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In this work, we study the traffic adjustment over multipath network in the presence of both inelastic and elastic traffic flows. The characteristics of these two types of traffic differ significantly. Hence, earlier approaches that focus on homogeneous scenarios with a single traffic type are not directly applicable. We formulate a new traffic adjustment problem based on time series prediction and...
The major issue related to the realization of multipath network is the design of suitable dynamic adjustment algorithm of traffic allocation. The design challenge is to guarantee the various qualities of service requirements for all traffic, special the loss rate while minimizing the cost function. In this paper a novel traffic allocation algorithm based on time series prediction and PSO (particle...
In the Internet today, multipath routing is proved a popular technique that improves reliability and robustness of data delivery; however, current forwarding algorithm over multipath network have problems such as lacking of preparing for sudden spikes. This paper proposes a new forwarding algorithm, which is based on time series prediction and PSO (particle swarm optimization). We describe how the...
This paper proposes a new load balancing mechanism, called PPLR (prediction packet loss rate), which is based on time series prediction. Load balancing is needed in many fields; however, current approaches have problems such as lacking of preparing for sudden spikes. In this paper, we describe how PPLR dynamically predicts loss levels over all available network paths and adjust the allocation proportion...
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