Before beginning with the C program implementation, let us first understand the conceptual theory of the Round Robin Scheduling Algorithm. The Round robin algorithm is a pre-emptive process scheduling algorithm. Here, every job request in the queue is associated with a fixed execution time called quantum. C program for solving round robin (with arrival time). This queue, allocating the CPU to each process for a time interval of one quantum.

1. Round Robin Scheduling Program In C Language Without Arrival Time
2. Round Robin Scheduling Program In C Language

A Round Robin preemptive scheduling example with quantum=3.Round-robin (RR) is one of the algorithms employed by and in.As the term is generally used, (also known as time quanta) are assigned to each process in equal portions and in circular order, handling all processes without (also known as ). Round-robin scheduling is simple, easy to implement, and -free. Round-robin scheduling can also be applied to other scheduling problems, such as data packet scheduling in computer networks. It is an concept.The name of the algorithm comes from the principle known from other fields, where each person takes an equal share of something in turn. Main article:To schedule processes fairly, a round-robin scheduler generally employs, giving each job a time slot or quantum (its allowance of CPU time), and interrupting the job if it is not completed by then.

The job is resumed next time a time slot is assigned to that process. If the process terminates or changes its state to waiting during its attributed time quantum, the scheduler selects the first process in the ready queue to execute. In the absence of time-sharing, or if the quanta were large relative to the sizes of the jobs, a process that produced large jobs would be favoured over other processes.Round-robin algorithm is a pre-emptive algorithm as the scheduler forces the process out of the CPU once the time quota expires.For example, if the time slot is 100 milliseconds, and job1 takes a total time of 250 ms to complete, the round-robin scheduler will suspend the job after 100 ms and give other jobs their time on the CPU. Once the other jobs have had their equal share (100 ms each), job1 will get another allocation of time and the cycle will repeat.

This process continues until the job finishes and needs no more time on the CPU. Job1 = Total time to complete 250 ms (quantum 100 ms).

First allocation = 100 ms. Second allocation = 100 ms.

Third allocation = 100 ms but job1 self-terminates after 50 ms. Total CPU time of job1 = 250 msConsider the following table with the arrival time and execute time of the process with the quantum time of 100ms to understand the round-robin scheduling:Process nameArrival timeExecute timeP70P21P535050. Main article:In and other, round-robin scheduling can be used as an alternative to queuing.A multiplexer, switch, or router that provides round-robin scheduling has a separate queue for every data flow, where a data flow may be identified by its source and destination address. The algorithm allows every active data flow that has data packets in the queue to take turns in transferring packets on a shared channel in a periodically repeated order. The scheduling is, meaning that if one flow is out of packets, the next data flow will take its place. Hence, the scheduling tries to prevent link resources from going unused.Round-robin scheduling results in if the data packets are equally sized, since the data flow that has waited the longest time is given scheduling priority. It may not be desirable if the size of the data packets varies widely from one job to another.

A user that produces large packets would be favored over other users.

Round-robin scheduling ( Figure 7.151) allocates each task an equal share of the CPU time. In its simplest form, tasks are in a circular queue and when a task's allocated CPU time expires, the task is put to the end of the queue and the new task is taken from the front of the queue. Round-robin scheduling is not very satisfactory in many real-time applications where each task can have varying amounts of CPU requirements depending upon the complexity of processing required. One variation of the pure round-robin scheduling is to provide priority-based scheduling, where tasks with the same priority levels receive equal amounts of CPU time. It is also possible to allocate different maximum CPU times to each task. An example project is given later on the use of round-robin scheduling.

Tim Wilmshurst, in, 2010 18.4.2 Round-robin scheduling and context switchingIn round-robin scheduling the operating system is driven by a regular interrupt (the ‘clock tick’). Tasks are selected in a fixed sequence for execution. On each clock tick, the current task is discontinued and the next is allowed to start execution.

All tasks are treated as being of equal importance and wait in turn for their slot of CPU time. Tasks are not allowed to run to completion, but are ‘pre-empted’, i.e. Their execution is discontinued mid-flight. This is an example of a ‘pre-emptive’ scheduler.The implications of this pre-emptive task switching, and its overheads, are not insignificant and must be taken into account. When the task is allowed to run again, it must be able to pick up operation seamlessly, with no side-effect from the pre-emption. Therefore, complete context saving (all flags, registers and other memory locations) must be undertaken as the task switches. Time-critical program elements should not be interrupted, however, and this requirement will need to be written into the program.

A diagrammatic example of round-robin scheduling is shown in Figure 18.6. The numbered blocks once more represent the tasks as they execute, but there is a major difference from Figure 18.5.

Now each task gets a slot of CPU time, which has a fixed length. The clock tick, which causes this task switch, is represented in the diagram by an arrow. When that time is up, the next task takes over, whether the current one has completed or not. At one stage Task 2 completes and does not need CPU time for several time slices. It then becomes ready for action again and takes its turn in the cycle.

