Multi-user network application is one of the most popular forms of distributed computing.To fully exploit computing resources in distributed systems

task scheduling is critical.However

in scheduling of multi-user network application because lots of uncertainties exist such as task arrival

task completion time

etc.

the state of the art scheduling approaches fail in dynamic

real time

or adaptability.On account of the real time property

we put forward the concept of probabilistic scheduling to compress scheduling time

which regards task allocation as a probabilistic event.Unexpectedly

compared with traditional scheduling approaches which are always determinate

probabilistic scheduling has other advantages like insensitivity to task execution time estimation and steady performance.Based on the idea of probabilistic scheduling and considering the shortest response time from the perspective of users

a queueing model is given for multi-user network application

and scheduling is defined as a non-linear programming problem.But due to its limitation on task arrival process and service rate

a self-adaptive algorithm is proposed by use of reinforcement learning theory.The scheduling problem is described by Markov Decision Process (MDP)

and then task arrival process and service rate can be learned on line.Once the allocation probability is acquired

scheduling is quite fast by following such probability distribution.The two algorithms are validated and they outperform such four classic algorithms as Min-Min

Max-Min

Suffrage

and ECT at the average response time.Except for the mean of response time

their variance is also examined to confirm the stability generated by probabilistic scheduling.