Cloud computing has become buzzword today. It is a digital service where dynamically scalable and virtualized resources are provided as a service over internet. Task scheduling is premier research topic in cloud computing. It is always a challenging task to map variety of complex task on various available heterogenous resources in scalable and efficient way. The very objective of this paper is to dynamically optimize task scheduling at system level as well as user level. This paper relates benefit-fairness algorithm based on weighted-fair Queuing model which is much more efficient than simple priority queuing. In proposed algorithm, we have classified and grouped all tasks as deadline based and minimum cost based constraints and after dynamic optimization, priority of fairness is applied. Here different priority queue (high, mid, low) are implemented in round-robin fashion as per weights assign to them .We recompile the CloudSim and simulate the proposed algorithm and results of this algorithm is compared with sequential task scheduling and simple constraints (cost and deadline) based task scheduling algorithm. The experimental results indicates that proposed algorithm is, not only beneficial to user and service provider, but also provides better efficiency and fairness at priority level, i.e. benefit at system level.