Resource Scheduling and Load Balancing Fusion Algorithm with Deep Learning Based on Cloud Computing

Hou, Xiaolu; Zhao, Guozeng

doi:10.4018/ijitwe.2018070104

Cited by 17 publications

(9 citation statements)

References 18 publications

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“…First, the time-frequency feature partition of audio resources is constructed. The resource Scheduling and load balancing fusion algorithm with deep learning based on cloud computing algorithm is used as the traditional comparison method [11] . The results of the two methods are compared.…”

Section: Analysis Of Experimental Resultsmentioning

confidence: 99%

Joint scheduling of audio teaching resources in NFV service chain deployment

Xu¹,

Yu²

2020

Proceedings of the 13th EAI International Conference on Mobile Multimedia Communications, Mobimedia 2020, 27-28 August 2020, Cy

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Section: Analysis Of Experimental Resultsmentioning

confidence: 99%

Joint scheduling of audio teaching resources in NFV service chain deployment

Xu¹,

Yu²

2020

Proceedings of the 13th EAI International Conference on Mobile Multimedia Communications, Mobimedia 2020, 27-28 August 2020, Cy

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“…So, a combined CHA‐WMA technique is used in this research to avoid the weakness that occurred as a result of individual use of these techniques. No precise 35,36 tuning is achieved using the optimum PI parameter. Hence, the CHA‐WMA technique is suggested in this article.…”

Section: Proposed Hybrid Control Methodologymentioning

confidence: 99%

An efficient CHA‐WMA control strategy for a non‐isolated quasi‐Z‐source novel multilevel inverter‐based grid connected PV system

Santhi

Srinivasan

2021

Int J Numerical Modelling

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This manuscript proposes a hybrid approach for non‐isolated quasi Z‐source (QZS)‐novel cascaded multilevel inverter (NIQZS‐NMLI)‐based grid‐connected photovoltaic (PV) system. The proposed hybrid approach is the combined execution of both the color harmony algorithm (CHA) and woodpecker‐mating algorithm (WMA), and hence it is called CHA‐WMA method. Initially, the NIQZS‐NMLI design is developed to gain the optimal control signal using the proposed controller. The NIQZS‐NMLI is developed with the reduced count of switches, diodes, and sources. The major objective of the proposed method is “to satisfy the load power requirement and to maintain the power regulation or maximum energy conversion of the wind and solar subsystems.” The proposed method strictly prohibits the variations present in the output voltage of the NIQZS‐NMLI. Here, the CHA is employed to evaluate the optimal gain parameters in light of the source currents variety from the normal value, also it is useful for generating the optimum control signals dataset in the offline way. In the online way, the WMA predicts the most optimal control signals of the NIQZS‐NMLI in light of the satisfied dataset. The resultant control signals have been utilized for controlling the insulated gate bi‐polar switches (IGBT) of NIQZS‐NMLI. The proposed method is implemented in MATLAB/Simulink site and the performance is analyzed with existing methods. Moreover, the PV performance is analyzed under three test cases. The PV efficiency of the proposed method for all cases is 89.3975%, 90.2653%, and 97.8095%. Overall, the comparison outcomes demonstrate that the proposed method is more efficient than the other existing methods.

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“…In the WSNs, the job scheduling layer mainly deals with the task submitted by the user. According to the needs of the users, the job is scheduled to the corresponding resource [43,44,45]. In order to better describe the method of job scheduling layer, the following definitions are introduced.…”

Section: Job Scheduling Level Methodsmentioning

confidence: 99%

Intelligent Sensor-Cloud in Fog Computer: A Novel Hierarchical Data Job Scheduling Strategy

Sun

Li-li

et al. 2019

Sensors

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In the Fog Computer (FC), the process of data is prone to problems such as low data similarity and poor data tolerance. This paper proposes a hierarchical data job scheduling strategy Based on Intelligent Sensor-Cloud in Fog Computer (HDJS). HDJS dynamically adjusts the priority of the job to avoid job starvation and maximize the use of resources, uses the key frame to the resource occupied information, distributes the frame sequence to the unit, and then combines the intra frame distribution strategy to balance the load between the nodes. The experimental results show our proposed strategy may be possible to avoid the operation of hunger and resource fragmentation problems, make full use of the advantages of multi-core and multi-thread, improve system resource utilization, and shorten the execution time and response time.

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Resource Scheduling and Load Balancing Fusion Algorithm with Deep Learning Based on Cloud Computing

Cited by 17 publications

References 18 publications

Joint scheduling of audio teaching resources in NFV service chain deployment

Joint scheduling of audio teaching resources in NFV service chain deployment

An efficient CHA‐WMA control strategy for a non‐isolated quasi‐Z‐source novel multilevel inverter‐based grid connected PV system

Intelligent Sensor-Cloud in Fog Computer: A Novel Hierarchical Data Job Scheduling Strategy

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