An Adaptive Short-Term Prediction Algorithm for Resource Demands in Cloud Computing

Chen, Jing; Wang, Yinglong

doi:10.1109/access.2020.2981011

Cited by 7 publications

(4 citation statements)

References 52 publications

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“…Both edge and cloud participation in traffic prediction have been considered in [25]. This work analyzed the characteristics of resource demand and load and proposed an adaptive selection strategy and error adjustment factor to select a better prediction algorithm based on a dynamic threshold.…”

Section: Related Workmentioning

confidence: 99%

Resource Allocation With Edge-Cloud Collaborative Traffic Prediction in Integrated Radio and Optical Networks

et al. 2023

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By integrating communications in different domains, integrated radio and optical networks can serve a wider range of applications and services. Integrated radio and optical network scenarios will involve more weak-computation-ability network nodes, such as small-cell base stations. To pursue efficient integrated radio and optical networks, more efficient ways to conduct transmission under the demand of edge and cloud collaboration are required. The lack of forward-looking resource allocation may easily lead to a waste of network resources without an expected return. Therefore, an efficient resource allocation scheme needs to consider certain issues: 1) a comprehensive perspective of traffic prediction; 2) a release of pressure on the transmission pipeline during the prediction process; and 3) a reduction of loss of edge nodes due to the computation. In this paper, benefiting from machine learning, we propose a resource allocation with edgecloud collaborative traffic prediction (TP-ECC) in integrated radio and optical networks, where an efficient resource allocation scheme (ERAS) is designed based on the prediction results with the gated recurrent unit model. We maximize the utilization of limited resources to improve the awareness of network status. We present three evaluation indicators and build a network architecture to evaluate our resource allocation scheme. Through edge-cloud collaboration, our proposal can improve traffic prediction accuracy by 9.5% compared with single-point traffic prediction, and resource utilization is also improved by edge-cloud collaborative traffic prediction.INDEX TERMS Integrated radio and optical networks, resource allocation, edge-cloud collaboration, traffic prediction.

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Section: Related Workmentioning

confidence: 99%

Resource Allocation With Edge-Cloud Collaborative Traffic Prediction in Integrated Radio and Optical Networks

et al. 2023

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“…Some works have classified the hosts as overloaded and underloaded based on linear regression [11], multiple linear regression [9] and hybrid method based on ensemble empirical mode decomposition and AutoRegressive Integrated Moving Average (ARIMA) [7]. VM migration was performed from overloaded hosts to underloaded hosts [7,9,11]. Linear regression-based CPU Utilization Prediction (LiRCUP) has reduced SLA violation, as well as power consumption [11].…”

Section: Regression Models Used For Resource Predictionmentioning

confidence: 99%

Novel Approaches for Resource Management Across Edge Servers

Surya

Rajam²

2023

Int J Netw Distrib Comput

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Edge computing aims at reducing computation and storage across the cloud and provides service with reduced latency. Edge devices can be mobile devices, routers, cameras, printers or any Internet of Things (IoT) devices that generate vast amounts of data. The processing of these data is done by virtual machines (VMs) present in the edge servers that are located within close proximity of the edge devices. This work proposes two models which predict resource contention at the edge servers, namely, a Dynamic Markov model for Resource Contention Prediction in Edge Cloud (DMRCP) and a Hybrid Cascade of Regression and Markov model for Resource Contention Prediction (CRMRCP). In DMRCP, a history matrix is updated based on the CPU utilization of a Virtual Machine (VM). This history matrix is used to update a transition probability matrix. This matrix is used to predict the future state of the VM. In the CRMRCP approach, the past CPU utilization values of the virtual machines in the edge servers are used for predicting a set of future CPU utilization values using linear regression, polynomial regression, lasso regression and ridge regression. Then, the predicted future CPU utilization values are used by the dynamic and the second-order Markov models to classify the state of the edge servers as overloaded, underloaded or normally loaded. In both the approaches, the VMs that may cause resource contention are predicted and are migrated to other edge servers such that the destination edge server does not get overloaded after the migration. The DMRCP method is compared with the first-order and the second-order Markov models and the number of VM migrations is analysed to evaluate the performance. The number of VM migrations in the CRMRCP method is compared with that in the second-order Markov model. The overall results prove that the number of VM migrations for the DMRCP is 52.9% less compared to the first-order Markov model and 21.1% less when compared to the second-order Markov model. The number of VM migrations in CRMRCP is reduced by 81.8% when ridge regression cascaded with the second-order Markov model is used.

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“…For the convenience of illustrating how to obtain the QoS evaluation values of the configured tasks, the evaluation of execution time is calculated here as an example. When the nearest neighbors of the newly configured task are selected, the execution time wt j consumed by the nearest neighbor j is viewed, and the set of execution times of these nearest neighbor tasks is denoted by WT and the set of contribution rate θ j is denoted by Ω, then the execution time w tnew of the newly configured task is calculated by Equation (19), and the density-based performance evaluator is designed in the form of Equation (19). Ω, WT h idenotes the dot product of vectors Ω and WT.…”

Section: Evaluation Of Qos Performance Metricsmentioning

confidence: 99%

“…Regardless of the perspective or which way the resource prediction problem is studied, the methods used include the following algorithms: time series-based, traditional machine learning, and deep learning-based algorithms. [18][19][20][21] Time series-based prediction algorithms are mainly due to the results of Zhang et al and Ramezani and Naderpour that showed that the resource demand of a task is strongly correlated in the time dimension, 22,23 so many time series modeling techniques can be applied to the resource prediction problem, such as Liao et al for data with different load patterns, combined with auto-regressive moving average (ARMA) and other time series models to propose an adaptive load prediction model for LSRP. 24 Calheiros et al used the ARIMA model for realistic request tracking of servers.…”

mentioning

confidence: 99%

Task‐oriented resource prediction and adjustment algorithm for space‐based information network

Wang,

Liu,

Chen

et al. 2023

Satell Commun Network

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SummaryThe tasks of a space‐based information network are complex and diverse, but the resources of a space‐based environment are minimal. The existing methods are challenging to match task demand to resource supply accurately. Aiming at the problem of accurate prediction from task to resource, we propose a resource prediction adjustment strategy. First, we propose a multidimensional resource prediction algorithm based on improved particle swarm optimization and back propagation (IPSO‐BP) neural network. The improved PSO is used to optimize the weight and threshold of BP neural network to make up for the defects that BP neural network is easy to fall into local minimum and the predicted output value is not unique. Second, to meet the quality of service (QoS) of tasks, we propose a density‐based performance evaluation algorithm (DPEA) to adjust resources. This method uses the idea of local sensitive hash to select the evaluation subset for the configuration task, then dynamically selects the nearest neighbors of the configuration task, and uses the idea of weighted average to evaluate the QoS performance index of the configuration task. Simulation results show that the proposed resource prediction and adjustment strategy effectively reduces the scheduling time overhead and improves resource utilization.

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An Adaptive Short-Term Prediction Algorithm for Resource Demands in Cloud Computing

Cited by 7 publications

References 52 publications

Resource Allocation With Edge-Cloud Collaborative Traffic Prediction in Integrated Radio and Optical Networks

Resource Allocation With Edge-Cloud Collaborative Traffic Prediction in Integrated Radio and Optical Networks

Novel Approaches for Resource Management Across Edge Servers

Task‐oriented resource prediction and adjustment algorithm for space‐based information network

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