Opposition grasshopper optimizer based multimedia data distribution using user evaluation strategy

doi:10.1007/s11042-021-11123-4

2021-07-15

PRE

Vinu Sundararaj *

M. Selvi

DOI：10.1007/s11042-021-11123-4

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摘要

En 中文

Multimedia services are offered to the demanding user by the multimedia cloud. By the fact, the sudden increase of network users has reduced the service receiving response time. Hence, it is impossible to achieve service satisfaction. Therefore, user experience based characteristic is a main role in the multimedia content acquisition, such as server distribution imbalance, huge user visits and limited bandwidth. In order to withstand these problems, a new multimedia cloud content distribution method is proposed based upon the integrated user utility and interest discovery. Initially, the interest features of users are extracted through applying an important feature extraction method. Subsequently, the separation of service and non-service users are formed through the development of a group depending on the categorization of same service interest and adjacent region included users. Followed with this, the integrated utility value is adopted to introduce user evaluation strategies. The integrated utility values are computed combining different user experience characteristics such as, user reputation, user selfish behaviours and user physical performance. However, the service user number evaluated by employing the Opposition Grasshopper Optimizer (OGHA) has minimized the content distribution time and user cost. Furthermore, the convergence profile and computational speed of standard GHA is enhanced by introducing the notion of opposition based population initialization in the proposed approach. Simulation outcomes have evidently proved the improvement of multimedia cloud users, minimizing the total cost of multimedia cloud users, and improvement of multimedia content utilization.

Keyword:

User experience

Content distribution

Integrated utility value

Opposition grasshopper optimizer

Multimedia content

Interest discovery