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<title>Job Scheduling Algorithms</title>
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	<div class="icon-bar">
		<a class="active" href="mainpage1.html"><i class="fa fa-home"></i></a>
		<a href="homepage2.html">Create Charts</a>
		<a href="jobschedulingpage.html">Job Scheduling Algorithms</a>
		<a href="pagereplacementpage.html">Page Replacement Algorithms</a>
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	  <a href="jobschedulingpage.html">First Come First Serve</a>
	  <a href="SJF.html">Shortest Job First</a>
	  <a href="SRTN.html">Shortest Remaining Time Next</a>
	  <a href="RR.html">Round Robin Scheduling</a>
	  <a href="PS.html">Priority Scheduling</a>
	  <a href="SPN.html">Shortest Process Next</a>
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		<br>
		<h4>Shortest Process Next</h4>
		Shortest job first always produces the minimum average response time for batch systems, it
		would be nice if it could be used for interactive processes as well. To a certain extent, it can be.
		Interactive processes generally follow the pattern of wait for command, execute command, wait for
		command, execute command, and so on. If we regard the execution of each command as a separate “job,”
		then we could minimize overall response time by running the shortest one first. The only problem is
		figuring out which of the currently runnable processes is the shortest one.
		One approach is to make estimates based on past behavior and run the process with the shortest estimated
		running time. Suppose that the estimated time per command for some terminal is T0. Now suppose its next
		run is measured to be T1. We could update our estimate by taking a weighted sum of these two numbers,
		that is, aT0 + (1 − a)T1. Through the choice of a we can decide to have the estimation process forget old
		runs quickly, or remember them for a long time. With a = 1/2, we get successive estimates of
		T0, T0/2 + T1/2, T0/4 + T1/4 + T2/2, T0/8 + T1/8 + T2/4 + T3/2
		After three new runs, the weight of T0 in the new estimate has dropped to 1/8.
		The technique of estimating the next value in a series by taking the weighted average of the current
		measured value and the previous estimate is sometimes called aging. It is applicable to many situations
		where a prediction must be made based on previous values. Aging is especially easy to implement when
		a = 1/2. All that is needed is to add the new value to the current estimate and divide the sum by 2 (by
		shifting it right 1 bit).
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