Optimization of virtual resource management for cloud applications to cope with traffic burst

Zhang, Qizhi; Chen, Haopeng; Shen, Yuxi; Ma, Sixiang; Lu, Heng

doi:10.1016/j.future.2015.12.011

Cited by 18 publications

(5 citation statements)

References 7 publications

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“…erefore, the system needs to include four functions of contract declaration, contract payment, contract collection, and contract ledger to further standardize the hospital's experimental project contract management. e third is the fund management function [28]. Currently, special funding related to experiments is managed by the experimental department and there is a shortage of special computerized software.…”

Section: Management Systemmentioning

confidence: 99%

Computer Management Design and Optimization of City Smart Medical Laboratory Service

Jin

Zhang

Fan

et al. 2021

Mathematical Problems in Engineering

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In order to optimize the computer management of smart medical laboratory services and find the optimal solution, we conducted experiments on the laboratory computers of hospitals in this city based on the RBF neural network, which provided references for other researchers. Through the collection of relevant data, this article summarizes and analyzes the existing medical laboratory research, summarizes the existing problems and development directions of the current laboratory, uses the RBF neural network to modify these models, and innovatively achieves a hospital laboratory computer management optimization system with the characteristics of high efficiency, low energy consumption, and fast response. The experimental results prove that the computer management and optimization of laboratory services are optimized through the RBF neural network, and the efficiency of computer management design and optimization is greatly improved. It is about 20% higher than traditional medical laboratory. This shows that the computer management design and optimization of smart medical laboratory services designed by RBF neural network can play an important role in the construction of hospital laboratories.

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Section: Management Systemmentioning

confidence: 99%

Computer Management Design and Optimization of City Smart Medical Laboratory Service

Jin

Zhang

Fan

et al. 2021

Mathematical Problems in Engineering

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“…Many of the proposals scale the infrastructure horizontally by adding new VMs, typically via predefined VM classes [22][23][24][25][26][27][28][29][30][31][32]. While this is simpler to apply, compared to fine grain CPU and memory configuration, it may lead to wastage by over-allocating resources to workloads as well consume more power.…”

Section: Vm Adaptation -Cpu and Memorymentioning

confidence: 99%

“…Their approach identifies and scales a bottlenecked tier in a multi-tier cloud application. The authors in [32] simplify the cloud application to a typical request queuing model and combine this with binary search.…”

Section: Adaptation Techniquementioning

confidence: 99%

“…Additionally, users typically overestimate resources reservations, leading to significant wastage [76]. Some existing approaches aim to predict future workloads using classical prediction models such as ARMA [28], a linear regression model [78] and a hybrid model tuned to bursty web traffic [32,79]. Other characterisation approaches aim to predict workload resource utilisation, by identifying a feature of the workload.…”

Section: Open Research Challengesmentioning

confidence: 99%

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Adaptation in cloud resource configuration: a survey

2016

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With increased demand for computing resources at a lower cost by end-users, cloud infrastructure providers need to find ways to protect their revenue. To achieve this, infrastructure providers aim to increase revenue and lower operational costs. A promising approach to addressing these challenges is to modify the assignment of resources to workloads. This can be used, for example, to consolidate existing workloads; the new capability can be used to serve new requests or alternatively unused resources may be turned off to reduce power consumption. The goal of this paper is to highlight features, approaches and findings in the literature, in order to identify open challenges and facilitate future developments. We present a definition of cloud systems adaptation, a classification of the key features and a survey of adapting compute and storage configuration. Based on our analysis, we identify three open research challenges: characterising the workload type, accurate online profiling of workloads, and building highly scalable adaptation mechanisms.

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“…At present, cloud computing technology has achieved good results in the fields of medical data storage, traffic data real-time analysis, and weather data analysis [ 37 , 63 ]. The cost of cloud computing is low, and there are no complicated requirements for the servers in the cluster establishment [ 64 ]. With the help of the large scale and fast calculation speed of cloud computing, the management and analysis of power monitoring data can be realized more effectively through integration with traditional data mining techniques [ 33 , 65 ].…”

Section: Introductionmentioning

confidence: 99%

Big Data Analytics Using Cloud Computing Based Frameworks for Power Management Systems: Status, Constraints, and Future Recommendations

AL-Jumaili

Muniyandi

Hasan

et al. 2023

Sensors

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Traditional parallel computing for power management systems has prime challenges such as execution time, computational complexity, and efficiency like process time and delays in power system condition monitoring, particularly consumer power consumption, weather data, and power generation for detecting and predicting data mining in the centralized parallel processing and diagnosis. Due to these constraints, data management has become a critical research consideration and bottleneck. To cope with these constraints, cloud computing-based methodologies have been introduced for managing data efficiently in power management systems. This paper reviews the concept of cloud computing architecture that can meet the multi-level real-time requirements to improve monitoring and performance which is designed for different application scenarios for power system monitoring. Then, cloud computing solutions are discussed under the background of big data, and emerging parallel programming models such as Hadoop, Spark, and Storm are briefly described to analyze the advancement, constraints, and innovations. The key performance metrics of cloud computing applications such as core data sampling, modeling, and analyzing the competitiveness of big data was modeled by applying related hypotheses. Finally, it introduces a new design concept with cloud computing and eventually some recommendations focusing on cloud computing infrastructure, and methods for managing real-time big data in the power management system that solve the data mining challenges.

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Optimization of virtual resource management for cloud applications to cope with traffic burst

Cited by 18 publications

References 7 publications

Computer Management Design and Optimization of City Smart Medical Laboratory Service

Computer Management Design and Optimization of City Smart Medical Laboratory Service

Adaptation in cloud resource configuration: a survey

Big Data Analytics Using Cloud Computing Based Frameworks for Power Management Systems: Status, Constraints, and Future Recommendations

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