In a computer, when the user is using an application or doing something that is very complicated in the computer, the users typically have to wait for a short amount of time. This is called the Queuing Systems when in certain tasks that the user tells what the computer wants them to do, they have to use the servers such as the CPU during the queuing time to do what the computer is commanded to do. In essence, the waiting time is what makes the Queuing Systems. The Queuing Systems in general definition is what we experience every day such as waiting to pay for your groceries, waiting for the subway to arrive, and etcetera. No one wants to wait at all on a personal level but there are compromises to cut the waiting time in certain instances like the supermarket such as having multiple cash registers. But if there is a lot of people in the supermarket, then there would be problems such as due to congestion, the waiting times will be longer. In general belief in the computer world, this would be true but in a certain server that results in the opposite effect. In addition to the myth of congestion in certain servers involving the Queuing system, in general belief, if something in the computer was to be corrupted or just doesn’t work, that the server in the queuing system will totally suffer. This isn’t nearly the case either according to one article. In addition, there are many types of Queues. In the Queuing Systems, though it has real-life applications, the effects aren’t what people are expecting it to be.
In the Queuing Systems, there are a lot of similarities that was previously discussed the lines in the Supermarket in the real world. In addition to that, there is a lot of different queues, but the main important one’s includes as the priority queue and the first-in, first-out (FIFO). The priority queue is when there is a variable that is somehow deemed in the highest priority queue such as being the most important in the queuing system in that particular situation, the item that was dequeued in the process will be the one that is marked that has been selected with the highest priority. This can be very relatable such as the express line in a Supermarket or any wholesale stores such as Costco, BJ’s, and Sam’s Club. There is a set of registers at the checkout part of the store such as a supermarket, or a wholesale store. They have some sort of express line where if you have a few items such as 5 items, for example, there are a few of the set of registers that accommodate the customers who want to purchase that few amount of items. This is in essence, how the first-in, first-out queue works as it takes the queues with the most priority first, then other types of queues that have less priority are taken care of later on. The first-in, first-out queue is when the first, and usually the first queue gets processed first, and the other consecutive ones are processed after the first queue is processed. This can be related in the real world by the lines in a Supermarket such as in a regular line where the customer will have his/her items, and wait for the customers who are in the front of the line that is near or at the register to purchase their items. First-in-first-out is the most common queue that is the most common subject regarding computers, and it is very intricate in terms of how complex the system actually is and that the queues are actually much related in the outside world. For example as waiting in a line to purchase items such as groceries, or electronics, the computers today than in the past has been the staple of how the business of today operates.
In the article in South Korea called, Server Unavailability Reduces Mean Waiting Time in Some Bach Service Queuing Systems by H.W. Lee, D.I. Chung, and K.C. Chae, they did an analysis of the effect of congestion of the Batch Server, and the waiting time. They found out in the end of their study that in the case of a Batch Server, that the more delayed the Batch Server was in terms of starting up/ booting up, the number of queues was reduced. The reason being so was that certain queuing there’s were actually taking effect. For example, the authors of the article stated that “If the mean queue size decreases, the mean waiting time also decreases from the Little’s law.” (Lee et al.). What the authors are suggesting is that because of Little’s law that is how long one person stays in an area, along with other variables of a customer and the times associated in the store, etc. the amount of people in the store or in this case a Batch Server, is reduced. In addition, another variable that the authors of the article had pointed out they stated that they would leave the server in order to do other things that the users in the servers wanted/needed to do. In this instance, they stated that the, “‘forced idle time’ may be used for taking an intermittence or attending to another task” (Lee et al.). This article used mathematical equations of some sort to support the evidence that the Batch Server when it is congested that the wait time will be long. This dispels the general belief that in the Queuing Systems when it is congested in the number of queues inside the server that it will always be slow, but that isn’t the case at all what so ever in certain instances such as the Batch Server. Queuing Systems though with its real-life applications, it has a lot of factors that need to take place in terms of internal operations of the computer, what kind of server is used, etc. When there is an issue with an application or some sort of system in the computer, there are usually a lot of harsh consequences, for example, the program will crash and has to be reset or that the application that has an issue
will have its performance drop very dramatically. In the Queuing Systems, this situation doesn’t come with either of the two outcomes.
In the article named, Fault-Tolerant Two-Stage Open Queuing Systems With Server Failures at Both Stages by Enver Ever, it has an unusual effect. In the article by Enver Ever, he stated that “Two stage open queuing systems where both stages at fault tolerant are not considered in existing studies”. (“Fault-Tolerant Two-Stage Open Queuing Systems With Server Failures at Both Stages”). Because of no study according to the author, he decided to study how fault-tolerant the two open servers are in the Queuing systems. He would use a mathematical function like properties to calculate the jobs that are inside the two faulted servers that there is. He also exponentially increased the faultiness of the servers by using overloading its queuing capacities. The result by using a lot of methods such as the numerical system in order to approximate the number of jobs that the servers can handle, and so on and so forth. In conclusion towards the study, Enver Ever stated that “The discrepancies between the results obtained by the new solution approach and the stimulation are less than 5%” (“Fault-Tolerant Two-Stage Open Queuing Systems With Server Failures at Both Stages”). This explains how efficient the servers are in the Queuing systems despite with its troubles such as having the queuing system overflowing.
The Queuing Systems is a very complex topic that has a lot of myths involved. Such as in general that any kind of queuing overload will always slow down the system, or that if there is an adverse effect in the Queuing System, that the performance will drop or worse than the application will be crashed. In this case, that has not been the case, and the Queuing System and its inner workings are discovered, but not want people would originally think.
Lee, H. w., et al. Server Unavailability Reduces Mean Waiting Time In Some Batch Service Queuing Systems. Pergamon, May 1996, ac-els-cdn-com.citytech.ezproxy.cuny.edu/S0305054896000366/1-s2.0-S0305054896000366-main.pdf?_tid=a4df4b64-d391-11e7-954b-00000aab0f6c&acdnat=1511800995_fde490cb7b1ced3377d2db67bea33c27. Accessed 11/26/17
Ever, Enver. “Fault-Tolerant Two-Stage Open Queuing Systems With Server Failures at Both Stages.” IEE Explorer, 8 Sept. 2014, ieeexplore.ieee.org.citytech.ezproxy.cuny.edu/xpls/icp.jsp?arnumber=6850000. Accessed 11/26/17