provisioning and modified heterogeneous earliest finish time; TDSA, task driven Scheduling algorithm; SLR, scheduling length ratio; IMPSO, immune mutation particle swarm optimization; CCA, cheetah chase algorithm; MOACO; modified ant colony optimization.…”

Section: Authorsmentioning

confidence: 99%

Multiobjective Prioritized Workflow Scheduling in Cloud Computing Using Cuckoo Search Algorithm

Sahu

Swain

Mangalampalli

et al. 2023

Applied Bionics and Biomechanics

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Effective workflow scheduling in cloud computing is still a challenging problem as incoming workflows to cloud console having variable task processing capacities and dependencies as they will arise from various heterogeneous resources. Ineffective scheduling of workflows to virtual resources in cloud environment leads to violations in service level agreements and high energy consumption, which impacts the quality of service of cloud provider. Many existing authors developed workflow scheduling algorithms addressing operational costs and makespan, but still, there is a provision to improve the scheduling process in cloud paradigm as it is an nondeterministic polynomial-hard problem. Therefore, in this research, a task-prioritized multiobjective workflow scheduling algorithm was developed by using cuckoo search algorithm to precisely map incoming workflows onto corresponding virtual resources. Extensive simulations were carried out on workflowsim using randomly generated workflows from simulator. For evaluating the efficacy of our proposed approach, we compared our proposed scheduling algorithm with existing approaches, i.e., Max–Min, first come first serve, minimum completion time, Min–Min, resource allocation security with efficient task scheduling in cloud computing-hybrid machine learning, and Round Robin. Our proposed approach is outperformed by minimizing energy consumption by 15% and reducing service level agreement violations by 22%.

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A Predictive Priority-Based Dynamic Resource Provisioning Scheme With Load Balancing in Heterogeneous Cloud Computing

Cited by 28 publications

References 52 publications

Information Extraction and Data Planning of Smart City Based on Internet of Things

Information Extraction and Data Planning of Smart City Based on Internet of Things

A Comprehensive Survey on Sustainable Resource Management in Cloud Computing Environments

Multiobjective Prioritized Workflow Scheduling in Cloud Computing Using Cuckoo Search Algorithm

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