Dimitrios Serpanos, Tilman Wolf, in, 2011 Deficit round-robin schedulingThe shortcoming of systematic unfairness due to large packets in round-robin scheduling is addressed in deficit round-robin scheduling. This scheduling algorithm was first described in Shreedhar and Varghese 166. The main idea is to track the amount of bandwidth consumed by each queue. The waiting time enforced between the transmissions of packets increases with larger packets.In deficit round-robin (DRR) scheduling, a “deficit counter” is associated with each queue. This counter tracks the credit that each queue has with the link scheduler. Credit is accumulated when a packet is delayed in the queue, and credit is used when a packet is transmitted. The round-robin operation is as follows: Every time the scheduler visits a queue, the deficit counter for this queue is increased by a fixed amount (i.e., “quantum size”).

Then, the queue can use the credit to transmit packets. A packet can be transmitted if the accumulated credit is equal to or larger than the packet size. Once the packet is transmitted, the credit of the queue is reduced by the size of the packet. While the remaining credit is sufficient to send the next packet in the queue, the process continues.

(Note that the credit is incremented by the quantum size only once per round, but multiple packet transmissions may be performed.) The process of DRR scheduling is shown in Figure 10-7. There are two scheduling examples using quantum sizes of 100 and 60.The quantum size determines how fine-grained the scheduler operates.

Larger quantum sizes increase the (temporary) unfairness between connections. Smaller quantum sizes provide better fairness, but also increase the computational requirements for the link scheduler as more rounds need to be computed before a packet can be transmitted. Also, note that inactive queues (i.e., those without packets) cannot accumulate credit. When using weights, the amount of credit allocated to each queue can simply be scaled by the queue's weight.

Larger weights lead to larger amounts of credit, which allow more use of the link. The SM in Fermi-style architecture is armed with two warp schedulers. Warps with odd and even IDs are dispatched into those two schedulers, respectively. At every cycle, two instruction warps are issued based on the round-robin scheduling policy, and they are likely to have identical PC since all threads in a kernel execute the same code. Mapping the same-ID registers from different warps into the same bank seriously exacerbates the bank conflicts, because different entries within a bank may be requested by the two simultaneously issued instruction warps. Therefore the register-to-bank mapping mechanism follows Eq.

To ensure that different banks hold the same-ID registers across the warps. ( 2) shows, consecutive warps tend to map their same-ID registers into nearby RF banks. For instance, R1 from warp0 and warp1 are mapped to bank 1 and bank 2, respectively. Generally, consecutive warps exhibit strong data locality 35, so their same-ID registers should be allocated to RF banks with the same speed type to ensure that they execute at a similar progress. We propose to save IDs of same-type subbanks into consecutive entries in the ROM at the chip test time; therefore bank0–bank10 are fast and bank11–bank15 are slow in the bank organization table under VL-SB and RF-BRO techniques. ( 2), there are a number of registers per warp mapping to the slow RF banks.

And the slow bank keeps registers with different IDs at the warp level. 23 demonstrates an example of register mapping: R11–R15 from warp0 while R0–R4 from warp11 are assigned to the slow RF bank11–bank15. Activity P5 Using the Operating Systems Workbench to Explore Scheduling Behavior (Introductory): Comparing First-Come First-Served, Shortest Job First, and Round-Robin Scheduling PrerequisiteDownload the Operating Systems Workbench and the supporting documentation from the book's supplementary materials website. Expected OutcomeThe simulation will run through the sequence of state changes for each process, until all processes have completed. You will see the run-time statistics and system statistics updating in real time as the simulation runs.

These statistics enable analysis of the low-level behavior of the algorithms and their effects on the processes present. Activity P5balancedExploring first-come first-served, shortest job first, and round-robin scheduling with “balanced” processes (these are processes that perform moderate amounts of input and output but are also moderately CPU-intensive in between the IO activities) MethodThe same as for Activity P5CPU above, except set both processes to be “balanced.” Expected OutcomeYou will see that processes get blocked when they perform IO and thus accrue time in the blocked state.

You should notice a significant difference between behaviors of the three algorithms when a process becomes blocked. Activity P5IOExploring first-come first-served, shortest job first, and round-robin scheduling with IO-intensive processes (these are processes that perform IO regularly) MethodThe same as for Activity P5CPU above, except set both processes to be “IO-intensive.” Expected OutcomeYou will see that the processes frequently perform IO and get blocked regularly and thus accrue a significant proportion of their time in the system in the blocked state.

