Multidimensional QoS Resource Scheduling Method with Stakeholder Perspective in Clouds

Su, Mingfeng; Pin, Liu; Wang, Guojun

doi:10.1109/smartworld.2018.00182

Cited by 3 publications

(2 citation statements)

References 21 publications

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“…The hardware resource parameters of the edge servers are shown in Table 3. The benchmark algorithms for task scheduling include BAO (the benchmark task scheduling algorithm with OREO) [50] and BAF (the benchmark task scheduling algorithm with FIFO) [51,52]. Task scheduling evaluation metrics include ATCT, OSSE, and TTDR.…”

Section: Experimental Evaluationmentioning

confidence: 99%

Prediction-Based Resource Deployment and Task Scheduling in Edge-Cloud Collaborative Computing

Wang

Choo

2022

Wireless Communications and Mobile Computing

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Edge computing is becoming increasingly commonplace, as consumer devices become more computationally capable and network connectivity improves (e.g., due to 5G). With the rapid development of edge computing and Internet of Things (IoT), the use of edge-cloud collaborative computing to provide service-oriented network application (i.e., task) in edge-cloud IoT has become an important research topic. In this paper, we present an edge-cloud collaborative computing framework and our resource deployment algorithm with task prediction (RDAP). Based on our paradigm, tasks in the cloud service center are predicted using the two-dimensional time series, and task classification aggregation and delay threshold determination are combined to optimize task resource deployment of edge servers. A task scheduling algorithm with Pareto improvement (TSAP) is also proposed. At the edge servers, the Pareto progressive comparison is conducted in two stages to obtain the tangent point or any intersection point of the two objective curves of user’s quality of service and effect of system service to optimize task scheduling. The experimental results show that for varying user task scales and different Zipf distribution α parameters, combining RDAP and TSAP (RDAP-TSAP) can improve the average user task hit rate. In addition, the average task completion time of users, the overall system service effect, and the total task delay rate of RDAP-TSAP are better than TSAP and the benchmark algorithms for task scheduling.

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Section: Experimental Evaluationmentioning

confidence: 99%

Prediction-Based Resource Deployment and Task Scheduling in Edge-Cloud Collaborative Computing

Wang

Choo

2022

Wireless Communications and Mobile Computing

Self Cite

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“…Static methods mainly include round-robin scheduling algorithms [2], and the idea is to follow the grid computing scheduling working principle of distributed systems, with long computation time and high complexity, which cannot be adapted to large-scale cloud computing task scheduling and easy imbalance of resource load. Dynamic methods mainly have some swarm intelligence optimization algorithms, whose path solving speed is faster and scheduling scheme is better than static methods, which become the main research direction at present [3]. The genetic algorithms, particle swarm algorithms, and ant colony algorithms all have corresponding advantages and disadvantages [4].…”

Section: Introductionmentioning

confidence: 99%

Cloud Resource Scheduling Method based on Markov Process and the Cuckoo Search

Zhang

Wang

et al. 2022

J. Phys.: Conf. Ser.

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With the rapid development of cloud computing, the computing reliability of cloud servers has become an important research area. To improve the load balancing capability of cloud servers, an autonomous adaptive architecture cloud computing framework based on software-defined networking and network function virtualization were proposed. And a cloud resource scheduling method based on the Markov process model and cuckoo search algorithm to realize the load balancing of cloud nodes was designed. The cloud platform verified by the experiment shows that the method can effectively guarantee the service reliability of cloud computing.

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A Cloud Resource Allocation Strategy with Entry Control for Multi-priority Cloud Requests

Zhao

Chen

et al. 2023

Arab J Sci Eng

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Multidimensional QoS Resource Scheduling Method with Stakeholder Perspective in Clouds

Cited by 3 publications

References 21 publications

Prediction-Based Resource Deployment and Task Scheduling in Edge-Cloud Collaborative Computing

Prediction-Based Resource Deployment and Task Scheduling in Edge-Cloud Collaborative Computing

Cloud Resource Scheduling Method based on Markov Process and the Cuckoo Search

A Cloud Resource Allocation Strategy with Entry Control for Multi-priority Cloud Requests

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