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PyTorch Code for Simple Neural Networks for MNIST Dataset

IRIS Tasks (Increased Reward with Increased Service)

IRIS means Increased Reward with Increased Service,

If a task runs to its completion, it get full reward. if not run to its completion then it gets zero reward. 

So, Each IRIS tasks has two portions a Mandatory part and an Optional Part. 

If a tasks completes atleast its mandatory part, then it gets a little reward, but if a tasks does not complete even the mandatory part, then it will get zero reward from it.

so the reward function is given below, mathematically,

Where Ri(x) is the reward function, oi being the optional part and mi being the mandatory part.... 

Each task has Mi={M0.....Mn} Mandatory portions and Oi={O0...On} Optional portions
so, the execution time of the task is the sum of mandatory portion and optional portion.
ei=mi+oi

The schedule is like this,
Step1; First try to schedule the task set using EDF as a whole with execution time, deadline and release time, if the task set is schedulable, the that is the feasible schedule.STOP. If not feasible, go to Step2;
Step2: Schdeule the Mandatory portion of the Task set for feasiblity using EDF. If that is also not feasible, then  that task set does not have any solution. STOP. if it is feasible go to step3:
Step3: By adjusting the mandatory task schedule and whole task schedule, we will make as scheduled...

the next post is an Example of IRIS Type1.
Source: Real Time Systems by Krishna and Shin

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