You should notice that the efficiency of the system overall is impacted if all processes are performing IO frequently, as a temporary situation can arise when there are no processes in the ready state, and thus, none can run. ReflectionYou have examined the behavior of three scheduling algorithms with a variety of differently behaved processes. Based on the run-time statistics you have collected, how do these scheduling algorithms compare to each other? Here are some specific questions to start your investigation: 1.What can be said about the fairness of the algorithms (hint: consider the waiting times for processes)? 2.What can be said about the efficiency of CPU usage (hint: consider the total time in the system for each process and the total system elapsed time)? 3.To what extent does the type of process behavior impact on the effectiveness of the scheduling algorithms? Is there always a clear winner, or do the algorithms have different strengths depending on the types of processes in the system?

Further ExplorationYou can perform further experiments with the “Scheduling Algorithms—Introductory” simulation to help gain a deeper understanding. You can use the single step button to run the simulations one step at a time allowing you to work at your own pace.The three scheduling algorithms discussed up to this point are quite limited in the context of modern general-purpose systems with a very wide variety of types of applications with very different behaviors. These scheduling algorithms lack the sophistication necessary to select processes based on contextual factors relating to dynamics of the system itself and the processes within. Some more-advanced algorithms take into account process priorities, or deadlines, or expected run time, or accumulated run time, or various other factors to improve the performance of individual processes and of the system overall. Round-robin scheduling is however ideally suited for systems in which a number of similar processes have equal importance, and as a result of its simple turn-based approach, it has the advantage of being “starvation-free,” which means that one process cannot hog the CPU at the expense of others, which can occur for extended periods, possibly indefinitely in some other schemes. Fortier, Howard E.

Michel, in, 2003 4.1 Scheduling algorithmsWhen analyzing computer systems one ultimately must look at the scheduling algorithms applied to resource allocation. The means by which resources are allocated and then consumed are of utmost importance in assessing the performance of a computer system. For example, scheduling algorithms are applied when selecting which program runs on a CPU, what I/O device is serviced, and when or how a specific device handles multiple requests.

When examining scheduling algorithms, two concepts must be addressed. The first is the major job of the scheduling algorithm, which is what job to select to run next. The second is to determine if the job presently running is the most appropriate to run and if not, should it be preempted (removed from service).The most basic form of scheduling algorithm is first-come first-served (FCFS). In this scheduling algorithm jobs enter the system and get operated on based on their arrival time. The job with the earliest arrival time gets served next.

This algorithm does not apply preemption to a running job, since the running job would still hold the criterion of having the earliest arrival time. A scheduling algorithm that operates opposite from the FCFS is the last-come first-served (LCFS) algorithm. In this algorithm the job with the most recent time tag is selected for operation. Given this algorithm's selection criteria, it is possible that this algorithm could be preemptive. The job being serviced is no longer the last to come in for service.

The preemption decision must be made based on the resource's ability to be halted in midstream and then restarted at some future time. Processors typically can be preempted, since there are facilities to save registers and other information needed to restart a job at some later time.

Other devices, such as a disk drive or I/O channel, may not have the ability to halt a job and pick it up at some later point.A number and variety of scheduling algorithms are associated with processor scheduling. One of the most common processor scheduling algorithms is round robin. Round-robin scheduling is a combination algorithm.

It uses FCFS scheduling, along with preemption. The processor's service is broken into chunks of time called quantum. These quanta or time slices are then used as the measure for service. Jobs get scheduled in an FCFS fashion as long as their required service time does not exceed the time of a quantum. If their required service time exceeds this, the job is preempted and placed in the back of the set of pending jobs.

This motion of placing a job back into the FCFS scheduling pipe continues until the job ultimately completes. Thus, the job's service time is broken up into some number of equal, fixed-size time slices. The major issue with round-robin scheduling is the selection of quantum size.

The reason quantum size selection is so important is due to the nature of preemption. Preempting a job requires overhead from the operating system to stop the job, save its state, and install a new job. If the time for these tasks is large in comparison to the quantum size, then performance will suffer. Many different rules of thumb have been developed in designing such systems.

Most look to make the overhead a very small fraction of the size of the quantum—typically, orders of magnitude smaller. A method used for approximating round-robin scheduling when the quantum is very large compared with the overhead is processor sharing (PS). This model of round-robin scheduling is used in theoretical analysis, as we will see in later chapters.Another algorithm is shortest remaining time first (SRTF). In this algorithm the job that requires the least amount of resource time is selected as the next job to service.

The CPU scheduling algorithm SRTF does support preemption. When an arriving job is found to have a smaller estimated execution time than the presently running job, the running job is preempted and replaced by the new job. The problem with this scheduling algorithm is that one must know the processing requirements of each job ahead of time, which is typically not the case. Due to this limitation it is not often used. The algorithm is, however, optimal and used as a comparison with other more practical algorithms.A useful algorithm related to SRTF is the value-driven algorithm, where both the time of execution and the value of getting the job completed within some time frame are known ahead of time. This class of algorithm is found in real-time, deadline-driven systems.

