Cloud Resource Integrated Prediction Model Based on Variational Modal Decomposition-Permutation Entropy and LSTM

Predicting the usage of container cloud resources has always been an important and challenging problem in improving the performance of cloud resource clusters.We proposed an integrated prediction method of stacking container cloud resources based on variational modal decomposition(VMD)-Permutation entropy(PE)and long short-term memory(LSTM)neural network to solve the prediction difficulties caused by the non-stationarity and volatility of resource data.The variational modal decomposition algorithm decomposes the time series data of cloud resources to obtain intrinsic mode function and residual components,which solves the signal decomposition algorithm’s end-effect and modal confusion problems.The permutation entropy is used to evaluate the complexity of the intrinsic mode function,and the reconstruction based on similar entropy and low complexity is used to reduce the difficulty of modeling.Finally,we use the LSTM and stacking fusion models to predict and superimpose;the stacking integration model integrates Gradient boosting regression(GBR),Kernel ridge regression(KRR),and Elastic net regression(ENet)as primary learners,and the secondary learner adopts the kernel ridge regression method with solid generalization ability.The Amazon public data set experiment shows that compared with Holt-winters,LSTM,and Neuralprophet models,we can see that the optimization range of multiple evaluation indicators is 0.338∼1.913,0.057∼0.940,0.000∼0.017 and 1.038∼8.481 in root means square error(RMSE),mean absolute error(MAE),mean absolute percentage error(MAPE)and variance(VAR),showing its stability and better prediction accuracy.

Aspects of Regional and Worldwide Mineral Resource Prediction

The purpose of this contribution is to highlight four topics of regional and worldwide mineral resource prediction:(1)use of the jackknife for bias elimination in regional mineral potential assessments;(2)estimating total amounts of metal from mineral potential maps;(3)fractal/multifractal modeling of mineral deposit density data in permissive areas;and(4)worldwide and large-areas metal size-frequency distribution modeling.The techniques described in this paper remain tentative because they have not been widely researched and applied in mineral potential studies.Although most of the content of this paper has previously been published,several perspectives for further research are suggested.

Resource Load Prediction of Internet of Vehicles Mobile Cloud Computing

Load-time series data in mobile cloud computing of Internet of Vehicles(IoV)usually have linear and nonlinear composite characteristics.In order to accurately describe the dynamic change trend of such loads,this study designs a load prediction method by using the resource scheduling model for mobile cloud computing of IoV.Firstly,a chaotic analysis algorithm is implemented to process the load-time series,while some learning samples of load prediction are constructed.Secondly,a support vector machine(SVM)is used to establish a load prediction model,and an improved artificial bee colony(IABC)function is designed to enhance the learning ability of the SVM.Finally,a CloudSim simulation platform is created to select the perminute CPU load history data in the mobile cloud computing system,which is composed of 50 vehicles as the data set;and a comparison experiment is conducted by using a grey model,a back propagation neural network,a radial basis function(RBF)neural network and a RBF kernel function of SVM.As shown in the experimental results,the prediction accuracy of the method proposed in this study is significantly higher than other models,with a significantly reduced real-time prediction error for resource loading in mobile cloud environments.Compared with single-prediction models,the prediction method proposed can build up multidimensional time series in capturing complex load time series,fit and describe the load change trends,approximate the load time variability more precisely,and deliver strong generalization ability to load prediction models for mobile cloud computing resources.

An Integrated Image Task Planning in Satellite Networks:From Instruction Release and Observation Perspective

The unreasonable observation arrangements in the satellite operation control center(SOCC)may result in the observation data cannot be downloaded as scheduled.Meanwhile,if the operation instructions released by the satellite telemetry tracking center(STTC)for the on-board payloads are not injected on the specific satellites in time,the corresponding satellites cannot perform the observation operations as planned.Therefore,there is an urgent need to design an integrated instruction release,and observation task planning(I-IRO-TP)scheme by efficiently collaborating the SOCC and STTC.Motivated by this fact,we design an interaction mechanism between the SOCC and the STTC,where we first formulate the I-IRO-TP problem as a constraint satisfaction problem aiming at maximizing the number of completed tasks.Furthermore,we propose an interactive imaging task planning algorithm based on the analysis of resource distribution in the STTC during the previous planning periods to preferentially select the observation arcs that not only satisfy the requirements in the observation resource allocation phase but also facilitate the arrangement of measurement and control instruction release.We conduct extensive simulations to demonstrate the effectiveness of the proposed algorithm in terms of the number of completed tasks.

Data-driven resource allocation with trac load prediction

Wireless big data is attracting extensive attention from operators,vendors and academia,which provides new freedoms in improving the performance from various levels of wireless networks.One possible way to leverage big data analysis is predictive resource allocation,which has been reported to increase spectrum and energy resource utilization eciency with the predicted user behavior including user mobility.However,few works address how the trac load prediction can be exploited to optimize the data-driven radio access.We show how to translate the predicted trac load into the essential information used for resource optimization by taking energy-saving transmission for non-real-time user as an example.By formulating and solving an energy minimizing resource allocation problem with future instantaneous bandwidth information,we not only provide a performance upper bound,but also reveal that only two key parameters are related to the future information.By exploiting the residual bandwidth probability derived from the trac volume prediction,the two parameters can be estimated accurately when the transmission delay allowed by the user is large,and the closed-form solution of global optimal resource allocation can be obtained when the delay approaches in nity.We provide a heuristic resource allocation policy to guarantee a target transmission completion probability when the delay is no-so-large.Simulation results validate our analysis,show remarkable energy-saving gain of the proposed predictive policy over non-predictive policies,and illustrate that the time granularity in predicting trac load should be identical to the delay allowed by the user.