Distributed Systems In The Host Load Prediction And Dynamic Load Balancing

Dynamic load balancing is the crucial technology on Network Computing,and how to improve the performance of dynamic load balancing is the research emphasis of the people working on Network Computing.Traditional dynamic load balancing methods have always collected the real time values of the node load on the grounds of task distributed on each node,But,this method may produce decision-making delay,and cause task-migration vibration,and then greatly degrade the performance of balancing system.If we can accurately measure and predict node load,and incorporate dynamic load balancing strategy,we should availably resolve above problem, and markedly improve the performance of Network Computing.Based on this idea,we utilize multi-discipline knowledge including maths,statistics,and artificial intelligence to construct many kinds of prediction models,such as traditional time series models,homeostatic and tendency-based time-series models,neural networks BP model.We compare these prediction models and get its performance by testing evaluation, then we choose some benign prediction models includeing AR,MT,BP to build the prediction models template.We thus construct HLPS framework, and develop HLPS software packages in linux distributed environment, the software may provide real-time on line prediction on host load.Afterwards,we rest on HLPS to expound the theory on prediction on the running time of task,this method has some improvement in facility and operation compared with traditional methods that construct performance model for given applications.Ultimately,we effectively combine load prediction with dynamic load balancing to propose prediction-based dynamic load balancing method that get load information by prediction and adopt reformative receiver-initiated strategy. According to performance analysis and experiment evaluation,this method is superior,and improve the performance of dynamic load balancing systems.