The algorithm selects the next job to do based on nearness to its deadline and the computation of the value it returns if done now. The algorithm also is preemptive in that it will remove an executing job from the processor if the contending job is nearer its deadline and has a higher relative value.

The interest in these classes of scheduling algorithms is that they deliver support for the most critical operations at a cost to overall throughput. 4.1.1 Relationship between scheduling and distributionsIn determining the performance of a computer system, the usual measure is throughput. In the discussions that follow we consider this to be the mean number of jobs passing through some point of interest in our architecture during an interval of time—for example, the number of jobs leaving the CPU per minute. In most cases we will realize the maximum value for throughput when our resources are fully utilized (busy).In the previous section we introduced measures that we can use now. The coefficient of variation defined previously is a good way to examine the variability of our data. If the service times are highly variable, C 1, then most measures will be smaller than the mean and some will be larger. For example, in the exponential distribution, C = 1, one would find from the probability density function that about 63 percent of the values are below the mean.

Such variability would cause problems with certain scheduling algorithms—for example, the FCFS scheduling algorithm, since jobs with large resource requirements will cause added delays to the majority of jobs that will be smaller than the mean. The effect can be further compounded by other resources dependent on the FCFS scheduled resource.

For example, if jobs pile up waiting for CPU service, other resources such as disk drives and I/O devices would go idle.One scheduling algorithm that is not as susceptible to this phenomenon is the round-robin scheduling protocol. Since no job, whether large or small, can acquire and hold the resource longer than a single quantum at a time, larger jobs will not starve out smaller jobs.

This fact makes the round-robin scheduling protocol a nice algorithm for measuring resource utilization with variable loads. If we were to compare the FCFS and round-robin scheduling protocols with each other for highly variable and highly correlated loads, we would see that as the loads became more correlated the algorithms performed in a more similar manner. On the other hand, as the data become more variable the round-robin scheduling protocol performs better than the FCFS. 4.1.2 Relationship to computer systems performance.

James Gemmell. Rowe, in, 2002 Managing buffers for maximum consumptionNaturally, a server must manage its buffers to leave sufficient free space for the next reads to be performed. On a per-stream basis, the most suitable buffer model is a firstin, first-out queue. Using a FIFO, contiguous files, and round-robin scheduling, the buffer size can approximate the size of the maximum required read. In this case, each stream's FIFO can simply be “topped up” in each round (that is, enough data is read to fill the FIFO).

In contrast, a Scan strategy would require at least a double-buffered scheme, where each buffer is the size of a maximum read. This is because Scan can schedule the reads for a stream so that the stream is serviced last in one round and first in the next (back to back). If a buffer is not completely empty when it is time for reading, the topping-up strategy can still be used.With a topping-up strategy, the amount read for a stream in each reading period will vary with the amount of free buffer space. When files are not stored contiguously but are split into blocks, variable read amounts might mean that the data to be retrieved is split across two blocks, causing an extra intrafile seek. One solution to this problem uses three block-sized buffers.

With three buffers, the only time a whole block cannot be read is when at least two buffers are full; buffering is otherwise sufficient so that reading is not necessary until the next round. Other solutions with fewer than three buffers are also possible.

Daniel Aarno, Jakob Engblom, in, 2015 Multiprocessor Simulation Speed MeasurementWhen simulating multiple processors, simulated time per host second (RTF) works just fine. Target time will progress at some speed, and simulating a larger system typically just makes this move slower.

Round Robin Scheduling Program In C Language Without Arrival Time

However, simulated instructions per host second (IPS) becomes a bit trickier because it can be considered both for each processor individually or for the simulation as a whole.Starting with an ISS that progresses at N IPS for a single target processor and running P such processors in the same simulation using round-robin scheduling means each ISS will run for 1/ P of each real-world second. Thus, the progress of each individual ISS will be N/ P IPS. However, the progress of the simulation as a whole will be P( N/ P), which is N. Thus, it is fair to call this an N IPS simulator regardless of the number of processors simulated. For clarity, the overall IPS might be called aggregate IPS, indicating the sum of progress on all processors in one host second.A domain-specific measurement like frames per second or packets per second might or might not reflect the effect of the round-robin simulation; it depends on the behavior and load balance of the software being run on the simulator. Slowdown tends to go up as more processors are simulated using a single host processor.When using multiple host cores to simulate multiple target processors or processor cores, there are essentially multiple ISSs running in parallel.

Round Robin Scheduling Program In C Language

Thus, if there are H host cores being used to run N IPS ISSs, the aggregate IPS is going to be H N. To measure the fundamental technology, the N IPS of the basic ISS still seems the most appropriate.

Note that the N H number assumes linear scaling as the number of host cores increases, which is only reasonable if the target software parallelizes perfectly and there is insignificant communication needed between the simulated cores.