This article is designed to solve the problem of system load imbalance and low efficiency introduced by massive parallel tasks running on some heavy nodes in the Cloud Computing environment. This study proposes a load balancing algorithm based on virtual machine migration which includes load balancing architecture, load gathering, load monitoring, load forecasting, migration trigger time, sourc...

We study the autocorrelation structure and the spectral density function of aggregates from a discrete-time process. The underlying discrete-time process is assumed to be a stationary AutoRegressive Fractionally Integrated Moving-Average (ARFIMA) process, after suitable number of differencing if necessary. We derive closed-form expressions for the limiting autocorrelation function and the norma...

ARFIMA is a time series forecasting model, which is an improve d ARMA model, the ARFIMA model proposed in this article is d emonstrated and deduced in detail. combined with network traffi c of CERNET backbone and the ARFIMA model,the result sho ws that,compare to the ARMA model, the prediction efficiency a nd accuracy has increased significantly, and not susceptible to sa mpling.

Intelligent Transportation Systems (ITS) is an emerging concept which has been utilised to improve efficiency and sustainability of existing transportation systems. Short term traffic flow forecasting, the process of predicting future traffic conditions based on historical and realtime observations is an essential aspect of ITS. The existing well-known algorithms used for short-term traffic for...

computational intelligence approaches have gradually established themselves as a popular tool for forecasting the complicated financial markets. forecasting accuracy is one of the most important features of forecasting models; hence, never has research directed at improving upon the effectiveness of time series models stopped. nowadays, despite the numerous time series forecasting models propos...

abstract forecasting electrical energy demand and consumption is one of the important decision-making tools in distributing companies for making contracts scheduling and purchasing electrical energy. this paper studies load consumption modeling in hamedan city province distribution network by applying esn neural network. weather forecasting data such as minimum day temperature, average day temp...

improving time series forecastingaccuracy is an important yet often difficult task.both theoretical and empirical findings haveindicated that integration of several models is an effectiveway to improve predictive performance, especiallywhen the models in combination are quite different. in this paper,a model of the hybrid artificial neural networks andfuzzy model is proposed for time series for...